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 yields 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.
|Original language||English (US)|
|Number of pages||14|
|State||Published - Apr 1996|
Bibliographical noteFunding Information:
Acknowledgements: This work was supported by United States Public Health Service grant PSMH48185 to A. P. Genrgopoulos, a contract from Office of Naval Research to A. P. Gcorgopoulos and A. V. Lukashin, and grants of supercomputer resources from the Minnesota Supercomputer Institute to G. L. Wilcox.
- Gaussian noise
- Simulated annealing
- Synaptic weight