AIMC Topic: Action Potentials

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An optimal time interval of input spikes involved in synaptic adjustment of spike sequence learning.

Neural networks : the official journal of the International Neural Network Society Apr 1, 2019
The supervised learning methods for spiking neurons based on temporal encoding are important foundation for the development of spiking neural networks. During the learning process, the synaptic weights of a spiking neuron are adjusted to make the neu...
Algorithms Time Factors Neural Networks, Computer Supervised Machine Learning Neurons Synapses Models, Neurological Action Potentials
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Evolutionary Spiking Neural Networks for Solving Supervised Classification Problems.

Computational intelligence and neuroscience Mar 28, 2019
This paper presents a grammatical evolution (GE)-based methodology to automatically design third generation artificial neural networks (ANNs), also known as spiking neural networks (SNNs), for solving supervised classification problems. The proposal ...
Neural Networks, Computer Action Potentials Neurons Problem Solving Algorithms Models, Neurological
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Compact Hardware Synthesis of Stochastic Spiking Neural Networks.

International journal of neural systems Feb 8, 2019
Spiking neural networks (SNN) are able to emulate real neural behavior with high confidence due to their bio-inspired nature. Many designs have been proposed for the implementation of SNN in hardware, although the realization of high-density and biol...
Pattern Recognition, Automated Stochastic Processes Computers Action Potentials Neural Networks, Computer Machine Learning
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Monitor-Based Spiking Recurrent Network for the Representation of Complex Dynamic Patterns.

International journal of neural systems Feb 8, 2019
Neural networks are powerful computation tools for mimicking the human brain to solve realistic problems. Since spiking neural networks are a type of brain-inspired network, called the novel spiking system, Monitor-based Spiking Recurrent network (Mb...
Pattern Recognition, Automated Machine Learning Neural Networks, Computer Action Potentials
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An Efficient Population Density Method for Modeling Neural Networks with Synaptic Dynamics Manifesting Finite Relaxation Time and Short-Term Plasticity.

eNeuro Jan 17, 2019
When incorporating more realistic synaptic dynamics, the computational efficiency of population density methods (PDMs) declines sharply due to the increase in the dimension of master equations. To avoid such a decline, we develop an efficient PDM, te...
Population Density Neurons Neural Networks, Computer Nonlinear Dynamics Computer Simulation Neuronal Plasticity Humans Animals Models, Neurological Time Factors Action Potentials
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An Attention-Based Spiking Neural Network for Unsupervised Spike-Sorting.

International journal of neural systems Dec 27, 2018
Bio-inspired computing using artificial spiking neural networks promises performances outperforming currently available computational approaches. Yet, the number of applications of such networks remains limited due to the absence of generic training ...
Pattern Recognition, Automated Models, Neurological Action Potentials Neural Networks, Computer
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Deep learning in spiking neural networks.

Neural networks : the official journal of the International Neural Network Society Dec 18, 2018
In recent years, deep learning has revolutionized the field of machine learning, for computer vision in particular. In this approach, a deep (multilayer) artificial neural network (ANN) is trained, most often in a supervised manner using backpropagat...
Neurons Brain Deep Learning Machine Learning Models, Neurological Humans Algorithms Action Potentials Neural Networks, Computer
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Asynchronous Multiplex Communication Channels in 2-D Neural Network With Fluctuating Characteristics.

IEEE transactions on neural networks and learning systems Dec 14, 2018
Neurons behave like transistors, but have fluctuating characteristics. In this paper, we show that several asynchronous multiplex communication channels can be established in a 2-D mesh neural network with randomly generated weights between eight nei...
Neurons Models, Neurological Action Potentials Humans Computer Simulation Neural Networks, Computer Communication Brain
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Synchronization-induced spike termination in networks of bistable neurons.

Neural networks : the official journal of the International Neural Network Society Nov 27, 2018
We observe and study a self-organized phenomenon whereby the activity in a network of spiking neurons spontaneously terminates. We consider different types of populations, consisting of bistable model neurons connected electrically by gap junctions, ...
Neural Networks, Computer Models, Neurological Action Potentials Neurons Humans Gap Junctions Synapses Cortical Synchronization Nerve Net
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Spontaneous activity emerging from an inferred network model captures complex spatio-temporal dynamics of spike data.

Scientific reports Nov 19, 2018
Inference methods are widely used to recover effective models from observed data. However, few studies attempted to investigate the dynamics of inferred models in neuroscience, and none, to our knowledge, at the network level. We introduce a principl...
Neural Networks, Computer Computer Simulation Poisson Distribution Action Potentials
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