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Correlation Coding in Stochastic Neural Networks
| Content Provider | CiteSeerX |
|---|---|
| Author | Gerstner, Wulfram Germond, Alain Ritz, Raphael Sejnowski, Terrence J. |
| Abstract | Abstract. Stimulus4ependent changes have been observed in the cor-relations between the spike trains of simultaneously-recorded pairs of neurons from the auditory cortex of marmosets even when there was no change in the average firing rates. A simple neural model can reproduce most of the characteristics of these experimental observations based on model neurons having leaky integration and fire-and-reset spikes and with Poisson-dis tributed, balanced input. The source of the synchrony in the model was common sensory input. The outputs of neurons in the model appear noisy (almost Poisson) owing to the stochastic nature of the input signal, but there is nevertheless a strong central peak in the correlation of the output spike trains. The experimental data and this simple model clearly demonstrate how even a noisy-looking spike train can convey basic3nformation about a sensory stimulus in the relative spike timing between neurons. 1 |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Stochastic Neural Network Correlation Coding Sensory Stimulus Strong Central Peak Simultaneously-recorded Pair Model Neuron Noisy-looking Spike Train Simple Model Fire-and-reset Spike Average Firing Rate Stochastic Nature Stimulus4ependent Change Experimental Observation Output Spike Train Auditory Cortex Spike Train Relative Spike Timing Common Sensory Input Leaky Integration Input Signal Experimental Data Simple Neural Model |
| Content Type | Text |
| Resource Type | Article |
