Brown, J.W., Bullock, D., and Grossberg, S. (2003). How laminar frontal cortex and basal ganglia circuits interact to control planned and reactive saccades. Neural Networks, in press.

How does the brain learn to balance between reactive and planned behaviors? The basal ganglia and frontal cortex together allow animals to learn planned behaviors that acquire rewards when prepotent reactive behaviors are insufficient. This paper proposes a new model, called TELOS, to explain how laminar circuitry of the frontal cortex, exemplified by the frontal eye fields, interacts with the basal ganglia, thalamus, superior colliculus, and inferotemporal and parietal cortices to learn and perform reactive and planned eye movements. The model is formulated as fourteen computational hypotheses. These specify how strategy priming and action planning (in cortical layers III, Va and VI) are dissociated from movement execution (in layer Vb), how the basal ganglia help to choose among and gate competing plans, and how a visual stimulus may serve either as a movement target or as a discriminative cue to move elsewhere. The direct, indirect and hyperdirect pathways through the basal ganglia are shown to enable complex gating functions, including deferred execution of selected plans, and switching among alternative sensory-motor mappings. Notably, the model can learn and gate the use of a What-to-Where transformation that enables spatially invariant object representations to selectively excite spatially coded movement plans. Model simulations show how dopaminergic reward and non-reward signals guide monkeys to learn and perform saccadic eye movements in the fixation, single saccade, overlap, gap, and delay (memory-guided) saccade tasks. Model cell activation dynamics quantitatively simulate seventeen established types of dynamics exhibited by corresponding real cells during performance of these tasks.

Preliminary version appears as Boston University Technical Report CAS/CNS TR-2000-023. Available in PDF (BroBulGro2003NN.pdf)(606Kb).

Grossberg, S. (2003). How does the cerebral cortex work? Development, learning, attention, and 3D vision by laminar circuits of visual cortex. Behavioral and Cognitive Neuroscience Reviews, in press.

A key goal of behavioral and cognitive neuroscience is to link brain mechanisms to behavioral functions. The present article describes recent progress towards explaining how the visual cortex sees. Visual cortex, like many parts of perceptual and cognitive neocortex, is organized into six main layers of cells, as well as characteristic sub-lamina. Here it is proposed how these layered circuits help to realize processes of development, learning, perceptual grouping, attention, and 3D vision through a combination of bottom-up, horizontal, and top-down interactions. A key theme is that the mechanisms which enable development and learning to occur in a stable way imply properties of adult behavior. These results thus begin to unify three fields: infant cortical development, adult cortical neurophysiology and anatomy, and adult visual perception. The identified cortical mechanisms promise to generalize to explain how other perceptual and cognitive processes work.

Preliminary version appears as Boston University Technical Report CAS/CNS TR-2003-005. Available in PDF (Gro2003BCNR.pdf)(829Kb).

Grossberg, S. (2003). Resonant neural dynamics of speech perception. Journal of Phonetics, in press.

What is the neural representation of a speech code as it evolves in time? How do listeners integrate temporally distributed phonemic information across hundreds of milliseconds, even backwards in time, into coherent representations of syllables and words? What sorts of brain mechanisms encode the correct temporal order, despite such backwards effects, during speech perception? How does the brain extract rate-invariant properties of variable-rate speech? This article describes an emerging neural model that suggests answers to these questions, while quantitatively simulating challenging data about audition, speech and word recognition. This model includes bottom-up filtering, horizontal competitive, and top-down attentional interactions between a working memory for short-term storage of phonetic items and a list categorization network for grouping sequences of items. The conscious speech and word recognition code is suggested to be a resonant wave of activation across such a network, and a percept of silence is proposed to be a temporal discontinuity in the rate with which such a resonant wave evolves. Properties of these resonant waves can be traced to the brain mechanisms whereby auditory, speech, and language representations are learned in a stable way through time. Because resonances are proposed to control stable learning, the model is called an Adaptive Resonance Theory, or ART, model.

Preliminary version appears as Boston University Technical Report CAS/CNS TR-2002-008. Available in PDF (Gro2003TIPSPhonetics.pdf)(829Kb).

Grossberg, S., Govindarajan, K.K., Wyse, L.L. , and Cohen, M.A. (2003). ARTSTREAM: A neural network model of auditory scene analysis and source segregation. Neural Networks , in press.

Abstract: Multiple sound sources often contain harmonics that overlap and may be degraded by environmental noise. The auditory system is capable of teasing apart these sources into distinct mental objects, or streams. Such an "auditory scene analysis" enables the brain to solve the cocktail party problem. A neural network model of auditory scene analysis, called the ARTSTREAM model, is presented to propose how the brain accomplishes this feat. The model clarifies how the frequency components that correspond to a given acoustic source may be coherently grouped together into a distinct streams based on pitch and spatial location cues. The model also clarifies how multiple streams may be distinguished and separated by the brain. Streams are formed as spectral-pitch resonances that emerge through feedback interactions between frequency-specific spectral representations of a sound source and its pitch. First, the model transforms a sound into a spatial pattern of frequency-specific activation across a spectral stream layer. The sound has multiple parallel representations at this layer. A sound's spectral representation activates a bottom-up filter that is sensitive to the harmonics of the sound's pitch. This filter activates a pitch category which, in turn, activates a top-down expectation that is also sensitive to the harmonics of the pitch. Resonance develops when the spectral and pitch representations mutually reinforce one another. Resonance provides the coherence that allows one voice or instrument to be tracked through a noisy multiple source environment. Spectral components are suppressed if they do not match harmonics of the top-down expectation that is read-out by the selected pitch, thereby allowing another stream to capture these components, as in the "old-plus-new heuristic" of Bregman. Multiple simultaneously occurring spectral-pitch resonances can hereby emerge. These resonance and matching mechanisms are specialized versions of Adaptive Resonance Theory, or ART, which clarifies how pitch representations can self-organize during learning of harmonic bottom-up filters and top-down expectations. The model also clarifies how spatial location cues can help to disambiguate two sources with similar spectral cues. Data are simulated from psychophysical grouping experiments, such as how a tone sweeping upwards in frequency creates a bounce percept by grouping with a downward sweeping tone due to proximity in frequency, even if noise replaces the tones at their intersection point. Illusory auditory percepts are also simulated, such as the auditory continuity illusion of a tone continuing through a noise burst even if the tone is not present during the noise, and the scale illusion of Deutsch whereby downward and upward scales presented alternately to the two ears are regrouped based on frequency proximity, leading to a bounce percept. Since related sorts of resonances have been used to quantitatively simulate psychophysical data about speech perception, the model strengthens the hypothesis that ART-like mechanisms are used at multiple levels of the auditory system. Proposals for developing the model to explain more complex streaming data are also provided.

Key words: auditory scene analysis, streaming, cocktail party problem, pitch perception, spatial localization, neural network, resonance, adaptive resonance theory, ART, spectral-pitch resonance.

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-03-017. Available in PDF ( GroGovWysCoh2003.pdf )(1.53Mb).

Grossberg, S. and Howe, P.D.L. (2003). A laminar cortical model of stereopsis and three-dimensional surface perception. Vision Research, 43, 801-829.

Abstract: A laminar cortical model of stereopsis and later stages of 3D surface perception is developed and simulated. The model describes how initial stages of monocular and binocular oriented filtering interact with later stages of 3D boundary formation and surface filling-in in the lateral geniculate nucleus (LGN) and cortical areas V1, V2, and V4. In particular, it details how interactions between layers 4, 3B, and 2/3A in V1 and V2 contribute to stereopsis, and clarifies how binocular and monocular information combine to form 3D boundary and surface representations. Along the way, the model modifies and significantly extends the disparity energy model. Neural explanations are given for psychophysical data concerning: contrast variations of dichoptic masking and the correspondence problem, the effect of interocular contrast differences on stereoacuity, Panum's limiting case, the Venetian blind illusion, stereopsis with polarity-reversed stereograms, da Vinci stereopsis, and various lightness illusions. By relating physiology to psychophysics, the model provides new functional insights and predictions about laminar cortical architecture.

