Modeling of directional operations in the motor cortex -- a noisy network of spiking neurons is trained to generate a neural vector trajectory. Lukashin, A. V., Wilcox, G. L., & Georgopoulos, A. P. Neural Networks, 9(3):397-410, April, 1996. abstract bibtex A fully connected network of spiking neurons modeling motor cortical directional operations is presented and analyzed. The model allows for the basic biological requirements stemming from the results of experimental studies. The dynamical evolution of the network's output is interpreted as the sequential generation of neuronal population vectors representing the combined directional tendency of the ensemble. Adding these population vectors tip-to-tail yields the neural-vector trajectory that describes the upcoming movement trajectory. The key point of the model is that the intra-network interactions provide sustained dynamics, whereas external inputs are only required to initiate the population. The network is trained to generate neural-vector trajectories corresponding to basic types of two-dimensional movements (the network with specified connections can store one trajectory). A simple modification of the simulated annealing algorithm enables training of the network in the presence of noise. Training in the presence of noise yield's robustness of the learned dynamical behaviors. Another key point of the model is that the directional preference of a single neuron is determined by the synaptic connections. Accordingly, individual preferred directions as well as tuning curves are not assigned, but emerge as the result of interactions inside the population. For trained networks, the spiking behavior of single neurons and correlations between different neurons as well as the global activity of the population are discussed in the light of experimental findings.
@article{ Lukashin_etal96,
author = {Lukashin, A. V. and Wilcox, G. L. and Georgopoulos, A. P.},
title = {Modeling of directional operations in the motor cortex -- a noisy
network of spiking neurons is trained to generate a neural vector
trajectory},
journal = {Neural Networks},
year = {1996},
volume = {9},
pages = {397-410},
number = {3},
month = {April},
abstract = { A fully connected network of spiking neurons modeling motor cortical
directional operations is presented and analyzed. The model allows
for the basic biological requirements stemming from the results of
experimental studies. The dynamical evolution of the network's output
is interpreted as the sequential generation of neuronal population
vectors representing the combined directional tendency of the ensemble.
Adding these population vectors tip-to-tail yields the neural-vector
trajectory that describes the upcoming movement trajectory. The key
point of the model is that the intra-network interactions provide
sustained dynamics, whereas external inputs are only required to
initiate the population. The network is trained to generate neural-vector
trajectories corresponding to basic types of two-dimensional movements
(the network with specified connections can store one trajectory).
A simple modification of the simulated annealing algorithm enables
training of the network in the presence of noise. Training in the
presence of noise yield's robustness of the learned dynamical behaviors.
Another key point of the model is that the directional preference
of a single neuron is determined by the synaptic connections. Accordingly,
individual preferred directions as well as tuning curves are not
assigned, but emerge as the result of interactions inside the population.
For trained networks, the spiking behavior of single neurons and
correlations between different neurons as well as the global activity
of the population are discussed in the light of experimental findings.},
en_number = { }
}
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The dynamical evolution of the network's output is interpreted as the sequential generation of neuronal population vectors representing the combined directional tendency of the ensemble. Adding these population vectors tip-to-tail yields the neural-vector trajectory that describes the upcoming movement trajectory. The key point of the model is that the intra-network interactions provide sustained dynamics, whereas external inputs are only required to initiate the population. The network is trained to generate neural-vector trajectories corresponding to basic types of two-dimensional movements (the network with specified connections can store one trajectory). A simple modification of the simulated annealing algorithm enables training of the network in the presence of noise. Training in the presence of noise yield's robustness of the learned dynamical behaviors. Another key point of the model is that the directional preference of a single neuron is determined by the synaptic connections. Accordingly, individual preferred directions as well as tuning curves are not assigned, but emerge as the result of interactions inside the population. For trained networks, the spiking behavior of single neurons and correlations between different neurons as well as the global activity of the population are discussed in the light of experimental findings.","author":["Lukashin, A. V.","Wilcox, G. L.","Georgopoulos, A. P."],"author_short":["Lukashin, A.<nbsp>V.","Wilcox, G.<nbsp>L.","Georgopoulos, A.<nbsp>P."],"bibtex":"@article{ Lukashin_etal96,\n author = {Lukashin, A. V. and Wilcox, G. L. and Georgopoulos, A. P.},\n title = {Modeling of directional operations in the motor cortex -- a noisy\n\tnetwork of spiking neurons is trained to generate a neural vector\n\ttrajectory},\n journal = {Neural Networks},\n year = {1996},\n volume = {9},\n pages = {397-410},\n number = {3},\n month = {April},\n abstract = { A fully connected network of spiking neurons modeling motor cortical\n\tdirectional operations is presented and analyzed. The model allows\n\tfor the basic biological requirements stemming from the results of\n\texperimental studies. The dynamical evolution of the network's output\n\tis interpreted as the sequential generation of neuronal population\n\tvectors representing the combined directional tendency of the ensemble.\n\tAdding these population vectors tip-to-tail yields the neural-vector\n\ttrajectory that describes the upcoming movement trajectory. 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