Proc. Joint International Conference ICANN/ICONIP 2003, Kaynak et al. (Eds.), Springer, LNCS 2714, pp. 846-853,
The Spike Response Model: a Framework to Predict Neuronal Spike Trains.
R. Jolivet, T.J. Lewis and W. Gerstner
We propose a simple method to map a generic threshold model, namely the Spike Response Model, to artificial
data of neuronal activity using a minimal amount of a priori information. Here, data are generated by a detailed
mathematical model of neuronal activity. The model neuron is driven with in-vivo-like current injected, and we
test to which extent it is possible to predict the spike train of the detailed neuron model from that of the Spike
Response Model. In particular, we look at the number of spikes correctly predicted within a biologically
relevant time window. We find that the Spike Response Model achieves prediction of up to $80\%$ of the
spikes with correct timing ($\pm2$ms). Other characteristics of activity, such as mean rate and coefficient of
variation of spike trains, are predicted in the correct range as well.