Keywords: Cortical model; Depth perception; Stereopsis; Surface perception; Cortical layers; Lightness perception; Monocular binocular interactions

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-2002-02 . Available in PDF (GroHow02VisRes.pdf)(1.84MB).

Grossberg, S. and Seitz, A., (2003). Laminar Development of Receptive Fields, Maps, and Columns in Visual Cortex: The Coordinating Role of the Subplate. Cerebral Cortex, in press.

How is development of cortical maps in V1 coordinated across cortical layers to form cortical columns? Previous neural models propose how maps of orientation (OR), ocular dominance (OD), and related properties develop in V1. These models show how spontaneous activity, before eye opening, combined with correlation learning and competition, can generate maps similar to those found in vivo. These models have not discussed laminar architecture or how cells develop and coordinate their connections across cortical layers. This is an important problem since anatomical evidence shows that clusters of horizontal connections form, between iso-oriented regions, in layer 2/3 before being innervated by layer 4 afferents. How are orientations in different layers aligned before these connections form? Anatomical evidence demonstrates that thalamic afferents wait in the subplate for weeks before innervating layer 4. Other evidence shows that ablation of the cortical subplate interferes with the development of OR and OD columns. The model proposes how the subplate develops OR and OD maps, which then entrain and coordinate the development of maps in other lamina. The model demonstrates how these maps may develop in layer 4 by using a known transient subplate-to-layer 4 circuit as a teacher. The model subplate also guides the early clustering of horizontal connections in layer 2/3, and the formation of the interlaminar circuitry that forms cortical columns. It is shown how layer 6 develops and helps to stabilize the network when the subplate atrophies. Finally the model clarifies how BDNF manipulations may influence cortical development.

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-02-006. Available in PDF (SeitzGrossbergCerCor.pdf)(5.85Mb)

• Raizada, R. and Grossberg, S. (2003). Towards a Theory of the Laminar Architecture of Cerebral Cortex: Computational Clues from the Visual System. Cerebral Cortex, 13, 100-113.

One of the most exciting and open research frontiers in neuroscience is that of seeking to understand the functional roles of the layers of cerebral cortex. New experimental techniques for probing the laminar circuitry of cortex have recently been developed, opening up novel opportunities for investigating how its six-layered architecture contributes to perception and cognition. The task of trying to interpret this complex structure can be facilitated by theoretical analyses of the types of computations that cortex is carrying out, and of how these might be implemented in specific cortical circuits. We have recently developed a detailed neural model of how the parvocellular stream of the visual cortex utilizes its feedforward, feedback and horizontal interactions for purposes of visual filtering, attention and perceptual grouping. This model, called LAMINART, shows how these perceptual processes relate to the mechanisms that ensure the stable development of cortical circuits in the infant, and to the continued stability of learning in the adult. The present article reviews this laminar theory of visual cortex, considers how it may be generalized towards a more comprehensive theory that encompasses other cortical areas and cognitive processes, and shows how its laminar framework generates a variety of testable predictions.

       Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-2001-12. Available in PDF (RaiGro03CerCor.pdf)(293KB)

2002

• Grossberg, S., Hwang, S., and Mingolla, E. (2002). Thalamocortical dynamics of the McCollough effect: Boundary-surface alignment through perceptual learning. Vision Research, 42, 1259-1286.

Abstract: This article further develops the FACADE neural model of 3-D vision and figure-ground perception to quantitatively explain properties of the McCollough effect. The model proposes that many McCollough effect data result from visual system mechanisms whose primary function is to adaptively align, through learning, boundary and surface representations that are positionally shifted, due to the process of binocular fusion. For example, binocular boundary representations are shifted by binocular fusion relative to monocular surface representations, yet the boundaries must become positionally aligned with the surfaces to control binocular surface capture and filling-in. The model also includes perceptual reset mechanisms that use habituative transmitters in opponent processing circuits. Thus the model shows how McCollough effect data may arise from a combination of mechanisms that have a clear functional role in biological vision. Simulation results with a single set of parameters quantitatively fit data from thirteen experiments that probe the nature of achromatic/chromatic and monocular/binocular interactions during induction of the McCollough effect. The model proposes how perceptual learning, opponent processing, and habituation at both monocular and binocular surface representations are involved, including early thalamocortical sites. In particular, it explains the anomalous McCollough effect utilizing these multiple processing sites. Alternative models of the McCollough effect are also summarized and compared with the present model.

Key Words: Color perception, Binocular vision, Perceptual learning, Visual cortex, Aftereffects, Boundary segmentation, Surface representation, McCollough effect, FACADE model

Preliminary version appears as Boston University Technical Report, CAS/CNS TR-01-004. Available in PDF (GroHwaMin02.pdf)(254Kb) and in Gzipped Postscript (GroHwaMin02.ps.gz)(171Kb).

2001

·         Granger, E., Rubin, M. , Grossberg, S. , and Lavoie, P. (2001). A what-and-where fusion neural network for recognition and tracking of multiple radar emitters. Neural Networks, in press.

Abstract: A neural network recognition and tracking system is proposed for classification of radar pulses in autonomous Electronic Support Measure systems. Radar type information is combined with position-specific information from active emitters in a scene. Type-specific parameters of the input pulse stream are fed to a neural network classifier trained on samples of data collected in the field. Meanwhile, a clustering algorithm is used to separate pulses from different emitters according to position-specific parameters of the input pulse stream. Classifier responses corresponding to different emitters are separated into tracks, or trajectories, one per active emitter, allowing for more accurate identification of radar types based on multiple views of emitter data along each emitter trajectory. Such a What-and-Where fusion strategy is motivated by a similar subdivision of labor in the brain. The fuzzy ARTMAP neural network is used to classify streams of pulses according to radar type using their functional parameters. Simulation results obtained with a radar pulse data set indicate that fuzzy ARTMAP compares favorably to several other approaches when performance is measured in terms of accuracy and computational complexity. Incorporation into fuzzy ARTMAP of negative match tracking (from ARTMAP-IC) facilitated convergence during training with this data set. Other modifications improved classification of data that include missing input pattern components and missing training classes. Fuzzy ARTMAP was combined with a bank of Kalman filters to group pulses transmitted from different emitters based on their position-specific parameters, and with a module to accumulate evidence from fuzzy ARTMAP responses corresponding to the track defined for each emitter. Simulation results demonstrate that the system provides a high level of performance on complex, incomplete and overlapping radar data.

Key Words: radar, electronic support measures, pattern recognition, data fusion, neural network, ARTMAP, Kalman filter.

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-2000-029. Available in PDF (GraRubGroLav01.pdf)(514Kb) and in Gzipped Postscript (GraRubGroLav01.ps.gz)(271Kb).

·         Grossberg, S., Mingolla, E., and Viswanathan, L. (2001). Neural dynamics of motion integration and segmentation within and across apertures. Vision Research, in press.

Abstract: A neural model is developed of how motion integration and segmentation processes, both within and across apertures, compute global motion percepts. Figure-ground properties, such as occlusion, influence which motion signals determine the percept. For visible apertures, a line's terminators do not specify true line motion. For invisible apertures, a line's intrinsic terminators create veridical feature tracking signals. Sparse feature tracking signals can be amplified before they propagate across position and are integrated with ambiguous motion signals within line interiors. This integration process determines the global percept. It is the result of several processing stages: Directional transient cells respond to image transients and input to a directional short-range filter that selectively boosts feature tracking signals with the help of competitive signals. Then a long-range filter inputs to directional cells that pool signals over multiple orientations, opposite contrast polarities, and depths. This all happens no later than cortical area MT. The directional cells activate a directional grouping network, proposed to occur within cortical area MST, within which directions compete to determine a local winner. Enhanced feature tracking signals typically win over ambiguous motion signals. Model MST cells which encode the winning direction feed back to model MT cells, where they boost directionally consistent cell activities and suppress inconsistent activities over the spatial region to which they project. This feedback accomplishes directional and depthful motion capture within that region. Model simulations include the barberpole illusion, motion capture, the spotted barberpole, the triple barberpole, the occluded translating square illusion, motion transparency and the chopsticks illusion. Qualitative explanations of illusory contours from translating terminators and plaid adaptation are also given.

