Temporal coding in vision: coding by the spike arrival times leads to oscillations in the case of moving targets

Spatiotemporally selective astrocytic ATP dynamics encode injury information sensed by microglia following brain injury in mice

Injuries to the brain result in tunable cell responses paired with stimulus properties, suggesting the existence of intrinsic processes that encode and transmit injury information; however, the molecular mechanism of injury information encoding is unclear. Here, using ATP fluorescent indicators, we identify injury-evoked spatiotemporally selective ATP dynamics, Inflares, in adult mice of both sexes. Inflares are actively released from astrocytes and act as the internal representations of injury. Inflares encode injury intensity and position at their population level through frequency changes and are further decoded by microglia, driving changes in their activation state. Mismatches between Inflares and injury severity lead to microglia dysfunction and worsening of injury outcome. Blocking Inflares in ischemic stroke in mice reduces secondary damage and improves recovery of function. Our results suggest that astrocytic ATP dynamics encode injury information and are sensed by microglia.

Contrast and Synthetic Multiexposure Fusion for Image Enhancement

Many hardware and software advancements have been made to improve image quality in smartphones, but unsuitable lighting conditions are still a significant impediment to image quality. To counter this problem, we present an image enhancement pipeline comprising synthetic multi-image exposure fusion and contrast enhancement robust to different lighting conditions. In this paper, we propose a novel technique of generating synthetic multi-exposure images by applying gamma correction to an input image using different values according to its luminosity for generating multiple intermediate images, which are then transformed into a final synthetic image by applying contrast enhancement. We observed that our proposed contrast enhancement technique focuses on specific regions of an image resulting in varying exposure, colors, and details for generating synthetic images. Visual and statistical analysis shows that our method performs better in various lighting scenarios and achieves better statistical naturalness and discrete entropy scores than state-of-the-art methods.

Surfing a spike wave down the ventral stream

Numerous theories of neural processing, often motivated by experimental observations, have explored the computational properties of neural codes based on the absolute or relative timing of spikes in spike trains. Spiking neuron models and theories however, as well as their experimental counterparts, have generally been limited to the simulation or observation of isolated neurons, isolated spike trains, or reduced neural populations. Such theories would therefore seem inappropriate to capture the properties of a neural code relying on temporal spike patterns distributed across large neuronal populations. Here we report a range of computer simulations and theoretical considerations that were designed to explore the possibilities of one such code and its relevance for visual processing. In a unified framework where the relation between stimulus saliency and spike relative timing plays the central role, we describe how the ventral stream of the visual system could process natural input scenes and extract meaningful information, both rapidly and reliably. The first wave of spikes generated in the retina in response to a visual stimulation carries information explicitly in its spatio-temporal structure: the most salient information is represented by the first spikes over the population. This spike wave, propagating through a hierarchy of visual areas, is regenerated at each processing stage, where its temporal structure can be modified by (i). the selectivity of the cortical neurons, (ii). lateral interactions and (iii). top-down attentional influences from higher order cortical areas. The resulting model could account for the remarkable efficiency and rapidity of processing observed in the primate visual system.

Precise burst synchrony in the superior colliculus of the awake cat during moving stimulus presentation

This study aimed to characterize the synchrony that occurs between cell discharges in the superior colliculus of the awake cat. We trained cats to perform a visual fixation in the presence of a visual moving stimulus and then recorded 686 pairs of neighboring cells in the superior colliculus during task performance. A new method to assess the significance of precise discharge synchronization is described, which permits analysis of nonstationary data. Of 181 pairs with sufficient data for quantitative analysis, 125 showed a cross-correlation histogram (CCH) with features assessed as significant using this approach. CCHs frequently showed an isolated central peak (41 of 125) or a peak flanked by one or two troughs (68 of 125), and in a few cases an oscillatory pattern of approximately 65 Hz (16 of 125). This is in contrast to the oscillation frequency reported for the visual cortex and shows that oscillations in the superior colliculus probably arise from a cortex-independent mechanism. Our method also permits direct quantification of the correlation shift predictors, assessing precise time locking of spikes to the stimulus. Only 1 of 125 cross-correlation shift predictors had a significant central peak, meaning that most of the CCH features were not related to cell discharges time-locked to the stimulus presentation. Further investigation using a burst-jittering method showed that synchrony in the superior colliculus is attributable to precise synchronization of short bursts of spikes. Such synchrony could be related to the network dynamics and the common inhibitory feedback from local interneurons, which would act as temporal selectors of the cells with greatest or fastest response.

