Computing the optimally fitted spike train for a synapse

Experimental data have shown that synapses are heterogeneous: different syn apses respond with different sequences of amplitudes of postsynaptic respon ses to the same spike train. Neither the role of synaptic dynamics itself n or the role of the heterogeneity of synaptic dynamics for computations in n eural circuits is well understood. We present in this article two computati onal methods that make it feasible to compute for a given synapse with know n synaptic parameters the spike train that is optimally fitted to the synap se 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 spi kes) that produces the largest sum of postsynaptic responses for a specific synapse. Several other applications are also discussed. To our surprise, w e 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 ex perimentally observed regularity in the combination of specific types of sy napses with specific types of neurons in neural circuits.