Key Words: motion integration, motion segmentation, motion capture, aperture problem, feature tracking, MT, MST, neural network

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-2000-004. Available in PDF (GroMinLav01.pdf)(4.3Mb), HTML (GroMinLav01.html)(1.8Mb) and in Gzipped Postscript (GroMinLav01.ps.gz)(3.5Mb).

·         Grossberg, S. and Williamson, J.R. (2001). A neural model of how horizontal and interlaminar connections of visual cortex develop into adult circuits that carry out perceptual groupings and learning. Cerebral Cortex, 11, 37-58.

Abstract: A neural model suggests how horizontal and interlaminar connections in visual cortical areas V1 and V2 develop within a laminar cortical architecture and give rise to adult visual percepts. The model suggests how mechanisms that control cortical development in the infant lead to properties of adult cortical anatomy, neurophysiology and visual perception. The model clarifies how excitatory and inhibitory connections can develop stably by maintaining a balance between excitation and inhibition. The growth of long-range excitatory horizontal connections between layer 2/3 pyramidal cells is balanced against that of short-range disynaptic interneuronal connections. The growth of excitatory on-center connections from layer 6-to-4 is balanced against that of inhibitory interneuronal off-surround connections. These balanced connections interact via intracortical and intercortical feedback to realize properties of perceptual grouping, attention and perceptual learning in the adult, and help to explain the observed variability in the number and temporal distribution of spikes emitted by cortical neurons. The model replicates cortical point spread functions and psychophysical data on the strength of real and illusory contours. The on-center, off-surround layer 6-to-4 circuit enables top-down attentional signals from area V2 to modulate, or attentionally prime, layer 4 cells in area V1 without fully activating them. This modulatory circuit also enables adult perceptual learning within cortical area V1 and V2 to proceed in a stable way.

Keywords:None

On-line version available from Cerebral Cortex (GroWil2000.pdf) (499Kb) in PDF format.

·         Pack, C., Grossberg, S. and Mingolla, E., (2001). A neural model of smooth pursuit control and motion perception by cortical area MST. Journal of Cognitive Neuroscience, in press.

Abstract: Smooth pursuit eye movements are eye rotations that are used to maintain fixation on a moving target. Such rotations complicate the interpretation of the retinal image, because they nullify the retinal motion of the target, while generating retinal motion of stationary objects in the background. This poses a problem for the oculomotor system, which must track the stabilized target image, while suppressing the optokinetic reflex, which would move the eye in the direction of the retinal background motion, which is opposite to the direction in which the target is moving. Similarly, the perceptual system must estimate the actual direction and speed of moving objects in spite of the confounding effects of the eye rotation. This paper proposes a neural model to account for the ability of primates to accomplish these tasks. The model simulates the neurophysiological properties of cell types found in the superior temporal sulcus of the macaque monkey, specifically the medial superior temporal (MST) region. These cells process signals related to target motion, background motion, and receive an efference copy of eye velocity during pursuit movements. The model focuses on the interactions between cells in the ventral and dorsal subdivisions of MST, which are hypothesized to process target velocity and background motion, respectively. The model explains how these signals can be combined to explain behavioral data about pursuit maintenance and perceptual data from human studies, including the Aubert-Fleischl phenomenon and the Filehne Illusion, thereby clarifying the functional significance of neurophysiological data about these MST cell properties. It is suggested that the connectivity used in the model may represent a general strategy used by the brain in analyzing the visual world.

Keywords: smooth pursuit, eye movements, visual cortex, MST, motion, optokinetic nystagmus, target tracking, perception

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-99-023. Available in HTML (PacGroMin.JCN00.html)(89Kb), PDF (PacGroMin.JCN00.pdf)(172Kb), or in Gzipped Postscript (PacGroMin.JCN00.ps.gz)(452Kb).

·         Raizada, R. and Grossberg, S. (2001). Context-sensitive bindings by the laminar circuits of V1 and V2: A unified model of perceptual grouping, attention, and orientation contrast. Visual Cognition, in press.

Abstract: A detailed neural model is presented of how the laminar circuits of visual cortical areas V1 and V2 implement context-sensitive binding processes such as perceptual grouping and attention. The model proposes how specific laminar circuits allow the responses of visual cortical neurons to be determined not only by the stimuli within their classical receptive fields, but also to be strongly influenced by stimuli in the extra-classical surround. This context-sensitive visual processing can greatly enhance the analysis of visual scenes, especially those containing targets that are low contrast, partially occluded, or crowded by distractors. We show how interactions of feedforward, feedback and horizontal circuitry can implement several types of contextual processing simultaneously, using shared laminar circuits. In particular, we present computer simulations which suggest how top-down attention and preattentive perceptual grouping, two processes that are fundamental for visual binding, can interact, with attentional enhancement selectively propagating along groupings of both real and illusory contours, thereby showing how attention can selectively enhance object representations. These simulations also illustrate how attention may have a stronger facilitatory effect on low contrast than on high contrast stimuli, and how pop-out from orientation contrast may occur. The specific functional roles which the model proposes for the cortical layers allow several testable neurophysiological predictions to be made. The results presented here simulate only the boundary grouping system of adult cortical architecture. However, we also discuss how this model contributes to a larger neural theory of vision which suggests how intracortical and intercortical feedback help to stabilize development and learning within these cortical circuits. Although feedback plays a key role, fast feedforward processing is possible in response to unambiguous information. Model circuits are capable of synchronizing quickly, but context-sensitive persistence of previous events can influence how synchrony develops. Although these results focus on how the interblob cortical processing stream controls boundary grouping and attention, related modeling of the blob cortical processing stream suggests how visible surfaces are formed, and modeling of the motion stream suggests how transient responses to scenic changes can control long-range apparent motion and also attract spatial attention.

Keywords: visual cortex, attention, grouping, orientation contrast, cortical layers, V1, V2, feedback, neural network

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-2000-008. Available in PDF (RaiGro01.pdf) (197Kb) and G'zipped Postscript (RaiGro01.ps.gz) (441Kb). Mac browsers may have difficulty with the equations but the document should print properly.

2000

·         Ajemian, R. , Bullock, D., and Grossberg, S. (2000). Kinematic coordinates in which motor cortical cells encode movement direction. Journal of Neurophysiology, 84, 2191-2203.

Abstract: During goal-directed reaching in primates, a sensorimotor transformation generates a dynamical pattern of muscle activation. Within the context of this sensorimotor transformation, a fundamental question concerns the coordinate systems in which individual cells in the primary motor cortex (MI) encode movement direction. This article develops a mathematical framework that computes, as a function of the coordinate system in which an individual cell is hypothesized to operate, the spatial preferred direction (pd) of that cell as the arm configuration and hand location vary. Three coordinate systems are explicitly modeled: Cartesian spatial, shoulder-centered, and joint angle. The computed patterns of spatial pds are distinct for each of these three coordinate systems, and experimental approaches are described which can capitalize upon these differences to compare the empirical adequacy of each coordinate hypothesis. One particular experiment involving curved motion (Hocherman and Wise 1991) was analyzed from this perspective. Out of the three coordinate systems tested, the assumption of joint angle coordinates best explained the observed cellular response properties. The mathematical framework developed in this paper can also be used to design new experiments that are capable of disambiguating between a given set of specified coordinate hypotheses.

Keywords:None

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-98-021. Available in PDF AjeBulGro00.pdf (221Kb), and Gzipped postscript AjeBulGro00.ps.gz (683Kb).

·         Grossberg, S. (2000a). The complementary brain: Unifying brain dynamics and modularity. Trends in Cognitive Sciences, 4, 233-246.

Abstract: How are our brains functionally organized to achieve adaptive behavior in a changing world? This article presents one alternative to the computer metaphor suggesting that brains are organized into independent modules. Evidence is reviewed that brains are organized into parallel processing streams with complementary properties. Hierarchical interactions within each stream and parallel interactions between streams create coherent behavioral representations that overcome the complementary deficiencies of each stream and support unitary conscious experiences. This perspective suggests how brain design reflects the organization of the physical world with which brains interact. Examples from perception, learning, cognition, and action are described, and theoretical concepts and mechanisms by which complementarity is accomplished are presented.

