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  • 1
    Electronic Resource
    Electronic Resource
    Arm movements in monkeys: behavior and neurophysiology (1996)
    Springer
    Journal of comparative physiology 179 (1996), S. 603-612 
    Details
    ISSN: 1432-1351
    Keywords: Monkey ; Motor cortex ; Neuronal populations ; Directional tuning ; Spinal Cord
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Medicine
    Notes: Abstract Reaching to objects of interest is very common in the behavioral repertoire of primates. Monkeys possess keen binocular vision and make graceful and accurate arm movements. This review focuses on behavioral and neurophysiological aspects of eye-hand coordination in behaving monkeys, including neural coding mechanisms at the single cell level and in neuronal populations. The results of these studies have converged to a common behavioral-neurophysiological ground and provided a springboard for studies of brain mechanisms underlying motor cognitive function.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00216125
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Modeling motor cortical operations by an attractor network of stochastic neurons (1996)
    Springer
    Biological cybernetics 74 (1996), S. 255-261 
    Details
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract Understanding the neural computations performed by the motor cortex requires biologically plausible models that account for cell discharge patterns revealed by neurophysiological recordings. In the present study the motor cortical activity underlying movement generation is modeled as the dynamic evolution of a large fully recurrent network of stochastic spiking neurons with noise superimposed on the synaptic transmission. We show that neural representations of the learned movement trajectories can be stored in the connectivity matrix in such a way that, when activated, a particular trajectory evolves in time as a dynamic attractor of the system while individual neurons fire irregularly with large variability in their interspike intervals. Moreover, the encoding of trajectories as attractors ensures high stability of the ensemble dynamics in the presence of synaptic noise. In agreement with neurophysiological findings, the suggested model can provide a wide repertoire of specific motor behaviors, whereas the number of specialized cells and specific connections may be negligibly small if compared with the whole population engaged in trajectory retrieving. To examine the applicability of the model we study quantitatively the relationship between local geometrical and kinematic characteristics of the trajectories generated by the network. The relationship obtained as a result of simulations is close to the ‘2/3 power law’ established by psychophysical and neurophysiological studies.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00652226
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    Paper (German National Licenses)
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  • 3
    Electronic Resource
    Electronic Resource
    A dynamical neural network model for motor cortical activity during movement: population coding of movement trajectories (1993)
    Springer
    Biological cybernetics 69 (1993), S. 517-524 
    Details
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract As a dynamical model for motor cortical activity during hand movement we consider an artificial neural network that consists of extensively interconnected neuron-like units and performs the neuronal population vector operations. Local geometrical parameters of a desired curve are introduced into the network as an external input. The output of the model is a time-dependent direction and length of the neuronal population vector which is calculated as a sum of the activity of directionally tuned neurons in the ensemble. The main feature of the model is that dynamical behavior of the neuronal population vector is the result of connections between directionally tuned neurons rather than being imposed externally. The dynamics is governed by a system of coupled nonlinear differential equations. Connections between neurons are assigned in the simplest and most common way so as to fulfill basic requirements stemming from experimental findings concerning the directional tuning of individual neurons and the stabilization of the neuronal population vector, as well as from previous theoretical studies. The dynamical behavior of the model reveals a close similarity with the experimentally observed dynamics of the neuronal population vector. Specifically, in the framework of the model it is possible to describe a geometrical curve in terms of the time series of the population vector. A correlation between the dynamical behavior of the direction and the length of the population vector entails a dependence of the “neural velocity” on the curvature of the tracing trajectory that corresponds well to the experimentally measured covariation between tangential velocity and curvature in drawing tasks.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00199451
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  • 4
    Electronic Resource
    Electronic Resource
    A dynamical neural network model for motor cortical activity during movement: population coding of movement trajectories (1993)
    Springer
    Biological cybernetics 69 (1993), S. 517-524 
    Details
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract As a dynamical model for motor cortical activity during hand movement we consider an artificial neural network that consists of extensively interconnected neuron-like units and performs the neuronal population vector operations. Local geometrical parameters of a desired curve are introduced into the network as an external input. The output of the model is a time-dependent direction and length of the neuronal population vector which is calculated as a sum of the activity of directionally tuned neurons in the ensemble. The main feature of the model is that dynamical behavior of the neuronal population vector is the result of connections between directionally tuned neurons rather than being imposed externally. The dynamics is governed by a system of coupled nonlinear differential equations. Connections between neurons are assigned in the simplest and most common way so as to fulfill basic requirements stemming from experimental findings concerning the directional tuning of individual neurons and the stabilization of the neuronal population vector, as well as from previous theoretical studies. The dynamical behavior of the model reveals a close similarity with the experimentally observed dynamics of the neuronal population vector. Specifically, in the framework of the model it is possible to describe a geometrical curve in terms of the time series of the population vector. A correlation between the dynamical behavior of the direction and the length of the population vector entails a dependence of the “neural velocity” on the curvature of the tracing trajectory that corresponds well to the experimentally measured covariation between tangential velocity and curvature in drawing tasks.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01185423
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  • 5
    Electronic Resource
    Electronic Resource
    Modeling motor cortical operations by an attractor network of stochastic neurons (1996)
    Springer
    Biological cybernetics 74 (1996), S. 255-261 
    Details
    ISSN: 1432-0770
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Computer Science , Physics
    Notes: Abstract.  Understanding the neural computations performed by the motor cortex requires biologically plausible models that account for cell discharge patterns revealed by neurophysiological recordings. In the present study the motor cortical activity underlying movement generation is modeled as the dynamic evolution of a large fully recurrent network of stochastic spiking neurons with noise superimposed on the synaptic transmission. We show that neural representations of the learned movement trajectories can be stored in the connectivity matrix in such a way that, when activated, a particular trajectory evolves in time as a dynamic attractor of the system while individual neurons fire irregularly with large variability in their interspike intervals. Moreover, the encoding of trajectories as attractors ensures high stability of the ensemble dynamics in the presence of synaptic noise. In agreement with neurophysiological findings, the suggested model can provide a wide repertoire of specific motor behaviors, whereas the number of specialized cells and specific connections may be negligibly small if compared with the whole population engaged in trajectory retrieving. To examine the applicability of the model we study quantitatively the relationship between local geometrical and kinematic characteristics of the trajectories generated by the network. The relationship obtained as a result of simulations is close to the ‘2/3 power law’ established by psychophysical and neurophysiological studies.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00652226
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  • 6
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    A dynamical neural network model for motor cortical activity during movement: population coding of movement trajectories (1993)
    Springer
    Details
    Publication Date: 1993-09-01
    Print ISSN: 0340-1200
    Electronic ISSN: 1432-0770
    Topics: Biology , Computer Science , Physics
    Published by Springer
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  • 7
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    Modeling motor cortical operations by an attractor network of stochastic neurons (1996)
    Springer
    Details
    Publication Date: 1996-03-01
    Print ISSN: 0340-1200
    Electronic ISSN: 1432-0770
    Topics: Biology , Computer Science , Physics
    Published by Springer
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  • 8
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    Modeling motor cortical operations by an attractor network of stochastic neurons (1996)
    Springer
    Details
    Publication Date: 1996-03-01
    Print ISSN: 0340-1200
    Electronic ISSN: 1432-0770
    Topics: Biology , Computer Science , Physics
    Published by Springer
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    PAPER CURRENT
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