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S Grossberg

Publications and source records attributed to S Grossberg.

At least 19 recordsLinked to original sources
PubMed2004

A quantitative evaluation of the AVITEWRITE model of handwriting learning.

Much sensory-motor behavior develops through imitation, as during the learning of handwriting by children. 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. The adaptive vector integration to endpoint handwriting (AVITEWRITE) model of Grossberg and Paine (2000) [A neural model of corticocerebellar interactions during attentive imitation and predictive learning of sequential handwriting movements. Neural Networks, 13, 999-1046] addressed how such complex movements may be learned through attentive imitation. The model suggested how parietal and motor cortical mechanisms, such as difference vector encoding, interact with adaptively-timed, predictive cerebellar learning during movement imitation and predictive performance. Key psychophysical and neural data about learning to make curved movements were simulated, including a decrease in writing time as learning progresses; generation of unimodal, bell-shaped velocity profiles for each movement synergy; size scaling with isochrony, 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. However, the model learned from letter trajectories of only one subject, and only qualitative kinematic comparisons were made with previously published human data. The present work describes a quantitative test of AVITEWRITE through direct comparison of a corpus of human handwriting data with the model's performance when it learns by tracing the human trajectories. The results show that model performance was variable across the subjects, with an average correlation between the model and human data of 0.89+/-0.10. The present data from simulations using the AVITEWRITE model highlight some of its strengths while focusing attention on areas, such as novel shape learning in children, where all models of handwriting and the learning of other complex sensory-motor skills would benefit from further research.

Attention↗
PubMed2001

A neural model of smooth pursuit control and motion perception by cortical area MST.

Smooth pursuit eye movements (SPEMs) 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 (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.

Animals↗
PubMed2001

Neural dynamics of motion integration and segmentation within and across apertures.

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 that 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.

Computer Simulation↗
PubMed2001

Linking the laminar circuits of visual cortex to visual perception: development, grouping, and attention.

How do the laminar circuits of visual cortical areas V1 and V2 implement context-sensitive binding processes such as perceptual grouping and attention, and how do these circuits develop and learn in a stable way? Recent neural models clarify how preattentive and attentive perceptual mechanisms are intimately linked within the laminar circuits of visual cortex, notably how bottom-up, top-down, and horizontal cortical connections interact within the cortical layers. These laminar circuits allow the responses of visual cortical neurons to be influenced, not only by the stimuli within their classical receptive fields, but also by stimuli in the extra-classical surround. Such 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. Attentional enhancement can selectively propagate along groupings of both real and illusory contours, thereby showing how attention can selectively enhance object representations. Recent models explain 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. Model mechanisms clarify how intracortical and intercortical feedback help to stabilize cortical development and learning. 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.

Animals↗
PubMed2001

A what-and-where fusion neural network for recognition and tracking of multiple radar emitters.

A neural network recognition and tracking system is proposed for classification of radar pulses in autonomous Electronic Support Measure systems. Radar type information is considered 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.

Algorithms↗
PubMed2001

A neural model of how horizontal and interlaminar connections of visual cortex develop into adult circuits that carry out perceptual grouping and learning.

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.

Animals↗
PubMed2001

A model of movement coordinates in the motor cortex: posture-dependent changes in the gain and direction of single cell tuning curves.

This article outlines a methodology for investigating the coordinate systems by which movement variables are encoded in the firing rates of individual motor cortical neurons. Recent neurophysiological experiments have probed the issue of underlying coordinates by examining how cellular preferred directions (as determined by the center-out task) change with posture. Several key experimental findings have resulted that constrain hypotheses about how motor cortical cells encode movement information. But while the significance of shifts in preferred direction is well known and widely accepted, posture-dependent changes in the depth of modulation of a cell's tuning curve--that is, gain changes--have not been similarly identified as a means of coordinate inference. This article develops a vector field framework in which the preferred direction and the gain of a cell's tuning curve are viewed as dual components of a unitary response vector. The formalism can be used to compute how each aspect of cell response covaries with posture as a function of the coordinate system in which a given cell is hypothesized to encode its movement information. Such an integrated approach leads to a model of motor cortical cell activity that codifies the following four observations: (i) cell activity correlates with hand movement direction; (ii) cell activity correlates with hand movement speed; (iii) preferred directions vary with posture; and (iv) the modulation depth of tuning curves varies with posture. Finally, the model suggests general methods for testing coordinate hypotheses at the single-cell level and simulates an example protocol for three possible coordinate systems: Cartesian spatial, shoulder-centered, and joint angle.

