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This work demonstrates that CMOS analog spiking neurons exhibit enhanced spike timing reliability under time-varying stimuli compared to constant excitation, mirroring biological neuron behavior. By leveraging a physics-based transient noise simulation framework, the researchers show that constant current leads to accumulated phase noise and jitter, while time-varying input triggers spikes via a controlled state-transition mechanism, significantly reducing jitter. This finding is further supported by a stochastic thermodynamic uncertainty relation, highlighting the inherent reliability-dissipation trade-off in oscillatory or rate-coding regimes.
Analog spiking neurons achieve superior spike timing precision with time-varying inputs, overcoming the inherent jitter limitations of constant excitation.
A spiking neuron is a dynamical system exhibiting a limit cycle (a spike) when subject to sufficiently excitatory input. Famous neuroscience experiments by Bryant and Segundo as well as Mainen and Sejnowski revealed that the spike times of some biological neurons are more reliable when subject to time-varying stimuli compared to constant ones. Provided with an industrial physics-based transient noise SPICE simulation framework compatible with foundry transistor compact models, we have demonstrated similar behaviours for a CMOS analog spiking neuron designed for neuromorphic computing hardware platforms. Under constant excitation current, the neuron operates in oscillatory regime and the empirical periods (interspike intervals) suffer from accumulated phase noise (jitter) during successive cycles, mainly due to the thermal noise of transistors. A time-varying input is reported to be capable of triggering output spikes according to a controlled statetransition mechanism, thereby significantly reducing the spike time jitter in the excitability regime. The physical limitation of the oscillatory or rate-coding regime in terms of reliabilitydissipation tradeoff is further evidenced by a stochastic thermodynamic uncertainty relation, a theorem that applies to general overdamped dissipative stochastic dynamical systems.