A Neural Network Model for Trace Conditioning
We studied the dynamics of a neural network which have both of recurrent excitatory and random inhibitory connections. Neurons started to become active when a relatively weak transient excitatory signal was presented and the activity sustained due to the recurrent excitatory connections. The sustained activity stopped when a strong transient signal was presented or when neurons were disinhibited. The random inhibitory connections modulated the activity patterns of neurons so that the patterns evolved without recurrence with time. Hence, a time passage between the onsets of the two transient signals was represented by the sequence of activity patterns.
We then applied this model to the trace eyeblink conditioning which is mediated by the hippocampus. We assumed this model as CA3 of hippocampus and considered an output neuron corresponding a neuron in CA1. The activity pattern of the output neuron was similar to that of CA1 neurons during the trace conditioning which was experimentally observed.
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