Keywords: modularity, What and Where processing, visual cortex, motor cortex, reinforcement, recognition, attention, learning, expectation, volition, speech, neural network

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-98-003. Available in HTML Gro00a.html (151Kb) and PDF Gro00a.pdf (210Kb).

·         Grossberg, S. Grossberg, S. (2000b). The imbalanced brain: From normal behavior to schizophrenia. Biological Psychiatry, 48, 81-98.

Abstract: An outstanding problem in psychiatry concerns how to link discoveries about the pharmacological, neurophysiological, and neuroanatomical substrates of mental disorders to the abnormal behaviors that they control. A related problem concerns how to understand abnormal behaviors on a continuum with normal behaviors. During the past few decades, neural models have been developed of how normal cognitive and emotional processes learn from the environment, focus attention and act upon motivationally important events, and cope with unexpected events. When arousal or volitional signals in these models are suitably altered, they give rise to symptoms that strikingly resemble negative and positive symptoms of schizophrenia, including flat affect, impoverishment of will, attentional problems, loss of a theory of mind, thought derailment, hallucinations, and delusions. The present article models how emotional centers of the brain, such as the amygdala, interact with sensory and prefrontal cortices (notably ventral, or orbital, prefrontal cortex) to generate affective states, attend to motivationally salient sensory events, and elicit motivated behaviors. When such emotional centers become depressed, negative symptoms of schizophrenia emerge in the model. Such emotional centers are modeled as opponent affective processes, such as fear and relief, whose response amplitude and sensitivity are calibrated by an arousal level and chemical transmitters that slowly inactivate, or habituate, in an activity-dependent way. These opponent processes exhibit an Inverted-U whereby behavior become depressed if the arousal level is chosen too large or too small. The negative symptoms are due to the way in which the depressed opponent process interacts with other circuits throughout the brain.

Keywords:schizophrenia, arousal, prefrontal cortex, amygdala, opponent process, neural networks

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-99-018. Available in HTML Gro.BioPsych2000.html (91Kb), PDF Gro.BioPsy2000.pdf (342Kb), and and Gzipped postscript Gro.BioPsy2000.ps.gz (489Kb).

·         Grossberg, S. (2000c). How hallucinations may arise from brain mechanisms of learning, attention, and volition. Invited article for the Journal of the International Neuropsychological Society, 6, 579-588.

Abstract: This article suggests how brain mechanisms of learning, attention, and volition may give rise to hallucinations during schizophrenia and other mental disorders. The article suggests that normal learning and memory are stabilized through the use of learned top-down expectations. These expectations learn prototypes that are capable of focusing attention upon the combinations of features that comprise conscious perceptual experiences. When top-down expectations are active in a priming situation, they can modulate or sensitize their target cells to respond more effectively to matched bottom-up information. They cannot, however, fully activate these target cells. These matching properties are shown to be essential towards stabilizing the memory of learned representations. The modulatory property of top-down expectations is achieved through a balance between top-down excitation and inhibition. The learned prototype is the excitatory on-center in this top-down network. Phasic volitional signals can shift the balance between excitation and inhibition to favor net excitatory activation. Such a volitionally-mediated shift enables top-down expectations, in the absence of supportive bottom-up inputs, to cause conscious experiences of imagery and inner speech, and thereby to enable fantasy and planning activities to occur. If these volitional signals become tonically hyperactive during a mental disorder, the top-down expectations can give rise to conscious experiences in the absence of bottom-up inputs and volition. These events are compared with data about hallucinations. The article predicts where these top-down expectations and volitional signals may act in the laminar circuits of visual cortex, and by extension in other sensory and cognitive neocortical areas, and how the level of abstractness of learned prototypes may covary with the abstractness of hallucinatory content. A similar breakdown of volition may lead to declusions of control in the motor system.

Key Words: hallucinations, learned expectations, attention, learning, adaptive resonance theory

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-99-020. Available in HTML, Gro99.hall.html PDF Gro99.hall.pdf (6.7Mb), and Gzipped postscript Gro99.hall.ps.gz (4.9Mb).

·         Grossberg, S. (2000d). Linking mind to brain: The mathematics of biological intelligence. Notices of the American Mathematical Society, 47, 1361-1372.

Abstract: How our brains give rise to our minds is one of the most intriguing questions in all of science. We are now living in a particularly interesting time to consider this question. This is true because, during the last decade, mathematical models about how the brain works have finally succeeded in quantitatively simulating the experimentally recorded dynamics of individual cells in identified brain circuits and the behaviors that these circuits control. The models that have led to these successes incorporate qualitatively new ideas about how the brain is organized to achieve the remarkable flexibility and power of biological intelligence. These advances represent significant challenges and opportunities for mathematicians for several reasons. One obvious reason is that the models themselves are interesting mathematical objects. These models are typically defined by high-dimensional dynamical systems in which several types of nonlinear feedback operate across multiple spatial and temporal scales. They represent systems which are capable of autonomously adapting, or self-organizing, in response to a rapidly changing and unpredictable world.

Keywords: None

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-2000-016. Available in HTML Gro00.AMS.html (126Kb) and PDF Gro00.AMS.pdf (112Kb).

·         Grossberg, S. and Myers, C.W. (2000) The resonant dynamics of speech perception: Interword integration and duration-dependent backward effects. Psychological Review, 4, 735-767.

Abstract: How do listeners integrate temporally distributed phonemic information into coherent representations of syllables and words? During fluent speech perception, variations in the durations of speech sounds and silent pauses can produce different perceived groupings. For example, increasing the silence interval between the words ``gray chip'' may result in the percept ``great chip'', whereas increasing the duration of fricative noise in ``chip'' may alter the percept to ``great ship'' (Repp et al., 1978). The ARTWORD neural model quantitatively simulates such context-sensitive speech data. In ARTWORD, sequential activation and storage of phonemic items in working memory provides bottom-up input to unitized representations, or list chunks, that group together sequences of items of variable length. The list chunks compete with each other as they dynamically integrate this bottom-up information. The winning groupings feed back to provide top-down support to their phonemic items. Feedback establishes a resonance which temporarily boosts the activation levels of selected items and chunks, thereby creating an emergent conscious percept. Because the resonance evolves more slowly than working memory activation, it can be influenced by information presented after relatively long intervening silence intervals. The same phonemic input can hereby yield different groupings depending on its arrival time. Processes of resonant transfer and competitive teaming help determine which groupings win the competition. Habituating levels of neurotransmitter along the pathways that sustain the resonant feedback lead to a resonant collapse that permits the formation of subsequent resonances.

Keywords: speech perception, word recognition, consciousness, adaptive resonance, context effects, consonant perception, neural network, silence duration, working memory, categorization, clustering.

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-99-001. Available in HTML GroMye00.html (350Kb), PDF GroMye00.pdf (504Kb) and Gzipped postscript GroMye00.ps.gz (303Kb).

·         Grossberg, S. and Paine, R.W.(2000). A neural model of corticocerebellar interactions during attentive imitation and predicitve learning of sequential handwriting movements. Neural Networks, 13, 999-1046.

