🤖 AI Summary
Conventional non-bursting neuron models fail to reproduce intrinsic bursting dynamics, hindering biologically plausible and hardware-efficient neuromorphic implementations.
Method: We propose “neuronal burst anatomy”—a design methodology grounded in dynamical systems theory—using the $I_{ ext{Na,p}} + I_{ ext{K}} + I_{ ext{K(M)}}$ model as a qualitative reference. It identifies bifurcation types at burst initiation and termination, and leverages fast-subsystem nullclines and bifurcation diagrams to guide circuit synthesis.
Contribution/Results: Two minimal MOSFET-based circuits are implemented, autonomously generating biologically realistic bursting patterns—including square-wave and mixed-type bursts—without external modulation. The approach preserves neurobiological plausibility while drastically reducing component count and enhancing behavioral predictability. Experimental validation confirms robust, low-power operation and interpretable dynamics, establishing a new paradigm for energy-efficient, explainable neuromorphic hardware.
📝 Abstract
This work introduces a novel methodology for designing biologically plausible bursting neuron circuits using a minimal number of components. We hypothesize that to design circuits capable of bursting, the neuron circuit design must mimic a neuron model that inherently exhibits bursting dynamics. Consequently, classical models such as the Hodgkin-Huxley, $I_{Na,p}+I_{K}$, and FitzHugh-Nagumo models are not suitable choices. Instead, we propose a methodology for designing neuron circuits that emulate the qualitative characteristics of the $I_{Na,p}+I_{K}+I_{K(M)}$ model, a well-established minimal bursting neuron model. Based on this methodology, we present two novel MOSFET-based circuits that exhibit bursting. Using the method of dissection of neural bursting, we demonstrate that the nullcline and bifurcation diagrams of the fast subsystem in our circuits are qualitatively equivalent to those of the $I_{Na,p}+I_{K}+I_{K(M)}$ model. Furthermore, we examine the effect of the type of bifurcation at burst initiation and termination on the bursting characteristics, showing that our circuits can exhibit diverse bursting behaviours. Importantly, the main contribution of this work lies not in the specific circuit implementation, but in the methodology proposed for constructing bursting neuron circuits.