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Models, Neurological

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Biologically Plausible Sparse Temporal Word Representations.

IEEE transactions on neural networks and learning systems
Word representations, usually derived from a large corpus and endowed with rich semantic information, have been widely applied to natural language tasks. Traditional deep language models, on the basis of dense word representations, requires large mem...

Self-Lateral Propagation Elevates Synaptic Modifications in Spiking Neural Networks for the Efficient Spatial and Temporal Classification.

IEEE transactions on neural networks and learning systems
The brain's mystery for efficient and intelligent computation hides in the neuronal encoding, functional circuits, and plasticity principles in natural neural networks. However, many plasticity principles have not been fully incorporated into artific...

Neuroscientific insights about computer vision models: a concise review.

Biological cybernetics
The development of biologically-inspired computational models has been the focus of study ever since the artificial neuron was introduced by McCulloch and Pitts in 1943. However, a scrutiny of literature reveals that most attempts to replicate the hi...

Learning to segment self-generated from externally caused optic flow through sensorimotor mismatch circuits.

Neural networks : the official journal of the International Neural Network Society
Efficient sensory detection requires the capacity to ignore task-irrelevant information, for example when optic flow patterns created by egomotion need to be disentangled from object perception. To investigate how this is achieved in the visual syste...

Pattern recognition using spiking antiferromagnetic neurons.

Scientific reports
Spintronic devices offer a promising avenue for the development of nanoscale, energy-efficient artificial neurons for neuromorphic computing. It has previously been shown that with antiferromagnetic (AFM) oscillators, ultra-fast spiking artificial ne...

A neural network model of differentiation and integration of competing memories.

eLife
What determines when neural representations of memories move together (integrate) or apart (differentiate)? Classic supervised learning models posit that, when two stimuli predict similar outcomes, their representations should integrate. However, the...

Referring Image Segmentation with Multi-Modal Feature Interaction and Alignment Based on Convolutional Nonlinear Spiking Neural Membrane Systems.

International journal of neural systems
Referring image segmentation aims to accurately align image pixels and text features for object segmentation based on natural language descriptions. This paper proposes NSNPRIS (convolutional nonlinear spiking neural P systems for referring image seg...

Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing.

Nature communications
Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neur...

Oscillations in an artificial neural network convert competing inputs into a temporal code.

PLoS computational biology
The field of computer vision has long drawn inspiration from neuroscientific studies of the human and non-human primate visual system. The development of convolutional neural networks (CNNs), for example, was informed by the properties of simple and ...

Deep brain stimulation and lag synchronization in a memristive two-neuron network.

Neural networks : the official journal of the International Neural Network Society
In the pursuit of potential treatments for neurological disorders and the alleviation of patient suffering, deep brain stimulation (DBS) has been utilized to intervene or investigate pathological neural activities. To explore the exact mechanism of h...