Sander Bohte - Efficient Adaptive Spike-Coding in a Spike Response Model

Neural adaptation underlies the ability of neurons to maximize encoded information over a wide dynamic range of input stimuli. While adaptation is an intrinsic feature of neuronal  models like the Hodgkin-Huxley model, the challenge is to integrate adaptation in models of neural computation. Taking a cue from kinetic models of adaptation, we propose an  Adaptive Spike Response Model where the spike-triggered adaptation dynamics are scaled multiplicatively by the adaptation state at the time of spiking. We show that in such a model, the firing rate in the multiplicative adaptation model saturates to a maximum spike-rate. When simulating variance-switching experiments, the model also quantitatively fits the experimental data over a wide dynamic range. Furthermore, such multiplicative spike-triggered adaptation suggests a straightforward interpretation of neural activity in terms of dynamic signal encoding with shifted and weighted exponential kernels. We show that when thus encoding rectified filtered stimulus signals, the Adaptive Spike Response Model achieves a high coding efficiency and maintains this efficiency over changes in the dynamic signal range of several orders of magnitude, without changing model parameters.