No abstract

Spike trains commonly exhibit interspike interval (ISI) correlations caused by spike-activated adaptation currents. Here we investigate how the dynamics of adaptation currents can represent spike pattern information generated from stimulus inputs. By analyzing dynamical models of stimulus-driven single neurons, we show that the activation states of the correlation-inducing adaptation current are themselves statistically independent from spike to spike. This paradoxical finding suggests a biophysically plausible means of information representation. We show that adaptation independence is elicited by input levels that produce regular, non-Poisson spiking. This adaptation-independent regime is advantageous for sensory processing because it does not require sensory inferences on the basis of multivariate conditional probabilities, reducing the computational cost of decoding. Furthermore, if the kinetics of postsynaptic activation are similar to the adaptation, the activation state information can be communicated postsynaptically with no information loss, leading to an experimental prediction that simple synaptic kinetics can decorrelate the correlated ISI sequence. The adaptation-independence regime may underly efficient weak signal detection by sensory afferents that are known to exhibit intrinsic correlated spiking, thus efficiently encoding stimulus information at the limit of physical resolution.neural correlations | spike train information N egative-feedback processes are ubiquitous in biological systems. In neurons, spike-triggered adaptation processes inhibit subsequent spiking and can produce temporal correlations in the spike train, in which a longer than average interspike interval (ISI) makes a shorter subsequent ISI more likely and vice versa. Such correlations are common in neurons (1-7) and model neurons (8-14). The impact of temporal relationships in spike trains, including ISI correlations, on neural information processing has been under intense investigation (1,2,(15)(16)(17)(18)(19)(20)(21)(22). Temporally correlated spike trains pose a challenge for ISI-based sensory inferences because each ISI depends on both present and past stimulus activity, requiring complex strategies to make accurate inferences (23,24). Some ISI correlations can be attributed to long-time-scale autocorrelations of the input (23, 25) or correlations related to long-time-scale adaptive responses (23, 26). However, if the ISI correlations arise from inputs or noise with fast autocorrelation time scales relative to both the mean ISI and the adaptation time scales of the cell, the resulting fine-grained time structure of spike trains can shape information processing of the underlying slower input fluctuations (2,3,11,15,18,19,(27)(28)(29). Specifically, Jacobs et al. (29) have recently shown that fine-grained temporal coding of ISIs that takes into account adaptive responses (e.g., refractoriness) not only conveys significant information, but the information benefit accounts for behaviorally important levels of stimulus discrimi...

We study an excitatory all-to-all coupled network of N spiking neurons with synaptically filtered background noise and slow activity-dependent hyperpolarization currents. Such a system exhibits noise-induced burst oscillations over a range of values of the noise strength (variance) and level of cell excitability. Since both of these quantities depend on the rate of background synaptic inputs, we show how noise can provide a mechanism for increasing the robustness of rhythmic bursting and the range of burst frequencies. By exploiting a separation of time scales we also show how the system dynamics can be reduced to low-dimensional mean field equations in the limit N --> infinity. Analysis of the bifurcation structure of the mean field equations provides insights into the dynamical mechanisms for initiating and terminating the bursts.

Short-term depression (STD) is observed at many synapses of the CNS and is important for diverse computations. We have discovered a form of fast STD (FSTD) in the synaptic responses of pyramidal cells evoked by stimulation of their electrosensory afferent fibers (P-units). The dynamics of the FSTD are matched to the mean and variance of natural P-unit discharge. FSTD exhibits switch-like behavior in that it is immediately activated with stimulus intervals near the mean interspike interval (ISI) of P-units (approximately 5 ms) and recovers immediately after stimulation with the slightly longer intervals (>7.5 ms) that also occur during P-unit natural and evoked discharge patterns. Remarkably, the magnitude of evoked excitatory postsynaptic potentials appear to depend only on the duration of the previous ISI. Our theoretical analysis suggests that FSTD can serve as a mechanism for noise reduction. Because the kinetics of depression are as fast as the natural spike statistics, this role is distinct from previously ascribed functional roles of STD in gain modulation, synchrony detection or as a temporal filter.

We investigate oscillation regularity of a noise-driven system modeled with a slow after-hyperpolarizing adaptation current (AHP) composed of multiple-exponential relaxation time scales. Sufficiently separated slow and fast AHP time scales (biphasic decay) cause a peak in oscillation irregularity for intermediate input currents I, with relatively regular oscillations for small and large currents. An analytic formulation of the system as a stochastic escape problem establishes that the phenomena is distinct from standard forms of coherence resonance. Our results explain data on the oscillation regularity of the pre-Bötzinger complex, a neural oscillator responsible for inspiratory breathing rhythm generation in mammals.