Adapting a feedforward heteroassociative network to Hodgkin-Huxley dynamics

Using the original McCulloch-Pitts notion of simple on and off spike coding in lieu of rate coding, an Anderson-Kohonen artificial neural network (ANN) associative memory model was ported to a neuronal network with Hodgkin-Huxley dynamics. In the ANN, the use of 0/1 (no-spike/spike) units introduced a cross-talk term that had to be compensated by introducing balanced feedforward inhibition. The resulting ANN showed good capacity and fair selectivity (rejection of unknown input vectors). Translation to the Hodgkin-Huxley model resulted in a network that was functional but not at all robust. Evaluation of the weaknesses of this network revealed that it functioned far better using spike timing, rather than spike occurrence, as the code. The algorithm requires a novel learning algorithm for feedforward inhibition that could be sought physiologically.

Continuum models in neurobiology and information processing

Continuum models have been used with considerable success for single neurons but have been neglected in the study of neuronal populations. In more popular discrete neuronal network models, the geometric details of the neuronal centers are usually neglected. We here give a continuum nonlinear dynamical model and an approximate model which admits the possibility of ascertaining the roles of the various connectivity patterns from center to center in the central nervous system. Frequency transfer characteristics are used to incorporate the nonlinear dynamics of single neurons. Simple examples are evaluated both analytically and numerically and the results presented graphically.

Analytical and simulation results for stochastic Fitzhugh-Nagumo neurons and neural networks

An analytical approach is presented for determining the response of a neuron or of the activity in a network of connected neurons, represented by systems of nonlinear ordinary stochastic differential equations--the Fitzhugh-Nagumo system with Gaussian white noise current. For a single neuron, five equations hold for the first- and second-order central moments of the voltage and recovery variables. From this system we obtain, under certain assumptions, five differential equations for the means, variances, and covariance of the two components. One may use these quantities to estimate the probability that a neuron is emitting an action potential at any given time. The differential equations are solved by numerical methods. We also perform simulations on the stochastic Fitzugh-Nagumo system and compare the results with those obtained from the differential equations for both sustained and intermittent deterministic current inputs with superimposed noise. For intermittent currents, which mimic synaptic input, the agreement between the analytical and simulation results for the moments is excellent. For sustained input, the analytical approximations perform well for small noise as there is excellent agreement for the moments. In addition, the probability that a neuron is spiking as obtained from the empirical distribution of the potential in the simulations gives a result almost identical to that obtained using the analytical approach. However, when there is sustained large-amplitude noise, the analytical method is only accurate for short time intervals. Using the simulation method, we study the distribution of the interspike interval directly from simulated sample paths. We confirm that noise extends the range of input currents over which (nonperiodic) spike trains may exist and investigate the dependence of such firing on the magnitude of the mean input current and the noise amplitude. For networks we find the differential equations for the means, variances, and covariances of the voltage and recovery variables and show how solving them leads to an expression for the probability that a given neuron, or given set of neurons, is firing at time t. Using such expressions one may implement dynamical rules for changing synaptic strengths directly without sampling. The present analytical method applies equally well to temporally nonhomogeneous input currents and is expected to be useful for computational studies of information processing in various nervous system centers.

Synchronous oscillatory activity in sensory systems: new vistas on mechanisms

The origin and nature, as well as the functional role, of synchronous oscillatory activity in the cortex are among the major unresolved issues in systems neurobiology. Recent advances in understanding the mechanisms underlying oscillations include the description of intrinsically bursting pyramidal cells in striate cortex in vivo and the discovery of inhibitory interneurons that fire spike doublets to induce synchrony. The behavioral consequences of coordinated activity in cortical neurons remain poorly understood.