|
Spike-Frequency Adaptation is ubiquitous in the central nervous
systems of diverse species. While adaptation has prominent
consequences for neuronal firing dynamics, it is often neglected in
the community when considering, for example, the dynamic nature of the
neural code, the propagation of synchrony in networks, or the function
of spike-time-based learning rules, probably due to the added
difficulty in accounting for it. We present a new and powerful tool
for treating Spike-Frequency Adaptation: an adapting continuous Markov
process. This adapting Markov process in two dimensions can be shown
to accurately capture the firing-rate dynamics and inter-spike interval
correlations of a spike-frequency adapting and relative refractory
conductance-based integrate-and-fire neuron driven by Poisson spike
trains.
[1] E. Muller, L. Buesing, J. Schemmel, K. Meier (2007). Spike-Frequency Adapting Neural Ensembles: Beyond Mean Adaptation and Renewal Theories. Neural Computation 19:2958-3010. |
|