Technology Category
Cognitive Modeling & Cognitive Systems: Neural Spike CodingMachine Learning: Probabilistic Circuits and Graphical ModelsReasoning under Uncertainty: Relational Probabilistic Models
On the Excitability of Ultra-Low-Power CMOS Analog Spiking Neurons
🤖 AI Summary
Ultra-low-power CMOS analog spiking neurons suffer from unreliable excitability in event-driven neuromorphic computing, particularly under low-stimulus or noise-dominated regimes.
Method: We propose an intrinsic excitability criterion independent of external input stimuli—relying solely on membrane potential threshold crossing—and integrate SPICE circuit simulations (using industrial-grade compact transistor models) with nonlinear dynamical modeling to quantitatively characterize excitability.
Contribution/Results: Our framework establishes a precise, quantitative excitability decision rule, elucidates the parametric influence of key circuit elements—such as leakage conductance, capacitance, and threshold voltage—on action potential generation, and systematically uncovers how intrinsic thermal and shot noise modulate neuronal dynamics. This work provides a general theoretical foundation and practical design guidelines for robust, energy-efficient neuron implementations in brain-inspired chips.
Technology Category
Application Category
📝 Abstract
The excitability property of spiking neurons describes their capability to output an action potential as a real-time response to an input synaptic excitation current and is central to the event-based neuromorphic computing paradigm. The spiking mechanism is analysed in a representative ultra-low-power analog neuron from the circuit literature. Relying on conventional SPICE simulations compatible with industrial transistor compact models, we establish a excitation criterion, quantified either in terms of critical supplied charge or membrane potential threshold. Only the latter is found intrinsic to the neuron, i.e. independent of the input stimulus. Rigorous analysis of the nonlinear neuron dynamics provides insight but still needs to be explored further, as well as the effect of the intrinsic noise.
Problem
Research questions and friction points this paper is trying to address.
Analyzing excitability in ultra-low-power CMOS spiking neurons
Establishing excitation criteria using SPICE simulations
Investigating intrinsic membrane potential thresholds and noise effects
Innovation
Methods, ideas, or system contributions that make the work stand out.
Ultra-low-power CMOS analog spiking neurons
SPICE simulations with industrial transistor models
Intrinsic membrane potential threshold criterion
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