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Synaptic Transmission

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Using intersection information to map stimulus information transfer within neural networks.

Bio Systems
Analytical tools that estimate the directed information flow between simultaneously recorded neural populations, such as directed information or Granger causality, typically focus on measuring how much information is exchanged between such population...

Constructing multilayered neural networks with sparse, data-driven connectivity using biologically-inspired, complementary, homeostatic mechanisms.

Neural networks : the official journal of the International Neural Network Society
The immense complexity of the brain requires that it be built and controlled by intrinsic, self-regulating mechanisms. One such mechanism, the formation of new connections via synaptogenesis, plays a central role in neuronal connectivity and, ultimat...

Reinforcement Learning in Spiking Neural Networks with Stochastic and Deterministic Synapses.

Neural computation
Though succeeding in solving various learning tasks, most existing reinforcement learning (RL) models have failed to take into account the complexity of synaptic plasticity in the neural system. Models implementing reinforcement learning with spiking...

Reducing variability in motor cortex activity at a resting state by extracellular GABA for reliable perceptual decision-making.

Journal of computational neuroscience
Interaction between sensory and motor cortices is crucial for perceptual decision-making, in which intracortical inhibition might have an important role. We simulated a neural network model consisting of a sensory network (N) and a motor network (N) ...

A computational model of systems memory consolidation and reconsolidation.

Hippocampus
In the mammalian brain, newly acquired memories depend on the hippocampus (HPC) for maintenance and recall, but over time, the neocortex takes over these functions, rendering memories HPC-independent. The process responsible for this transformation i...

Biological batch normalisation: How intrinsic plasticity improves learning in deep neural networks.

PloS one
In this work, we present a local intrinsic rule that we developed, dubbed IP, inspired by the Infomax rule. Like Infomax, this rule works by controlling the gain and bias of a neuron to regulate its rate of fire. We discuss the biological plausibilit...

Neuronal mechanisms for sequential activation of memory items: Dynamics and reliability.

PloS one
In this article we present a biologically inspired model of activation of memory items in a sequence. Our model produces two types of sequences, corresponding to two different types of cerebral functions: activation of regular or irregular sequences....

Competitive Learning in a Spiking Neural Network: Towards an Intelligent Pattern Classifier.

Sensors (Basel, Switzerland)
One of the modern trends in the design of human-machine interfaces (HMI) is to involve the so called spiking neuron networks (SNNs) in signal processing. The SNNs can be trained by simple and efficient biologically inspired algorithms. In particular,...

A Mean-Field Description of Bursting Dynamics in Spiking Neural Networks with Short-Term Adaptation.

Neural computation
Bursting plays an important role in neural communication. At the population level, macroscopic bursting has been identified in populations of neurons that do not express intrinsic bursting mechanisms. For the analysis of phase transitions between bur...

Long-Tailed Characteristic of Spiking Pattern Alternation Induced by Log-Normal Excitatory Synaptic Distribution.

IEEE transactions on neural networks and learning systems
Studies of structural connectivity at the synaptic level show that in synaptic connections of the cerebral cortex, the excitatory postsynaptic potential (EPSP) in most synapses exhibits sub-mV values, while a small number of synapses exhibit large EP...