Abstract: Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children (Burns, 1962; Freeman, 1914; Iacoboni et al., 1999). Such complex sequential acts are broken down into distinct motor control synergies, or muscle groups, whose activities overlap in time to generate continuous, curved movements that obey an inverse relation between curvature and speed. How are such complex movements learned through attentive imitation? Novel movements may be made as a series of distinct segments that may be quite irregular both in space and time, but a practiced movement can be made smoothly, with a continuous, often bell-shaped, velocity profile. How does learning of sequential movements transform reactive imitation into predictive, automatic performance? A neural model is summarized here which suggests how parietal, frontal, and motor cortical mechanisms, such as difference vector encoding, interact with adaptively-timed, predictive cerebellar learning during movement imitation and predictive performance (Grossberg & Paine, 2000). To initiate movement, visual attention shifts along the shape to be imitated and generates vector movement using motor cortical cells. During such an imitative movement, cerebellar Purkinje cells with a spectrum of delayed response profiles sample and learn the changing directional information and, in turn, send that learned information back to the cortex and eventually to the muscle synergies involved. If the imitative movement deviates from an attentional focus around a shape to be imitated, the visual system shifts attention, and may make an eye movement back to the shape, thereby providing corrective directional information to the arm movement system. This imitative movement cycle repeats until the corticocerebellar system can accurately drive the movement based on memory alone. A cortical working memory buffer transiently stores the cerebellar output and releases it at a variable rate, allowing speed scaling of learned movements which is limited by the rate of cerebellar memory readout. Movements can be learned at variable speeds if the density of the spectrum of delayed cellular responses in the cerebellum varies with speed. Learning at slower speeds facilitates learning at faster speeds. Size can be varied after learning while keeping the movement duration constant (isochrony). Context-effects arise from the overlap of cerebellar memory outputs. The model is used to simulate key psychophysical and neural data about learning to make curved movements, including a decrease in writing time as learning progresses; generation of unimodal, bell-shaped velocity profiles for each movement synergy; size and speed scaling with preservation of the letter shape and the shapes of the velocity profiles; an inverse relation between curvature and tangential velocity; and a Two-Thirds Power Law relation between angular velocity and curvature.

Keywords:Handwriting, learning, imitation, cerebellum, frontal cortex, working memory, motor control

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-2000-009. Available in PDF GroPai00.pdf (988Kb) and Gzipped postscript GroPai00.ps.gz (745Kb).

·         Grossberg, S. and Raizada, R. (2000). Contrast-sensitive perceptual grouping and object-based attention in the laminar circuits of primary visual cortex. Vision Research, 40, 1413-1432.

Recent neurophysiological studies have shown that primary visual cortex, or V1, does more than passively process image features using the feedforward filters suggested by Hubel and Wiesel. It also uses horizontal interactions to group features preattentively into object representations, and feedback interactions to selectively attend to these groupings. All neocortical areas, including V1, are organized into layered circuits. We present a neural model showing how the layered circuits in areas V1 and V2 enable feedforward, horizontal, and feedback interactions to complete perceptual groupings over positions that do not receive contrastive visual inputs, even while attention can only modulate or prime positions that do not receive such inputs. Recent neurophysiological data about how grouping and attention occur and interact in V1 are simulated and explained, and testable predictions are made. These simulations show how attention can selectively propagate along an object grouping and protect it from competitive masking, and how contextual stimuli can enhance or suppress groupings in a contrast-sensitive manner.

Keywords: attention, grouping, cortical layers, V1, V2, feedback, neural network

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-99-008. Available in PDF (GroRai99.pdf) (394Kb), and available in gzip'ed postscript (GroRai99.ps.gz) (186Kb).

·         Kelly, F.J. and Grossberg, S. (2000). Neural dynamics of 3-D surface perception: Figure-ground separation and lightness perception. Perception & Psychophysics, 62, 1596-1619.

Abstract:This article develops the FACADE theory of three-dimensional (3-D) vision to simulate data concerning how two-dimensional (2-D) pictures give rise to 3-D percepts of occluded and occluding surfaces. The theory suggests how geometrical and contrastive properties of an image can either cooperate or compete when forming the boundary and surface representations that subserve conscious visual percepts. Spatially long-range cooperation and short-range competition work together to separate boundaries of occluding figures from their occluded neighbors, thereby providing sensitivity to T-junctions without the need to assume that T-junction "detectors" exist. Both boundary and surface representations of occluded objects may be amodally completed, while the surface representations of unoccluded objects become visible through modal processes. Computer simulations include Bregman-Kanizsa figure-ground separation, Kanizsa stratification, and various lightness percepts, including the Munker-White, Benary cross, and checkerboard percepts.

Keywords: Amodal Completion, Depth Perception, Figure-Ground Perception, Lightness, Visual Cortex, Neural Network

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-98-026. Note that the Appendix equations and Parameter Table are separate files due to size constraints. The paper is available in HTML KelGro2000.PP.html, PDF KelGro2000p.pdf, and Gzipped postscript KelGro2000p.ps.gz. The Appendix Equations are available in HTML KelGro2000.html, PDF KelGro2000.pdf, and Gzipped postscript KelGro2000.ps.gz. The Parameter Table is available separately in HTML table1.html, PDF table1.pdf, and Gzipped postscript table1.ps.gz.

·         Okatan, M. and Grossberg, S. (2000). Frequency-dependent synaptic potentiation, depression, and spike timing induced by Hebbian pairing in cortical pyramidal neurons. Neural Networks, 13, 699-708.

Abstract: Experiments by Markram and Tsodyks (1996) have suggested that Hebbian pairing in cortical pyramidal neurons potentiates or depresses the transmission of a subsequent presynaptic spike train at steady-state depending on whether the spike train is of low frequency or high frequency, respectively. The frequency above which pairing induced a significant decrease in steady-state synaptic efficacy was as low as about 20 Hz and this value depends on such synaptic properties as probability of release and time constant of recovery from short-term synaptic depression. These characteristics of cortical synapses have not yet been fully explained by neural models, notably the decreased steady-state synaptic efficacy at high presynaptic firing rates. This article suggests that this decrease in synaptic efficacy in cortical synapses was not observed at steady-state, but rather during a transition period preceding it whose duration is frequency-dependent. It is shown that the time taken to reach steady-state may be frequency-dependent, and may take considerably longer to occur at high than low frequencies. As a result, the pairing-induced decrease in synaptic efficacy at high presynaptic firing rates helps to localize the firing of the postsynaptic neuron to a short time interval following the onset of high frequency presynaptic spike trains. This effect may "speed up the time scale" in response to high frequency bursts of spikes, and may contribute to rapid synchronization of spike firing across cortical cells that are bound together by associatively learned connections. Key Words: synaptic potentiation, synaptic depression, frequency-dependent synaptic plasticity, cortical pyramidal cells, Hebbian pairing, cortical synchronization

Key Words: synaptic potentiation, synaptic depression, frequency-dependent synaptic plasticity, cortical pyramidal cells, Hebbian pairing, cortical synchronization

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-2000-003. Available in HTML (OkaGro01.htm) (300Kb), PDF (OkaGro01.pdf) (4.295Mb) and in Gzipped Postscript (OkaGro01.ps.gz)(5.253Mb).

·         Ross, Grossberg, S. and Mingolla, E. (2000). Visual cortical mechanisms of perceptual grouping: Interacting layers, networks, columns, and maps. Neural Networks, 13, 571-588.

Abstract: The visual cortex has a laminar organization whose circuits form functional columns in cortical maps. How this laminar architecture supports visual percepts is not well understood. A neural model proposes how the laminar circuits of V1 and V2 generate perceptual groupings that maintain sensitivity to the contrasts and spatial organization of scenic cues. The model can decisively choose which groupings cohere and survive, even while balanced excitatory and inhibitory interactions preserve contrast-sensitive measures of local boundary likelihood or strength. In the model, excitatory inputs from LGN activate layers 4 and 6 of V1. Layer 6 activates an on-center off-surround network of inputs to layer 4. Together these layer 4 inputs preserve analog sensitivity to LGN input contrasts. Layer 4 cells excite pyramidal cells in layer 2/3 which activate monosynaptic long-range horizontal excitatory connections between layer 2/3 pyramidal cells, and short-range disynaptic inhibitory connections mediated by smooth stellate cells. These interactions support inward perceptual grouping between two or more boundary inducers, but not outward grouping from a single inducer. These boundary signals feed back to layer 4 via the layer 6-to-4 on-center off-surround network. This {\em folded feedback} joins cells in different layers into functional columns while selecting winning groupings. Layer 6 in V1 also sends top-down signals to LGN using an on-center off-surround network, which suppresses LGN cells that do not receive feedback, while selecting, enhancing, and synchronizing activity of those that do. The model is used to simulate psychophysical and neurophysiological data about perceptual grouping, including various Gestalt grouping laws.

