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Biomedical subjects

S Grossberg

Publications and source records attributed to S Grossberg.

107 records · Page 6Linked to original sources
PubMed1971

Pavlovian pattern learning by nonlinear neural networks.

This note describes laws for the anatomy, potentials, spiking rules, and transmitters of some networks of formal neurons that enable them to learn spatial patterns by Pavlovian conditioning. Applications to spacetime pattern learning and operant conditioning are then possible, if the conditioning is viewed as multi-channel Pavlovian conditioning in a highly inhomogeneous anatomy. In suitable anatomies, biases in learning because of axon collaterals with nonuniformly distributed diameters can be corrected if one properly couples the action potential to transmitter potentiation, and chooses signal velocity proportional to axon diameter. These anatomies can contain any number of cells. Anatomies exist in which patterns may be learned without their being practiced overtly, whereas persistent recall of old patterns without the learning of newly imposed patterns is impossible. Physiologically, this constraint has the trivial interpretation that signals from one cell to another first pass through the intervening synaptic knob. Mechanisms that control learning rates at times important to the network (e.g., reward and punishment times) can be discussed. Serial behavior like that described by Lashley is possible: this consists of sequential learning and performance of patterns faster than would be allowed by a motor-feedback control, at velocities influenced by arousal level, with the possibility of abrupt termination of performance if conflicting environmental demands arise. Analogs of pattern completion and mass action exist, as do phase transitions in memory (for some rate parameters and anatomies, memory is rigid, for others, it is plastic). The laws limit the ways in which these networks can be interconnected to yield specific discrimination, learning, memory, and recall capabilities.

Action Potentials↗
PubMedDate not supplied

A neural model of cortico-cerebellar interactions during attentive imitation and predictive learning of sequential handwriting movements.

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. How are such complex movements learned through attentive imitation? Novel movements may be made as a series of distinct segments, but a practiced movement can be made smoothly, with a continuous, often bell-shaped, velocity profile. How does learning of complex movements transform reactive imitation into predictive, automatic performance? A neural model is developed which suggests how parietal and motor cortical mechanisms, such as difference vector encoding, interact with adaptively timed, predictive cerebellar learning during movement imitation and predictive performance. 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 cortico-cerebellar 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.

Attention↗
PubMedDate not supplied

Brightness perception, illusory contours, and corticogeniculate feedback.

A neural network model is developed to explain how visual thalamocortical interactions give rise to boundary percepts such as illusory contours and surface percepts such as filled-in brightnesses. Top-down feedback interactions are needed in addition to bottom-up feed-forward interactions to simulate these data. One feedback loop is modeled between lateral geniculate nucleus (LGN) and cortical area V1, and another within cortical areas V1 and V2. The first feedback loop realizes a matching process which enhances LGN cell activities that are consistent with those of active cortical cells, and suppresses LGN activities that are not. This corticogeniculate feedback, being endstopped and oriented, also enhances LGN ON cell activations at the ends of thin dark lines, thereby leading to enhanced cortical brightness percepts when the lines group into closed illusory contours. The second feedback loop generates boundary representations, including illusory contours, that coherently bind distributed cortical features together. Brightness percepts form within the surface representations through a diffusive filling-in process that is contained by resistive gating signals from the boundary representations. The model is used to simulate illusory contours and surface brightness induced by Ehrenstein disks, Kanizsa squares, Glass patterns, and café wall patterns in single contrast, reverse contrast, and mixed contrast configurations. These examples illustrate how boundary and surface mechanisms can generate percepts that are highly context-sensitive, including how illusory contours can be amodally recognized without being seen, how model simple cells in V1 respond preferentially to luminance discontinuities using inputs from both LGN ON and OFF cells, how model bipole cells in V2 with two colinear receptive fields can help to complete curved illusory contours, how short-range simple cell groupings and long-range bipole cell groupings can sometimes generate different outcomes, and how model double-opponent, filling-in and boundary segmentation mechanisms in V4 interact to generate surface brightness percepts in which filling-in of enhanced brightness and darkness can occur before the net brightness distribution is computed by double-opponent interactions.

Computer Simulation↗
PubMedDate not supplied

Cortical networks for control of voluntary arm movements under variable force conditions.

A neural model of voluntary movement and proprioception is developed that offers an integrated interpretation of the functional roles of diverse cell types in movement-related areas of primate cortex. The model circuit maintains accurate proprioception while controlling voluntary reaches to spatial targets, exertion of force against obstacles, posture maintenance despite perturbations, compliance with an imposed movement, and static and inertial load compensations. Computer simulations show that properties of model elements correspond to the properties of many known cells types in areas 4 and 5. Among these properties are delay period activation, response profiles during movement, kinematic and kinetic sensitivities, and latency of activity onset. In particular, area 4 phasic and tonic cells, respectively, compute velocity and position commands that are capable of activating alpha and gamma motor neurons, thereby shifting the mechanical equilibrium point. Anterior area 5 cells compute the position of the limb using corollary discharges from area 4 and feedback from muscle spindles. Posterior area 5 neurons use the position perception signal and a target position signal to compute a desired movement vector. The cortical loop is closed by a volition-gated projection of this movement vector to the area 4 phasic cells. An auxiliary circuit allows phasic-tonic cells in area 4 to incorporate force command components needed to compensate for static and inertial loads. After reporting simulations of prior experimental results, predictions are made for both motor and parietal cell types under novel experimental protocols.

Algorithms↗
PubMedDate not supplied

A neural model of cerebellar learning for arm movement control: cortico-spino-cerebellar dynamics.

A neural network model of opponent cerebellar learning for arm movement control is proposed. The model illustrates how a central pattern generator in cortex and basal ganglia, a neuromuscular force controller in spinal cord, and an adaptive cerebellum cooperate to reduce motor variability during multijoint arm movements using mono- and bi-articular muscles. Cerebellar learning modifies velocity commands to produce phasic antagonist bursts at interpositus nucleus cells whose feed-forward action overcomes inherent limitations of spinal feedback control of tracking. Excitation of alpha motoneuron pools, combined with inhibition of their Renshaw cells by the cerebellum, facilitate movement initiation and optimal execution. Transcerebellar pathways are opened by learning through long-term depression (LTD) of parallel fiber-Purkinje cell synapses in response to conjunctive stimulation of parallel fibers and climbing fiber discharges that signal muscle stretch errors. The cerebellar circuitry also learns to control opponent muscles pairs, allowing cocontraction and reciprocal inhibition of muscles. Learning is stable, exhibits load compensation properties, and generalizes better across movement speeds if motoneuron pools obey the size principle. The intermittency of climbing fiber discharges maintains stable learning. Long-term potentiation (LTP) in response to uncorrelated parallel fiber signals enables previously weakened synapses to recover. Loss of climbing fibers, in the presence of LTP, can erode normal opponent signal processing. Simulated lesions of the cerebellar network reproduce symptoms of cerebellar disease, including sluggish movement onsets, poor execution of multijoint plans, and abnormally prolonged endpoint oscillations.

Adaptation, Physiological↗
PubMedDate not supplied

Predictive regulation of associative learning in a neural network by reinforcement and attentive feedback.

At least four types of learning processes are relevant in the present paper: learning of conditioned reinforcement, incentive motivation, sensory expectancy, and motor command. These several types of learning processes, which operate on a slow time scale, regulate and are regulated by rapidly fluctuating limited capacity STM representations of sensory events. The theory suggest how nonlinear feedback interactions among these fast information processing mechanisms and slow learning mechanisms participate in different conditioning paradigms, and actively regulate learning and memory to generate predictive internal representations of external environmental contingencies.

Animals↗