A dynamic neural network with temporal coding and functional connectivity.
A neural network model capable of altering its pattern classifying properties by program input is proposed. Here the "program input" is another source of input besides the pattern input. Unlike most neural network models, this model runs as a deterministic point process of spikes in continuous time; connections among neurons have finite delays, which are set randomly according to a normal distribution. Furthermore, this model utilizes functional connectivity which is dynamic connectivity among neurons peculiar to temporal-coding neural networks with short neuronal decay time constants. Computer simulation of the proposed network has been performed, and the results are considered in light of experimental results shown recently for correlated firings of neurons.
Watanabe
M
M
Department of Quantum Engineering and Systems Science, School of Engineering, University of Tokyo, Japan.
Aihara
K
K
Kondo
S
S
D003198:Computer Simulation; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology*; D009474:Neurons / Q000502:physiology*; D011930:Reaction Time / Q000502:physiology*