Keywords: visual cortex, perceptual grouping, cortical layers, cortical columns, cortical maps, cortical feedback, illusory contours, V1, V2, LGN

This paper is only available in print. Requests for a reprint of CAS/CNS-TR-98-023 can be sent to amos@cns.bu.edu. Please include street address.

1999

·         Baloch, A.A., Grossberg, S. Mingolla, E., and Nogueira, C.A.M. (1999). A neural model of first-order and second-order motion perception and magnocellular dynamics. Journal of the Optical Society of America A, 16, 953-978.

Abstract: A neural model of motion perception simulates psychophysical data concerning first-order and second-order motion stimuli, including the reversal of perceived motion direction with distance from the stimulus (Gamma display), and data about directional judgments as a function of relative spatial phase or spatial and temporal frequency. Many other second-order motion percepts that have been ascribed to a second non-Fourier processing stream can also be explained in the model by interactions between ON and OFF cells within a single, neurobiologically interpreted magnocellular processing stream. Yet other percepts may be traced to interactions between form and motion processing streams, rather than to processing within multiple motion processing streams. The model hereby explains why monkeys with lesions of of the parvocellular layers, but not the magnocellular layers, of the lateral geniculate nucleus (LGN) are capable of detecting the correct direction of second-order motion, why most cells in area MT are sensitive to both first-order and second-order motion, and why after APB injection selectively blocks retinal ON bipolar cells, cortical cells are sensitive only to the motion of a moving bright bar's trailing edge. Magnocellular LGN cells show relatively transient responses while parvocellular LGN cells show relatively sustained responses. Correspondingly, the model bases its directional estimates on the outputs of model ON and OFF transient cells that are organized in opponent circuits wherein antagonistic rebounds occur in response to stimulus offset. Center-surround interactions convert these ON and OFF outputs into responses of lightening and darkening cells that are sensitive both to direct inputs and to rebound responses in their receptive field centers and surrounds. The total pattern of activity increments and decrements is used by subsequent processing stages (spatially short-range filters, competitive interactions, spatially long-range filters, and directional grouping cells) to determine the perceived direction of motion.

Keywords: first-order motion, second-order motion, Gamma display, visual cortex, lateral geniculate nucleus, magnocellular processing, neural network

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-96-030. Available in gzip'ed postscript (101Kb). The figure files are available in 3 separate files. Figure file 1. Size in gzip'ed postscript (1,494Kb) Figure file 2. Size in gzip'ed postscript (372Kb) Figure file 3. Size in gzip'ed postscript (611Kb)

·         Boardman, I., Grossberg, S., Myers, C., and Cohen, M. (1999). Neural dynamics of perceptual order and context effects for variable-rate speech syllables. Perception & Psychophysics, 61, 1477-1500.

Abstract: How does the brain extract invariant properties of variable-rate speech? A neural model, called PHONET, is developed to explain aspects of this process and, along the way, data about perceptual context effects. For example, in consonant vowel (CV) syllables such as /ba/ and /wa/, an increase in the duration of the vowel can cause a switch in the percept of the preceding consonant from /w/ to /b/ (Miller and Liberman, 1979). The frequency extent of the initial formant transitions of fixed duration also influences the percept (Schwab, Sawusch, and Nusbaum, 1981). PHONET quantitatively simulates over 98% of the variance in these data using a single set of parameters. The model also qualitatively explains many data about other perceptual context effects. In the model, C and V inputs are filtered by parallel auditory streams that respond preferentially to transient and sustained properties of the acoustic signal before being stored in parallel working memories. A lateral inhibitory network of onset- and rate-sensitive cells in the transient channel extracts measures of frequency transition rate and extent. Greater activation of the transient stream can increase the processing rate in the sustained stream via a cross-stream automatic gain control interaction. The stored activities across these gain-controlled working memories provide a basis for rate-invariant perception, since the transient-to-sustained gain control tends to preserve the relative activities across the transient and sustained working memories as speech rate changes. Comparisons with alternative models tested suggest the fit can not be attributed to the simplicity of the data. Brain analogs of model cell types are described. Key words: context effects, CV syllable, formant transition, neural network, phonetic perception, speech perception, vowel, consonant, sustained cells, transient cells, transition duration, transition rate, working memory.

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-98-004. Available in PDF BoaGroMyeCoh99.pdf and Gzipped postscript BoaGroMyeCoh99.ps.gz (5.4Mb).

·         Brown, J., Bullock, D., and Grossberg, S. (1999). How the basal ganglia use parallel excitatory and inhibitory learning pathways to selectively respond to unexpected rewarding cues. Journal of Neuroscience, 19, 10502-10511.

Abstract: After classically conditioned learning, dopaminergic cells in the substantia nigra pars compacta (SNc) respond immediately to unexpected conditioned stimuli (CS) but omit formerly seen responses to expected unconditioned stimuli, notably rewards. These cells play an important role in reinforcement learning. A neural model explains the key neurophysiological properties of these cells before, during, and after conditioning, as well as related anatomical and neurophysiological data about the pedunculo-pontine tegmental nucleus (PPTN), lateral hypothalamus, ventral striatum, and striosomes. The model proposes how two parallel learning pathways from limbic cortex to the SNc, one devoted to excitatory conditioning (through the ventral striatum, ventral pallidum, and PPTN) and the other to adaptively timed inhibitory conditioning (through the striosomes), control SNc responses. The excitatory pathway generates CS-induced excitatory SNc dopamine bursts. The inhibitory pathway prevents dopamine bursts in response to predictable reward-related signals. When expected rewards are not received, striosomal inhibition of SNc that is unopposed by excitation results in a phasic drop in dopamine cell activity. The adaptively timed inhibitory learning uses an intracellular spectrum of timed responses that is proposed to be similar to adaptively timed cellular mechanisms in the hippocampus and the cerebellum. These mechanisms are proposed to include metabotropic glutamate receptor-mediated Ca2+ spikes that occur with different delays in striosomal cells. A dopaminergic burst in concert with a Ca2+ spike is proposed to potentiate inhibitory learning. The model provides a biologically predictive alternative to temporal difference (TD) conditioning models and explains substantially more data than alternative models.

Keywords: dopamine, substantia nigra, reward, basal ganglia, conditioning, pedunculopontine tegmental nucleus, lateral hypothalamus, striosomes, adaptive timing

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-99-011. Download this paper as: PDF (BroBulGro99.pdf) or Gzipped Postscript (BroBulGro99.ps.gz) (152Kb).

·         Gancarz, G. and Grossberg, S. (1999). A neural model of saccadic eye movement control explains task-specific adaptation. Vision Research, 39, 3123-3143.

Abstract: Multiple brain learning sites are needed to calibrate the accuracy of saccadic eye movements. This is true because saccades can be made reactively to visual cues, attentively to visual or auditory cues, or planned in response to memory cues using visual, parietal, and prefrontal cortex, as well as superior colliculus, cerebellum, and reticular formation. The organization of these sites can be probed by displacing a visual target during a saccade. The resulting adaptation typically shows incomplete and asymmetric transfer between different tasks. A neural model of saccadic system learning is developed to explain these data, as well as data about saccadic coordinate changes.

Keywords: saccades, learning, superior colliculus, cerebellum, parietal cortex, prefrontal cortex, reticular formation, neural network

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-98-024. Download this paper as a Gzipped Postscript GanGro99.ps.gz (138Kb).

·         Grossberg, S. (1999). How does the cerebral cortex work? Learning, attention and grouping by the laminar circuits of visual cortex. Spatial Vision, 12, 163-186.

Abstract: The organization of neocortex into layers is one of its most salient anatomical features. These layers include circuits that form functional columns in cortical maps. A major unsolved problem concerns how bottom-up, top-down, and horizontal interactions are organized within cortical layers to generate adaptive behaviors. This article models how these interactions help visual cortex to realize: (1) the binding process whereby cortex groups distributed data into coherent object representations; (2) the attentional process whereby cortex selectively processes important events; and (3) the developmental and learning processes whereby cortex shapes its circuits to match environmental constraints. New computational ideas about feedback systems suggest how neocortex develops and learns in a stable way, and why top-down attention requires converging bottom-up inputs to fully activate cortical cells, whereas perceptual groupings do not.

