Computing the Optimally Fitted Spike Train for a Synapse

Neural Computation 13, 2477–2494 (2001)Experimental data have shown that synapses are heterogeneous: different
synapses respond with different sequences of amplitudes of postsynaptic
responses to the same spike train. Neither the role of synaptic dynamics
itself nor the role of the heterogeneity of synaptic dynamics for computations
in neural circuits is well understood. We present in this article
two computational methods that make it feasible to compute for a given
synapse with known synaptic parameters the spike train that is optimally
fitted to the synapse in a certain sense. With the help of these methods,
one can compute, for example, the temporal pattern of a spike train (with
a given number of spikes) that produces the largest sum of postsynaptic
responses for a specific synapse. Several other applications are also
discussed. To our surprise, we find that most of these optimally fitted
spike trains match common firing patterns of specific types of neurons
that are discussed in the literature. Hence, our analysis provides a possible
functional explanation for the experimentally observed regularity
in the combination of specific types of synapses with specific types of
neurons in neural circuits.