Selective attention is an important filter for complex environments where distractions compete with signals. Attention increases both the gamma-band power of cortical local field potentials and the spike-field coherence within the receptive field of an attended object. However, the mechanisms by which gamma-band activity enhances, if at all, the encoding of input signals are not well understood...

We assess both numerically and theoretically how external noise current affect the behavior of Leaky Integrate and Fire model. Integrate and fire model works better in asynchronous inputs environment in case of signal to noise ratio of efferent spike trains. External noise and sub threshold stimulus are subject to all the neurons. Response of the models is totally different to correlated inputs...

Compelling behavioral evidence suggests that humans can make optimal decisions despite the uncertainty inherent in perceptual or motor tasks. A key question in neuroscience is how populations of spiking neurons can implement such probabilistic computations. In this article, we develop a comprehensive framework for optimal, spike-based sensory integration and working memory in a dynamic environm...

Generation of detailed biophysical data through standardized, systematic experimental methods facilitates the creation of computational models that simulate or predict cell behavior. The data created as part of the Allen Cell Types Database can be used in multiple different types of models and simulations. For simulations of neural networks, there is a tradeoff between the size of the network t...

Prediction of barley yield is an attempt to accurately forecast the outcome of a specific situation, using as input information extracted from a set of data features that potentially describe the situation. In this study, an attempt has been made to analyze and compare multiple linear regression (MLR), and artificial neural network (ANN) including multi-layer p erceptron (MLP) and r adial basis...

A necessary ingredient for a quantitative theory of neural coding is appropriate "spike kinematics": a precise description of spike trains. While summarizing experiments by complete spike time collections is clearly inefficient and probably unnecessary, the most common probabilistic model used in neurophysiology, the inhomogeneous Poisson process, often seems too crude. Recently a more general ...

For a slowly varying stimulus, the simplest relationship between a neuron's input and output is a rate code, in which the spike rate is a unique function of the stimulus at that instant. In the case of spike-rate adaptation, there is no unique relationship between input and output, because the spike rate at any time depends both on the instantaneous stimulus and on prior spiking (the "history")...

Precise spike timing is considered to play a fundamental role in communications and signal processing in biological neural networks. Understanding the mechanism of spike timing adjustment would deepen our understanding of biological systems and enable advanced engineering applications such as efficient computational architectures. However, the biological mechanisms that adjust and maintain spik...

We present several variations of a model of gain control in the retina of the toad Bufo marinus, and use them to fit the threshold-vs-intensity data of an actual toad ganglion cell [Donner et al. (1990). Journal of General Physiology, 95, 733-753]. Our models are based on a proposal by Donner et al. that the gain (neural spike per photon ratio) of toad ganglion cells is set by a sequence of two...