AIMC Topic:
Neurons

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Evolving Plasticity for Autonomous Learning under Changing Environmental Conditions.

Evolutionary computation Sep 1, 2021
A fundamental aspect of learning in biological neural networks is the plasticity property which allows them to modify their configurations during their lifetime. Hebbian learning is a biologically plausible mechanism for modeling the plasticity prope...
Neural Networks, Computer Algorithms Learning Neurons
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A Biologically Plausible Neural Network for Multichannel Canonical Correlation Analysis.

Neural computation Aug 19, 2021
Cortical pyramidal neurons receive inputs from multiple distinct neural populations and integrate these inputs in separate dendritic compartments. We explore the possibility that cortical microcircuits implement canonical correlation analysis (CCA), ...
Neural Networks, Computer Algorithms Neurons Canonical Correlation Analysis
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Few-Shot Learning in Spiking Neural Networks by Multi-Timescale Optimization.

Neural computation Aug 19, 2021
Learning new concepts rapidly from a few examples is an open issue in spike-based machine learning. This few-shot learning imposes substantial challenges to the current learning methodologies of spiking neuron networks (SNNs) due to the lack of task-...
Neural Networks, Computer Machine Learning Algorithms Neurons
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Hidden coexisting firings in fractional-order hyperchaotic memristor-coupled HR neural network with two heterogeneous neurons and its applications.

Chaos (Woodbury, N.Y.) Aug 1, 2021
The firing patterns of each bursting neuron are different because of the heterogeneity, which may be derived from the different parameters or external drives of the same kind of neurons, or even neurons with different functions. In this paper, the di...
Neural Networks, Computer Cluster Analysis Models, Neurological Neurons
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Phase-locking intermittency induced by dynamical heterogeneity in networks of thermosensitive neurons.

Chaos (Woodbury, N.Y.) Aug 1, 2021
In this work, we study the phase synchronization of a neural network and explore how the heterogeneity in the neurons' dynamics can lead their phases to intermittently phase-lock and unlock. The neurons are connected through chemical excitatory conne...
Neural Networks, Computer Models, Neurological Neurons
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Storage Capacity of Quaternion-Valued Hopfield Neural Networks With Dual Connections.

Neural computation Jul 26, 2021
A complex-valued Hopfield neural network (CHNN) is a multistate Hopfield model. A quaternion-valued Hopfield neural network (QHNN) with a twin-multistate activation function was proposed to reduce the number of weight parameters of CHNN. Dual connect...
Neural Networks, Computer Neurons
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Randomized Self-Organizing Map.

Neural computation Jul 26, 2021
We propose a variation of the self-organizing map algorithm by considering the random placement of neurons on a two-dimensional manifold, following a blue noise distribution from which various topologies can be derived. These topologies possess rando...
Neural Networks, Computer Algorithms Neurons
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Organizing principles of the C. elegans contactome.

Cell systems Jul 21, 2021
Two recent studies published in Nature generate and analyze, for the first time, the network of ∼100,000 membrane contacts between neurons in the C. elegans nerve ring. These novel data, extracted from legacy electron microscographs, represent a shif...
Neural Networks, Computer Brain Animals Neurons Caenorhabditis elegans
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Bursting potentiates the neuro-AI connection.

Nature neuroscience Jul 1, 2021
Artificial Intelligence Action Potentials Neurons
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Skip-Connected Self-Recurrent Spiking Neural Networks With Joint Intrinsic Parameter and Synaptic Weight Training.

Neural computation Jun 11, 2021
As an important class of spiking neural networks (SNNs), recurrent spiking neural networks (RSNNs) possess great computational power and have been widely used for processing sequential data like audio and text. However, most RSNNs suffer from two pro...
Neural Networks, Computer Neurons
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