AIMC Topic: Neurons

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BrainS: Customized multi-core embedded multiple scale neuromorphic system.

Neural networks : the official journal of the International Neural Network Society
Research on modeling and mechanisms of the brain remains the most urgent and challenging task. The customized embedded neuromorphic system is one of the most effective approaches for multi-scale simulations ranging from ion channel to network. This p...

Stimulation-mediated reverse engineering of silent neural networks.

Journal of neurophysiology
Reconstructing connectivity of neuronal networks from single-cell activity is essential to understanding brain function, but the challenge of deciphering connections from populations of silent neurons has been largely unmet. We demonstrate a protocol...

Auditory perception architecture with spiking neural network and implementation on FPGA.

Neural networks : the official journal of the International Neural Network Society
Spike-based perception brings up a new research idea in the field of neuromorphic engineering. A high-performance biologically inspired flexible spiking neural network (SNN) architecture provides a novel method for the exploration of perception mecha...

Motor decoding from the posterior parietal cortex using deep neural networks.

Journal of neural engineering
Motor decoding is crucial to translate the neural activity for brain-computer interfaces (BCIs) and provides information on how motor states are encoded in the brain. Deep neural networks (DNNs) are emerging as promising neural decoders. Nevertheless...

Approximate spectral decomposition of Fisher information matrix for simple ReLU networks.

Neural networks : the official journal of the International Neural Network Society
We argue the Fisher information matrix (FIM) of one hidden layer networks with the ReLU activation function. For a network, let W denote the d×p weight matrix from the d-dimensional input to the hidden layer consisting of p neurons, and v the p-dimen...

Targeting operational regimes of interest in recurrent neural networks.

PLoS computational biology
Neural computations emerge from local recurrent neural circuits or computational units such as cortical columns that comprise hundreds to a few thousand neurons. Continuous progress in connectomics, electrophysiology, and calcium imaging require trac...

Design of continuous-time recurrent neural networks with piecewise-linear activation function for generation of prescribed sequences of bipolar vectors.

Neural networks : the official journal of the International Neural Network Society
A recurrent neural network (RNN) can generate a sequence of patterns as the temporal evolution of the output vector. This paper focuses on a continuous-time RNN model with a piecewise-linear activation function that has neither external inputs nor hi...

Brain-inspired multimodal hybrid neural network for robot place recognition.

Science robotics
Place recognition is an essential spatial intelligence capability for robots to understand and navigate the world. However, recognizing places in natural environments remains a challenging task for robots because of resource limitations and changing ...

A 5.3 pJ/Spike CMOS Neural Array Employing Time-Modulated Axon-Sharing and Background Mismatch Calibration Techniques.

IEEE transactions on biomedical circuits and systems
Inspired by the human brain, spiking neuron networks are promising to realize energy-efficient and low-latency neuromorphic computing. However, even state-of-the-art silicon neurons are orders of magnitude worse than biological neurons in terms of ar...

Evolution-communication spiking neural P systems with energy request rules.

Neural networks : the official journal of the International Neural Network Society
Evolution-communication spiking neural P systems with energy request rules (ECSNP-ER systems) are proposed and developed as a new variant of evolution-communication spiking neural P systems. In ECSNP-ER systems, in addition to spike-evolution rules a...