AIMC Topic: Neurons

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Systematic errors in connectivity inferred from activity in strongly recurrent networks.

Nature neuroscience Sep 7, 2020
Understanding the mechanisms of neural computation and learning will require knowledge of the underlying circuitry. Because it is difficult to directly measure the wiring diagrams of neural circuits, there has long been an interest in estimating them...
Brain Neurons Humans Neural Pathways Animals Models, Neurological Neural Networks, Computer Action Potentials Data Analysis
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A recurrent circuit implements normalization, simulating the dynamics of V1 activity.

Proceedings of the National Academy of Sciences of the United States of America Aug 25, 2020
The normalization model has been applied to explain neural activity in diverse neural systems including primary visual cortex (V1). The model's defining characteristic is that the response of each neuron is divided by a factor that includes a weighte...
Visual Cortex Neurons Neural Networks, Computer Animals Computational Biology Models, Neurological
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A Hybrid Method Based on Extreme Learning Machine and Self Organizing Map for Pattern Classification.

Computational intelligence and neuroscience Aug 25, 2020
Extreme learning machine is a fast learning algorithm for single hidden layer feedforward neural network. However, an improper number of hidden neurons and random parameters have a great effect on the performance of the extreme learning machine. In o...
Generalization, Psychological Algorithms Neural Networks, Computer Neurons
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Frequency-dependent organization of the brain's functional network through delayed-interactions.

Neural networks : the official journal of the International Neural Network Society Aug 20, 2020
The structure of the brain network exhibits modularity at multiple spatial scales. The effect of the modular structure on the brain dynamics has been the focus of several studies in recent years but many aspects remain to be explored. For example, it...
Nerve Net Neurons Connectome Brain Humans Time Factors Neural Networks, Computer
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Spiking Neural P Systems with Extended Channel Rules.

International journal of neural systems Aug 18, 2020
This paper discusses a new variant of spiking neural P systems (in short, SNP systems), spiking neural P systems with extended channel rules (in short, SNP-ECR systems). SNP-ECR systems are a class of distributed parallel computing models. In SNP-ECR...
Models, Neurological Action Potentials Neurons Synapses Neural Networks, Computer
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Medical Image Fusion Method Based on Coupled Neural P Systems in Nonsubsampled Shearlet Transform Domain.

International journal of neural systems Aug 18, 2020
Coupled neural P (CNP) systems are a recently developed Turing-universal, distributed and parallel computing model, combining the spiking and coupled mechanisms of neurons. This paper focuses on how to apply CNP systems to handle the fusion of multi-...
Tomography, X-Ray Computed Magnetic Resonance Imaging Neural Networks, Computer Algorithms Neurons
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Comparing SNNs and RNNs on neuromorphic vision datasets: Similarities and differences.

Neural networks : the official journal of the International Neural Network Society Aug 17, 2020
Neuromorphic data, recording frameless spike events, have attracted considerable attention for the spatiotemporal information components and the event-driven processing fashion. Spiking neural networks (SNNs) represent a family of event-driven models...
Neural Networks, Computer Machine Learning Algorithms Databases, Factual Humans Vision, Ocular Action Potentials Recognition, Psychology Neurons
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DeepCINAC: A Deep-Learning-Based Python Toolbox for Inferring Calcium Imaging Neuronal Activity Based on Movie Visualization.

eNeuro Aug 17, 2020
Two-photon calcium imaging is now widely used to infer neuronal dynamics from changes in fluorescence of an indicator. However, state-of-the-art computational tools are not optimized for the reliable detection of fluorescence transients from highly s...
Motion Pictures Boidae Animals Neurons Deep Learning Calcium Mice
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Engineering recurrent neural networks from task-relevant manifolds and dynamics.

PLoS computational biology Aug 12, 2020
Many cognitive processes involve transformations of distributed representations in neural populations, creating a need for population-level models. Recurrent neural network models fulfill this need, but there are many open questions about how their c...
Neural Networks, Computer Computational Biology Humans Neurons Models, Neurological Memory, Short-Term
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SpiFoG: an efficient supervised learning algorithm for the network of spiking neurons.

Scientific reports Aug 4, 2020
There has been a lot of research on supervised learning in spiking neural network (SNN) for a couple of decades to improve computational efficiency. However, evolutionary algorithm based supervised learning for SNN has not been investigated thoroughl...
Supervised Machine Learning Neurons Nerve Net Action Potentials
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