We investigate a novel class of neural stochastic resonance (SR) exhibiting error-free information transfer. Unlike conventional neural SR, where the decrease of a system's response with too much noise is associated with an increase in the baseline firing rate, here the bell-shaped SR behavior of the input-output cross correlation emerges versus increasing input noise in spite of no significant...

Although gamma frequency oscillations are common in the brain, their functional contributions to neural computation are not understood. Here we report in vitro electrophysiological recordings to evaluate how noisy gamma frequency oscillatory input interacts with the overall activation level of a neuron to determine the precise timing of its action potentials. The experiments were designed to ev...

Synchronous firing peaks at levels greatly exceeding background activity have recently been reported in neocortical tissue. A small subset of neurons is dominant in a large fraction of the peaks. To investigate whether this striking behavior can emerge from a simple model, we constructed and studied a model neural network that uses a modified Hopfield-type dynamical rule. We find that networks ...

Hippocampal neural systems consolidate multiple complex behaviors into memory. However, the temporal structure of neural firing supporting complex memory consolidation is unknown. Replay of hippocampal place cells during sleep supports the view that a simple repetitive behavior modifies sleep firing dynamics, but does not explain how multiple episodes could be integrated into associative networ...

Antagonists of N-methyl-D-aspartate receptors (NMDAR) have psychotomimetic effects in humans and are used to model schizophrenia in animals. We used high-density electrophysiological recordings to assess the effects of acute systemic injection of an NMDAR antagonist (MK-801) on ensemble neural processing in the medial prefrontal cortex of freely moving rats. Although MK-801 increased neuron fir...

Population rate or activity equations are the foundation of a common approach to modeling for neural networks. These equations provide mean field dynamics for the firing rate or activity of neurons within a network given some connectivity. The shortcoming of these equations is that they take into account only the average firing rate while leaving out higher order statistics like correlations be...

Studies of the neural correlates of short-term memory in a wide variety of brain areas have found that transient inputs can cause persistent changes in rates of action potential firing, through a mechanism that remains unknown. In a premotor area that is responsible for holding the eyes still during fixation, persistent neural firing encodes the angular position of the eyes in a characteristic ...

The artillery firing precision plays an important role in the war and it’s hard to describe the projectile trajectory in a mathematical model. In this paper, the neural network is used to build the artillery ballistic model for range prediction and Particle Swarm Optimization (PSO) is applied to optimize the initial weight and bias to accelerate the training speed. Besides, some firing data fro...

Memory and learning are believed to be neurally based on a general class of processes known as synaptic plasticity (Kandel et al., 2000). Working memory, however, is arguably too fast to rely solely on synaptic weighting and must instead take place as an active neural process (O’Reilly & Manukata, 2000). I propose that active processes, which can account for both simple stimulus based memories ...

Population rate or activity equations are the foundation of a common approach to modeling for neural networks. These equations provide mean field dynamics for the firing rate or activity of neurons within a network given some connectivity. The shortcoming of these equations is that they take into account only the average firing rate, while leaving out higher-order statistics like correlations b...