Key Words: no key words

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-97-023. Available in HTML spvis5.html

·         Grossberg, S.(1999). The link between brain learning, attention, and consciousness. Consciousness and Cognition, 8, 1-44.

Abstract: The processes whereby our brains continue to learn about a changing world in a stable fashion throughout life are proposed to lead to conscious experiences. These processes include the learning of top-down expectations, the matching of these expectations against bottom-up data, the focusing of attention upon the expected clusters of information, and the development of resonant states between bottom-up and top-down processes as they reach an attentive consensus between what is expected and what is there in the outside world. It is suggested that all conscious states in the brain are resonant states, and that these resonant states trigger learning of sensory and cognitive representations. The models which summarize these concepts are therefore called Adaptive Resonance Theory, or ART, models. Psychophysical and neurobiological data in support of ART are presented from early vision, visual object recognition, auditory streaming, variable-rate speech perception, somatosensory perception, and cognitive-emotional interactions, among others. It is noted that ART mechanisms seem to be operative at all levels of the visual system, and it is proposed how these mechanisms are realized by known laminar circuits of visual cortex. It is predicted that the same circuit realization of ART mechanisms will be found in the laminar circuits of all sensory and cognitive neocortex. Concepts and data are summarized concerning how some visual percepts may be visibly, or modally, perceived, whereas amodal percepts may be consciously recognized even though they are perceptually invisible. It is also suggested that sensory and cognitive processing in the What processing stream of the brain obey top-down matching and learning laws that are often complementary to those used for spatial and motor processing in the brain's Where processing stream. This enables our sensory and cognitive representations to maintain their stability as we learn more about the world, while allowing spatial and motor representations to forget learned maps and gains that are no longer appropriate as our bodies develop and grow from infanthood to adulthood. Procedural memories are proposed to be unconscious because the inhibitory matching process that supports these spatial and motor processes cannot lead to resonance.

Keywords: Learning, expectation, attention, adaptive resonance, neural network, procedural memory, consciousness, object recognition, speech perception.

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-97-018. Availabe in PDF (Gro.concog98.pdf) and Gzipped postscript (Gro.concog98.ps.gz) (170Kb).

• Grossberg, S. and Kelly, F.J. (1999). Neural dynamics of binocular brightness perception. Vision Research, 39, 3796-3816.

Abstract: How does the visual cortex combine information from both eyes to generate perceptual representations of object surfaces? Important clues about this process may be derived from data about the perceived brightnesses of surface regions under binocular viewing conditions, including data about binocular brightness summation in response to ganzfelds, the U-shaped data of Fechner's Paradox that violates binocular brightness summation, and the effects of different combinations of monocular and binocular contours and surface luminance differences on threshold sensitivity to monocular flashes of light. How to reconcile these apparently contradictory data properties has been a severe challenge to previous models, and none has explained them all. The present article quantitatively simulates them all by further developing the FACADE vision model. Key model processes discount the illuminant and compute image contrasts in each monocular channel using shunting on-center off-surround networks; binocularly fuse these discounted monocular signals using shunting on-center off-surround networks with nonlinear excitatory and inhibitory signals; and use these binocularly fused activities to trigger filling-in of a binocular surface representation that represents perceived surface brightness. Previous models that have suggested explanations of subsets of these data are discussed.

Keywords: brightness perception, binocular vision, Fechner's paradox, ganzfeld, neural networks, FACADE theory, BCS, FCS

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-98-018. Available in PDF (GroKel99.pdf) and Gzip'ed postscript GroKel99.ps.gz (549Kb). Appendix Equation file is currently a separate document: Available in PDF (GroKel99b.pdf) and G'zipped postscript. GroKel99b.ps.gz (25Kb).

·         Grossberg, S., Mingolla, E., and Pack C. (1999). A neural model of motion processing and visual navigation by cortical area MST. Cerebral Cortex, 9, 878-895.

Abstract Cells in the dorsal medial superior temporal cortex (MSTd) process optic flow generated by self-motion during visually-guided navigation. A neural model shows how interactions between well-known neural mechanisms (log polar cortical magnification, Gaussian motion-sensitive receptive fields, spatial pooling of motion-sensitive signals, and subtractive extraretinal eye movement signals) lead to emergent properties that quantitatively simulate neurophysiological data about MSTd cell properties and psychophysical data about human navigation. Model cells match MSTd neuron responses to optic flow stimuli placed in different parts of the visual field, including position invariance, tuning curves, preferred spiral directions, direction reversals, average response curves, and preferred locations for stimulus motion centers. The model shows how the preferred motion direction of the most active MSTd cells can explain human judgments of self-motion direction (heading), without using complex heading templates. The model explains when extraretinal eye movement signals are needed for accurate heading perception, and when retinal input is sufficient, and how heading judgments depend on scene layouts and rotation rates.

Keywords: optic flow, heading, pursuit, cortical magnification

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-97-015. Available in HTML GroMinPac99.html. Available in PDF GroMinPac99.pdf, and available in gzip'ed postscript GroMinPac99.ps.gz (1,125Kb).

·         Grossberg, S. and Williamson, J.R. (1999). A self-organizing neural system for learning to recognize textured scenes. Vision Research, 39, 1385-1406.

Abstract: A self-organizing ARTEX model is developed to categorize and classify textured image regions. ARTEX specializes the FACADE model of how the visual cortex sees, and the ART model of how temporal and prefrontal cortices interact with the hippocampal system to learn visual recognition categories and their names. FACADE processing generates a vector of boundary and surface properties, notably texture and brightness properties, by utilizing multi-scale filtering, competition, and diffusive filling-in. Its context-sensitive local measures of textured scenes can be used to recognize scenic properties that gradually change across space, as well as abrupt texture boundaries. ART incrementally learns recognition categories that classify FACADE output vectors, class names of these categories, and their probabilities. Top-down expectations within ART encode learned prototy,pes that pay attention to expected visual features. When novel visual information creates a poor match with the best existing category prototype, a memory search selects a new category with which classify the novel data. ARTEX is compared with psychophysical data, and is bench marked on classification of natural textures and synthetic aperture radar images. It outperforms state-of-the-art systems that use rule-based, backpropagation, and K-nearest neighbor classifiers.

Keywords: pattern recognition; boundary segmentation; surface representation; filling-in; texture classification; neural network; adaptive resonance theory

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-97-001. To download the paper in PDF (GroWil99.pdf) or in Gzipped Postscript GroWil99.ps.gz(1.2Mb).

·         Mingolla, E., Ross, W., and Grossberg, S., (1999) A neural network for enhancing boundaries and surfaces in synthetic aperture radar images. Neural Networks,12, 499-511.

Abstract: A neural network system for boundary segmentation and surface representation, inspired by a new local-circuit model of visual processing in the cerebral cortex, is used to enhance images of range data gathered by a synthetic aperture radar (SAR) sensor. Boundary segmentation is accomplished by an improved Boundary Contour System (BCS) model which completes coherent boundaries that retain their sensitivity to image contrasts and locations. A Feature Contour System (FCS) model compensates for local contrast variations and uses the compensated signals to diffusively fill-in surface regions within the BCS boundaries. Image noise pixels that are not supported by BCS boundaries are hereby eliminated. More generally, BCS/FCS processing normalizes input dynamic range, reduces noise, and enhances contrasts between surface regions. BCS/FCS processing hereby makes structures such as motor vehicles, roads, and buildings more salient to human observers than in original imagery. The new BCS model improves image enhancement with significant reductions in processing time and complexity over previous BCS applications. The new system also outperforms several established techniques for image enhancement.

Keywords:Synthetic aperture radar, neural network, image enhancement, boundary segmentation, diffusion

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-98-032. Note: This paper is difficult to download due to the large size of the figure file. To download only the paper in PDF (MinRosGro99.pdf) or in Gzipped Postscript MinRosGro99.ps.gz(55Kb). To download the figures as a separate large file in PDF MinRosGro99.figs.pdf or in Gzipped Postscript MinRosGro99.figs.ps.gz(2,998Kb). Send mail requests to Robin Amos for a paper copy.

