We study the encoding of weak signals in spike trains with interspike interval (ISI) correlations and the signals' subsequent detection in sensory neurons. Motivated by the observation of negative ISI correlations in auditory and electrosensory afferents, we assess the theoretical performance limits of an individual detector neuron receiving a weak signal distributed across multiple afferent inputs. We assess the functional role of ISI correlations in the detection process using statistical detection theory and derive two sequential likelihood ratio detector models: one for afferents with renewal statistics; the other for afferents with negatively correlated ISIs. We suggest a mechanism that might enable sensory neurons to implicitly compute conditional probabilities of presynaptic spikes by means of short-term synaptic plasticity. We demonstrate how this mechanism can enhance a postsynaptic neuron's sensitivity to weak signals by exploiting the correlation structure of the input spike trains. Our model not only captures fundamental aspects of early electrosensory signal processing in weakly electric fish, but may also bear relevance to the mammalian auditory system and other sensory modalities.

A simple model of spike generation is described that gives rise to negative correlations in the interspike interval (ISI) sequence and leads to long-term spike train regularization. This regularization can be seen by examining the variance of the kth-order interval distribution for large k (the times between spike i and spike i + k). The variance is much smaller than would be expected if successive ISIs were uncorrelated. Such regularizing effects have been observed in the spike trains of electrosensory afferent nerve fibers and can lead to dramatic improvement in the detectability of weak signals encoded in the spike train data (Ratnam & Nelson, 2000). Here, we present a simple neural model in which negative ISI correlations and long-term spike train regularization arise from refractory effects associated with a dynamic spike threshold. Our model is derived from a more detailed model of electrosensory afferent dynamics developed recently by other investigators (Chacron, Longtin, St.-Hilaire, & Maler, 2000;Chacron, Longtin, & Maler, 2001). The core of this model is a dynamic spike threshold that is transiently elevated following a spike and subsequently decays until the next spike is generated. Here, we present a simplified version-the linear adaptive threshold model-that contains a single state variable and three free parameters that control the mean and coefficient of variation of the spontaneous ISI distribution and the frequency characteristics of the driven response. We show that refractory effects associated with the dynamic threshold lead to regularization of the spike train on long timescales. Furthermore, we show that this regularization enhances the detectability of weak signals encoded by the linear adaptive threshold model. Although inspired by properties of electrosensory afferent nerve fibers, such regularizing effects may play an important role in other neural systems where weak signals must be reliably detected in noisy spike trains. When modeling a neuronal system that exhibits this type of ISI correlation structure, the linear adaptive threshold model may provide a more appropriate starting point than conventional renewal process models that lack long-term regularizing effects.

Many implementations of adaptive signal processing in the nervous system are likely to require a mechanism for gain control at the single neuron level. To properly adjust the gain of an individual neuron, it may be necessary to use information carried by neurons in other parts of the system. The ability to adjust the gain of neurons in one part of the brain, using control signals arising from another, has been observed in the electrosensory system of weakly electric fish, where descending pathways to a first-order sensory nucleus have been shown to influence the gain of its output neurons. Although the neural circuitry associated with this system is well studied, the exact nature of the gain control mechanism is not fully understood. In this paper, we propose a mechanism based on the regulation of total membrane conductance via synaptic activity on descending pathways. Using a simple neural model, we show how the activity levels of paired excitatory and inhibitory control pathways can regulate the gain and baseline excitation of a target neuron.

Head movements in vertebrates give rise to involuntary eye movements that stabilize visual images on the retina. Previous models of the vestibulo-ocular reflex (VOR), one of the neural mechanisms responsible for stabilizing the eyes during head movements, have assumed that the VOR transfer function should have unity gain and 180° phase shift. Experimental measurements of VOR gain and phase, however, exhibit frequency dependencies that are not easily interpreted within the framework of existing models. We reanalyze the problem of VOR control using stochastic optimal estimation theory and show that VOR dynamics, in general, should differ from the "ideal" unity-gain, 180° phase shift transfer function. We illustrate this approach by computing the optimal VOR transfer function for a simple, second-order dynamical model of a head–neck system. Despite its simplicity, this model is able to give some insight into the dynamical properties of the VOR. In particular, it qualitatively reproduces an experimentally observed gain peak in monkey VOR at high frequencies. The model also predicts that the gain and phase characteristics of the optimal VOR transfer function should depend on the spectrum of natural head movements, possibly giving rise to species-dependent and gait-dependent differences in the VOR transfer function. We suggest that the applicability of optimal estimation extends beyond the control of compensatory eye movements and that it is probably a universal component of movement control in the nervous system.