  Inverse stochastic resonance (ISR) is a phenomenon where noise reduces rather
than increases the firing rate of a neuron, sometimes leading to complete
quiescence. ISR was first experimentally verified with cerebellar Purkinje
neurons. These experiments showed that ISR enables optimal information transfer
between the input and output spike train of neurons. Subsequent studies
demonstrated the efficiency of information processing and transfer in neural
networks with small-world topology. We conducted a numerical investigation into
the impact of adaptivity on ISR in a small-world network of noisy
FitzHugh-Nagumo (FHN) neurons, operating in a bistable regime with a stable
fixed point and a limit cycle -- a prerequisite for ISR. Our results show that
the degree of ISR is highly dependent on the FHN model's timescale separation
parameter $\epsilon$. The network structure undergoes dynamic adaptation via
mechanisms of either spike-time-dependent plasticity (STDP) with
potentiation-/depression-domination parameter $P$, or homeostatic structural
plasticity (HSP) with rewiring frequency $F$. We demonstrate that both STDP and
HSP amplify ISR when $\epsilon$ lies within the bistability region of FHN
neurons. Specifically, at larger values of $\epsilon$ within the bistability
regime, higher rewiring frequencies $F$ enhance ISR at intermediate (weak)
synaptic noise intensities, while values of $P$ consistent with
depression-domination (potentiation-domination) enhance (deteriorate) ISR.
Moreover, although STDP and HSP parameters may jointly enhance ISR, $P$ has a
greater impact on ISR compared to $F$. Our findings inform future ISR
enhancement strategies in noisy artificial neural circuits, aiming to optimize
information transfer between input and output spike trains in neuromorphic
systems, and prompt venues for experiments in neural networks.