1998

·         Cisek, P., Grossberg, S. and Bullock, D. (1998). A Cortico-Spinal Model of Reaching and Proprioception under Multiple Task Constraints. Journal of Cognitive Neuroscience, 10, 425-444.

Abstract: A model of cortico-spinal trajectory for voluntary reaching movements is developed to functionally interpret a broad range of behavioral, physiological, and anatomical data. The model simulates how arm movements achieve their remarkable effficiency and accuracy in response to widely varying positional, speed, and force constraints. A key issue in arm movement control is how the brain copes with such a wide range of movement contexts. The model suggests how the brain may set automatic and volitional gating mechanisms to vary the balance of static and dynamic feedback information to guide the movement command and to compensate for external forces. For example, with increasing movement speed, the system shifts from a feedback position controller to a feedforward trajectory generator with superimposed dynamics compensation. Simulations of the model illustrate how it reproduces the effects of elastic loads on fast movements, endpoint errors in Coriolis fields, and several effects of muscle tendon vibration, including tonic and antagonist vibration reflexes, position and movement illusions, effects of obstructing the tonic vibration reflex, and reaching undershoots caused by antagonist vibration.

Keywords: no keywords

This paper available by mail only. Contact Robin Amos.

Abstract: A neural model is developed of the neural circuitry in the reticular formation that is used to generate saccadic eye movements. The model simulates the behavior of identified cell types--such as long-lead burst neurons, short-lead excitatory and inhibitory burst neurons, omnipause neurons, and tonic neurons-- under many experimental conditions. Simulated phenomena include: saccade staircases, duration and amplitude of cell discharges for saccades of variable amplitude, component stretching to achieve straight oblique saccades, saturation of saccade velocity after saturation of saccade amplitude in response to high stimulation frequencies, tradeoffs between saccade velocity and duration to generate constant saccade amplitude, conservation of saccade amplitude in response to sufficiently brief stimulation of omnipause neurons, and high velocity smooth eye movements evoked by high levels of electrical stimulation of the superior colliculus. Previous saccade generator models have not explained this range of data. These models have also invoked mechanisms for which no neurophysiological evidence has been forthcoming, such as resetable integrators, perfect integrators, or target position movement commands. The present model utilizes only known reticular formation neurons. It suggests that a key part of the feedback loop within the saccade generator is realized by inhibitory feedback from short-lead to long-lead burst neurons, in response to excitatory feedforward signals from long-lead to short-lead burst neurons. When this property is combined with opponent interactions between agonist and antagonist muscle-controlling neurons, and motor error, or vector, inputs from the superior colliculus and other saccade-controlling brain regions, all of the above data can be explained. Taken together, these components generate a saccade reset cycle whereby activation of long-lead burster neurons inhibits omnipause neurons and thereby disinhibits short-lead excitatory burst neurons. The excitatory short-lead burst neurons can then respond to excitatory inputs from the long-lead burst neurons. Outputs from the excitatory short-lead burst neurons are integrated by the tonic cells while they also inhibit the long-lead burst neurons via inhibitory burst interneurons. When this inhibition is complete, the omnipause neurons are disinhibited. The omnipause neurons can then, once again, inhibit the short-lead burst neurons, whose inhibition of the long-lead burst neurons is thereby removed. The saccadic cycle can then begin again. In response to sustained electrical input, this cycle generates a staircase of identical saccades whose properties match the data much better than the staircases proposed by alternative models. A comparative analysis of the hypotheses and predictive capabilities of other saccade generator models is provided.

Keywords: saccade generator, reticular formation, burst neurons, saccade staircase, neural network

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-98-014. Available in Gzipped postscript (2.35MB).

Grossberg, S. and Pessoa, L. (1998). Texture segregation, surface representation, and figure-ground separation. Vision Research, 38, 2657-2684.

Abstract: A widespread view is that most of texture segregation can be accounted for by differences in the spatial frequency content of texture regions. Evidence from both psychophysical and physiological studies indicate, however, that beyond these early filtering stages, there are stages of 3-D boundary segmentation and surface representation that are used to segregate textures. Chromatic segregation of element-arrangement patterns - as studied by Beck and colleagues - cannot be completely explained by the filtering mechanisms previously employed to account for achromatic segregation. An element arrangement pattern is composed of two types of elements that are arranged differently in different image regions (e.g., vertically on top and diagonally on bottom). FACADE theory mechanisms that have previously been used to explain data about 3-D vision and figure-ground separation are here used to simulate chromatic texture segregation data, including data with equiluminant elements on dark or light homogenous backgrounds, or backgrounds composed of vertical and horizontal dark or light stripes, or horizontal notched stripes. These data include the fact that segregation of patterns composed of red and blue squares decreases with increasing luminance of the interspaces. Asymmetric segregation properties under 3-D viewing conditions with the equiluminant elements close or far are also simulated. Two key model properties are a spatial impenetrability property that inhibits boundary grouping across regions with noncolinear texture elements, and a boundary-surface consistency property that uses feedback between boundary and surface representations to eliminate spurious boundary groupings and separate figures from their backgrounds.

Keywords: figure-ground, perception, texture segregation, 3-D vision, grouping, boundary, surface, color vision, neural networks, filling-in

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-96-025. Available in gzip'ed postscript GroPes97.ps.gz (198Kb).

1997

• Baloch, A.A. and Grossberg, S. A neural model of high-level motion processing: Line motion and formotion dynamics. Vision Research, 37, 3037-3059.

The percepts known variously as the line motion illusion, motion induction, and transformational apparent motion have attracted a great deal of experimental interest, since they sensitively probe interactions between preattentive and attentive vision processes. The present article develops a neural model that qualitatively explains essentially all the data reported thus far, and quantitatively simulates key illustrative percepts. The model suggests how these data arise from neural mechanisms of preattentive boundary and surface formation, long-range apparent motion, form-motion interactions, and spatial attention. Direct cortical V1 $\rightarrow$ MT motion mechanisms and interstream V1 $\rightarrow$ V2 $\rightarrow$ MT form-motion interactions play a key role in model explanations. It is also proposed how preattentive long-range motion mechanisms can engage spatial attention as part of the motion capture process that solves the aperture problem.

Preliminary version appears as Boston University Technical Report, CAS/CNS-TR-96-020. Available in PDF BalGro97.pdf (340Kb) and Gzip'ed postscript BalGro97.ps.gz (171Kb).

·         Grossberg, S., and Cohen, M. (1997). Parallel auditory filtering by sustained and transient channels separates coarticulated vowels and consonants IEEE Transactions on Speech and Audio Processing, 5, 301-318.

Abstract: A neural model of peripheral auditory processing is described and used to separate features of coarticulated vowels and consonants After preprocessing of speech via a filterbank, the model splits into two parallel channels, a sustained channel and a transient channel. The sustained channel is sensitive to relatively stable parts of the speech waveform, notably synchronous properties of the vocalic portion of the stimulus. It extends the dynamic range of eighth nerve filters using coincidence detectors that combine operations of raising to a power, rectification, delay, multiplication, time averaging, and preemphasis. The transient channel is sensitive to critical features at the onsets and offsets of speech segments. It is built up from fast excitatory neurons that are modulated by slow inhibitory interneurons. These units are combined over high- frequency and low-frequency ranges using operations of rectification, normalization, mulUplicative gating, and opponent processing. Detectors sensitive to frication and to onset or offset of stop consonants and vowels are described. Model properties are characterized by mathematical analysis and computer simulations. Neural analogs of model cells in the cochlear nucleus and inferior colliculus are noted, as are psychophysical data about perception of CV syllables that may be explained by the sustained-transient channel hypothesis. The proposed sustained and transient processing seems to be an auditory analog of the sustained and transient processing that is known to occur in vision.

Paper copies of these and Grossberg's other papers available from: Robin Amos
Maintained by Susanne Daley
June 18,2003