Computational NeuroscienceSpike, rate, field, and hybrid methods for treating neuronal dynamics and interactions

Spike-, rate-, and field-based approaches to neural dynamics are adapted and hybridized to provide new methods of analyzing dynamics of single neurons and large neuronal systems, to elucidate the relationships and intermediate forms between these limiting cases, and to enable faster simulations with reduced memory requirements. At the single-neuron level, the new approaches involve reformulation of dynamics in synapses, dendrites, cell bodies, and axons to enable new types of analysis, longer numerical timesteps, and demonstration that rate-based methods can predict spike times. In multineuron systems, hybrids and intermediates between spike-based and field-based coupling between neurons are used to provide stepping stones between descriptions based on pairwise spike-based interactions between neurons and ones based on neural field-based interactions within and between populations, including arbitrary spatial structure and temporal delays in the connections in general. In particular, a new neuron-in-cell approach is introduced that is a hybrid between neural field theory and spiking-neuron models in analogy to particle-in-cell methods in plasma physics. This approach enables large speedups in computations while preserving spike shapes and times. Various approaches are illustrated numerically for specific cases.

► Spike, rate, field, and hybrid methods for neural calculations are investigated. ► New links between existing methods are found, and new methods are proposed that are faster than existing ones in many cases. ► Bounds of validity of methods are found. ► Numerical illustrations are provided. ► This work will enable faster neural computations in multiple situations.