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Synapses

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Dendritic computations captured by an effective point neuron model.

Proceedings of the National Academy of Sciences of the United States of America
Complex dendrites in general present formidable challenges to understanding neuronal information processing. To circumvent the difficulty, a prevalent viewpoint simplifies the neuronal morphology as a point representing the soma, and the excitatory a...

Robust Associative Learning Is Sufficient to Explain the Structural and Dynamical Properties of Local Cortical Circuits.

The Journal of neuroscience : the official journal of the Society for Neuroscience
The ability of neural networks to associate successive states of network activity lies at the basis of many cognitive functions. Hence, we hypothesized that many ubiquitous structural and dynamical properties of local cortical networks result from as...

Prototyping a memristive-based device to analyze neuronal excitability.

Biophysical chemistry
Many efforts have been spent in the last decade for the development of nanoscale synaptic devices integrated into neuromorphic circuits, trying to emulate the behavior of natural synapses. The study of brain properties with the standard approaches ba...

Learning cellular morphology with neural networks.

Nature communications
Reconstruction and annotation of volume electron microscopy data sets of brain tissue is challenging but can reveal invaluable information about neuronal circuits. Significant progress has recently been made in automated neuron reconstruction as well...

A Probabilistic Synapse With Strained MTJs for Spiking Neural Networks.

IEEE transactions on neural networks and learning systems
Spiking neural networks (SNNs) are of interest for applications for which conventional computing suffers from the nearly insurmountable memory-processor bottleneck. This paper presents a stochastic SNN architecture that is based on specialized logic-...

Short-term synaptic plasticity expands the operational range of long-term synaptic changes in neural networks.

Neural networks : the official journal of the International Neural Network Society
The brain is highly plastic, with synaptic weights changing across a wide range of time scales, from hundreds of milliseconds to days. Changes occurring at different temporal scales are believed to serve different purposes, with long-term changes for...

SynGO: An Evidence-Based, Expert-Curated Knowledge Base for the Synapse.

Neuron
Synapses are fundamental information-processing units of the brain, and synaptic dysregulation is central to many brain disorders ("synaptopathies"). However, systematic annotation of synaptic genes and ontology of synaptic processes are currently la...

Spatiotemporal discrimination in attractor networks with short-term synaptic plasticity.

Journal of computational neuroscience
We demonstrate that a randomly connected attractor network with dynamic synapses can discriminate between similar sequences containing multiple stimuli suggesting such networks provide a general basis for neural computations in the brain. The network...

Neural network model of an amphibian ventilatory central pattern generator.

Journal of computational neuroscience
The neuronal multiunit model presented here is a formal model of the central pattern generator (CPG) of the amphibian ventilatory neural network, inspired by experimental data from Pelophylax ridibundus. The kernel of the CPG consists of three pacema...

DoGNet: A deep architecture for synapse detection in multiplexed fluorescence images.

PLoS computational biology
Neuronal synapses transmit electrochemical signals between cells through the coordinated action of presynaptic vesicles, ion channels, scaffolding and adapter proteins, and membrane receptors. In situ structural characterization of numerous synaptic ...