Conditional mixture model for correlated neuronal spikes.
Analysis of correlated spike trains is a hot topic of research in computational neuroscience. A general model of probability distributions for spikes includes too many parameters to be of use in analyzing real data. Instead, we need a simple but powerful generative model for correlated spikes. We developed a class of conditional mixture models that includes a number of existing models and analyzed its capabilities and limitations. We apply the model to dynamical aspects of neuron pools. When Hebbian cell assemblies coexist in a pool of neurons, the condition is specified by these assemblies such that the probability distribution of spikes is a mixture of those of the component assemblies. The probabilities of activation of the Hebbian assemblies change dynamically. We used this model as a basis for a competitive model governing the states of assemblies.
Amari
Shun-ichi
S
Laboratory for Mathematical Neuroscience, RIKEN Brain Science Institute, Wako-shi, Hirosawa 2-1, Saitama 351-0198, Japan. amari@brain.riken.jp
D000200:Action Potentials / Q000502:physiology*; D000818:Animals; D003198:Computer Simulation; D006801:Humans; D009424:Nervous System Physiological Phenomena; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D011930:Reaction Time / Q000502:physiology; D009435:Synaptic Transmission / Q000502:physiology