Animals↗
PubMed2000

The imbalanced brain: from normal behavior to schizophrenia.

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. This 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. Closing this feedback loop between cognitive and emotional centers is predicted to generate a cognitive-emotional resonance that can support conscious awareness. 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 becomes depressed if the arousal level is chosen too large or too small. The negative symptoms are owing to the way in which the depressed opponent process interacts with other circuits throughout the brain.

Affect↗
PubMed2000

Contrast-sensitive perceptual grouping and object-based attention in the laminar circuits of primary visual cortex.

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.

Attention↗
PubMed2000

Frequency-dependent synaptic potentiation, depression and spike timing induced by Hebbian pairing in cortical pyramidal neurons.

Experiments by Markram and Tsodyks (Nature, 382 (1996) 807-810) have suggested that Hebbian pairing in cortical pyramidal neurons potentiates or depresses the transmission of a subsequent pre-synaptic 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 pre-synaptic 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 pre-synaptic firing rates helps to localize the firing of the post-synaptic neuron to a short time interval following the onset of high-frequency pre-synaptic 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.

Action Potentials↗
PubMed2000

Visual cortical mechanisms of perceptual grouping: interacting layers, networks, columns, and maps.

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 lateral geniculate nucleus (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 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.

Animals↗
PubMed2000

The complementary brain: unifying brain dynamics and modularity.

How are our brains functionally organized to achieve adaptive behavior in a changing world? This article presents one alternative to the computer analogy that suggests brains are organized into independent modules. Evidence is reviewed that brains are in fact 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 might be accomplished are presented.

Journal Article↗
PubMed2000

How hallucinations may arise from brain mechanisms of learning, attention, and volition.

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 delusions of control in the motor system.

Attention↗
PubMed2000

The resonant dynamics of speech perception: interword integration and duration-dependent backward effects.

How do listeners integrate temporally distributed phonemic information into coherent representations of syllables and words? 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" (B. H. Repp, A. M. Liberman, T. Eccardt, & D. Pesetsky, 1978). The ARTWORD neural model quantitatively simulates such context-sensitive speech data. In ARTWORD, sequentially stored phonemic items in working memory provide bottom-up input to unitized list chunks that group together sequences of items of variable length. The list chunks compete with each other. The winning groupings feed back to establish a resonance which temporarily boosts the activation levels of selected items and chunks, thereby creating an emergent conscious percept whose properties match such data.

Cues↗
PubMed2000

Kinematic coordinates in which motor cortical cells encode movement direction.

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 that can capitalize on these differences to compare the empirical adequacy of each coordinate hypothesis. One particular experiment involving curved motion 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.

Animals↗
PubMed2000

Neural dynamics of 3-D surface perception: figure-ground separation and lightness perception.

This article develops the FACADE theory of three-dimensional (3-D) vision to simulate data concerning how two-dimensional 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, whereas 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 Münker-White, Benary cross, and checkerboard percepts.

Attention↗
PubMed1999

How the basal ganglia use parallel excitatory and inhibitory learning pathways to selectively respond to unexpected rewarding cues.

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 pedunculopontine 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 cerebellum. These mechanisms are proposed to include metabotropic glutamate receptor-mediated Ca(2+) spikes that occur with different delays in striosomal cells. A dopaminergic burst in concert with a Ca(2+) spike is proposed to potentiate inhibitory learning. The model provides a biologically predictive alternative to temporal difference conditioning models and explains substantially more data than alternative models.

Animals↗