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Synapses

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Application of Deep Compression Technique in Spiking Neural Network Chip.

IEEE transactions on biomedical circuits and systems
In this paper, a reconfigurable and scalable spiking neural network processor, containing 192 neurons and 6144 synapses, is developed. By using deep compression technique in spiking neural network chip, the amount of physical synapses can be reduced ...

Perceptrons from memristors.

Neural networks : the official journal of the International Neural Network Society
Memristors, resistors with memory whose outputs depend on the history of their inputs, have been used with success in neuromorphic architectures, particularly as synapses and non-volatile memories. However, to the best of our knowledge, no model for ...

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...

Stable memory with unstable synapses.

Nature communications
What is the physiological basis of long-term memory? The prevailing view in Neuroscience attributes changes in synaptic efficacy to memory acquisition, implying that stable memories correspond to stable connectivity patterns. However, an increasing b...

Flexible Neuromorphic Electronics for Computing, Soft Robotics, and Neuroprosthetics.

Advanced materials (Deerfield Beach, Fla.)
Flexible neuromorphic electronics that emulate biological neuronal systems constitute a promising candidate for next-generation wearable computing, soft robotics, and neuroprosthetics. For realization, with the achievement of simple synaptic behavior...

Mutual Inhibition with Few Inhibitory Cells via Nonlinear Inhibitory Synaptic Interaction.

Neural computation
In computational neural network models, neurons are usually allowed to excite some and inhibit other neurons, depending on the weight of their synaptic connections. The traditional way to transform such networks into networks that obey Dale's law (i....

A comprehensive knowledge base of synaptic electrophysiology in the rodent hippocampal formation.

Hippocampus
The cellular and synaptic architecture of the rodent hippocampus has been described in thousands of peer-reviewed publications. However, no human- or machine-readable public catalog of synaptic electrophysiology data exists for this or any other neur...

A Mechanism for Synaptic Copy Between Neural Circuits.

Neural computation
Cortical oscillations are central to information transfer in neural systems. Significant evidence supports the idea that coincident spike input can allow the neural threshold to be overcome and spikes to be propagated downstream in a circuit. Thus, a...

Artificial Sensory Memory.

Advanced materials (Deerfield Beach, Fla.)
Sensory memory, formed at the beginning while perceiving and interacting with the environment, is considered a primary source of intelligence. Transferring such biological concepts into electronic implementation aims at achieving perceptual intellige...

Sensitivity to Stimulus Irregularity Is Inherent in Neural Networks.

Neural computation
Behavior is controlled by complex neural networks in which neurons process thousands of inputs. However, even short spike trains evoked in a single cortical neuron were demonstrated to be sufficient to influence behavior in vivo. Specifically, irregu...