Dynamics and reliability of bistable neurons driven with time-dependent stimuli

The reliability of a spiking neuron depends on the frequency content of the driving input signal. Previous studies have shown that well above threshold, regularly firing neurons generate reliable responses when the input signal resonates with the firing frequency of the cell. Instead, well below threshold, reliable responses are obtained when the input frequency resonates with the subthreshold oscillations of the neuron. Previous theories, however, provide no clear prediction for the input frequency giving rise to maximally reliable spiking at threshold, which is probably the most relevant firing regime in mammalian cortex under physiological conditions. In particular, when the firing onset is governed by a subcritical Hopf bifurcation, the frequency of subthreshold oscillations often differs from the firing rate at threshold. The predictions of previous studies, hence, cannot be smoothly merged at threshold. Here we explore the behavior of reliability in bistable neurons near threshold using three types of driving stimuli: constant, periodic and stochastic. We find that the two natural frequencies of the system, associated to the two coexisting attractors, provide a rich variety of possible locking modes with the external signal. Reliability is determined by the sensitivity to noise of each locking mode, and also by the transition probabilities between modes. Noise increases the amount of spike-time jitter, and minimal jitter is obtained for input frequencies coinciding with the suprathreshold firing rate of the cell. In addition, noise may either enhance or inhibit transitions between the two attractors, depending on the input frequency. The dual role played by noise in bistable systems implies that reliability is determined by a delicate balance between spike-time jitter and the rate of transitions between attractors.Fil: Gonzalo Cogno, Ximena Soledad. Comisión Nacional de Energía Atomica. Gerencia del _rea de Investigaciones y Aplicaciones No Nucleares. Gerencia de Física (cab). División Física Estadistica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Schreiber, Susanne. Center for Computational Neuroscience, Humboldt Universit ̈at zu Berlin and Bernstein; AlemaniaFil: Samengo, Ines. Comisión Nacional de Energía Atomica. Gerencia del _rea de Investigaciones y Aplicaciones No Nucleares. Gerencia de Física (cab). División Física Estadistica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin