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A Spike-Based Model of Neuronal Intrinsic Plasticity

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2 Author(s)
Chunguang Li ; Department of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, People's Republic of China ; Yuke Li

The discovery of neuronal intrinsic plasticity (IP) processes which persistently modify a neuron's excitability necessitates a new concept of the neuronal plasticity mechanism and may profoundly influence our ideas on learning and memory. In this paper, we propose a spike-based IP model/adaptation rule for an integrate-and-fire (IF) neuron to model this biological phenomenon. By utilizing spikes denoted by Dirac delta functions rather than computing instantaneous firing rates for the time-dependent stimulus, this simple adaptation rule adjusts two parameters of an individual IF neuron to modify its excitability. As a result, this adaptation rule helps an IF neuron to keep its firing activity in a relatively “low but not too low” level and makes the spike-count distributions computed with adjusted window sizes similar to the experimental results.

Published in:

IEEE Transactions on Autonomous Mental Development  (Volume:5 ,  Issue: 1 )