Stochastic resonance driven by time-modulated neurotransmitter random point trains

Effect of synaptic plasticity on sensory coding and steady-state filtering properties in the electric sense

Our modeling study examines short-term plasticity at the synapse between afferents from electroreceptors and pyramidal cells in the electrosensory lateral lobe (ELL) of the weakly electric fish Apteronotus leptorhynchus. It focusses on steady-state filtering and coherence-based coding properties. While developed for electroreception, our study exposes general functional features for different mixtures of depression and facilitation. Our computational model, constrained by the available in vivo and in vitro data, consists of a synapse onto a deterministic leaky integrate-and-fire (LIF) neuron. The synapse is either depressing (D), facilitating (F) or both (FD), and is driven by a sinusoidally or randomly modulated Poisson process. Due to nonlinearity, numerically computed input-output transfer functions are used to determine the filtering properties. The gain of the response at each sinusoidally modulated frequency is computed by dividing the fitted amplitudes of the input and output cycle histograms of the LIF models. While filtering is always low-pass for F alone, D alone exhibits a gain resonance (non-monotonicity) at a frequency that decreases with increasing recovery time constant of synaptic depression (tau(d)). This resonance is mitigated by the presence of F. For D, F and FD, coherence improves as the synaptic conductance time constant (tau(g)) increases, yet the mutual information per spike decreases. The information per spike for D and F follows opposite trends as their respective time constants increase. The broadband but non-monotonic gain and coherence functions seen in vivo suggest that D and perhaps FD dynamics are involved at this synapse. Our results further predict that the likely synaptic configuration is a slower tau(g), e.g. via a mixture of AMPA and NMDA synapses, and a relatively smaller synaptic facilitation time constant (tau(f)) and larger tau(d) (with tau(f) smaller than tau(d) and tau(g)). These results are compatible with known physiology.

Signal-to-noise ratio gain of a noisy neuron that transmits subthreshold periodic spike trains

The transmission properties of an integrate-and-fire neuron model that transmits coherent subthreshold spike trains in a shot noise environment are investigated by numerical simulation. For very weak coherent couplings, it is shown that the input-output signal-to-noise ratio (SNR) gain is easier to exceed unity; while for stronger coherent couplings it is difficult to observe the SNR gain larger than unity at the optimal noise intensity. These observations are different from those acquired in the case of continuous noise. Our analysis further suggests that the larger SNR gain in the very weak coherent coupling case should be due to the noise-induced resonance. It is also shown that there is more possibility of the SNR gain above unity for slower periodic spike trains transmitted by the model. The results may be useful in understanding the performance of real noisy neurons acting as signal-processing elements.