Artificial neuron with somatic and axonal computation units: Mathematical and neuromorphic models of persistent firing neurons

The conventional view of axon as a transmission cable has been challenged by continuous progress in neuroscience discoveries, which indicate the rich functional and computational repertoire of the axon. Recent experimental findings of slow integration induced persistent firing in distal axons of interneurons have shown that the slow integration from tens of seconds to minutes in distal axon leads to persistent firing of action potentials lasting for similar duration, suggesting that the axon performs its own integration functions. In this paper, we present an artificial neuron model including both somatic and axonal computation units, which reproduces the neural behavior of persistent firing. Complementary to the classic somatic computational unit which evokes action potentials by integrating dendritic inputs in a short timescale, the axon integrates the soma evoked spikes in a longer timescale of tens of second to minutes. Consequently the persistent firing behavior of the axon is determined through toggling the axon dynamics between passive conduction mode and persistent firing mode based on the integrated axonal potential. We present and discuss in this work the mathematical and neuromorphic models of the artificial neuron, as well as their simulation results. The artificial neuron proposed, being computationally efficient yet bio-plausible, would be useful to construct and simulate the large scale models of animal or human cortex, which provides a neuromorphic platform for further investigation of the possible functions of persistent firing and their roles in animal and human brain, especially their correlations with working memory.

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