Simple model of spiking neurons
IEEE Transactions on Neural Networks
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We present a minimal neuron model that captures the essence of the persistent firing behavior of interneurons as discovered recently in the field of Neuroscience. The mathematical model reproduces the phenomenon that slow integration in distal axon of interneurons on a timescale of tens of seconds to minutes, leads to persistent firing of axonal action potentials lasted for similar duration. In this model, we consider the axon as a slow leaky integrator, which is capable of dynamically switching the neuronal firing states between normal firing and persistent firing, through axonal computation. This model is based on the Izhikevich neuron model and includes additional equations and parameters to represent the persistent firing dynamics, making it computationally efficient yet bio-plausible, and thus well suitable for large scale spiking network simulations.