AIMC Topic: Neuronal Plasticity

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Distributed Synaptic Connection Strength Changes Dynamics in a Population Firing Rate Model in Response to Continuous External Stimuli.

Neural computation Apr 17, 2025
Neural network complexity allows for diverse neuronal population dynamics and realizes higherorder brain functions such as cognition and memory. Complexity is enhanced through chemical synapses with exponentially decaying conductance and greater vari...
Primary Visual Cortex Synapses Nerve Net Computer Simulation Neuronal Plasticity Action Potentials Animals Models, Neurological Humans Neurons Neural Networks, Computer
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Role of short-term plasticity and slow temporal dynamics in enhancing time series prediction with a brain-inspired recurrent neural network.

Chaos (Woodbury, N.Y.) Feb 1, 2025
Typical reservoir networks are based on random connectivity patterns that differ from brain circuits in two important ways. First, traditional reservoir networks lack synaptic plasticity among recurrent units, whereas cortical networks exhibit plasti...
Neural Networks, Computer Brain Humans Models, Neurological Nerve Net Recurrent Neural Networks Time Factors Neuronal Plasticity
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Combining meta reinforcement learning with neural plasticity mechanisms for improved AI performance.

PloS one Jan 1, 2025
This research explores the potential of combining Meta Reinforcement Learning (MRL) with Spike-Timing-Dependent Plasticity (STDP) to enhance the performance and adaptability of AI agents in Atari game settings. Our methodology leverages MRL to swiftl...
Humans Neuronal Plasticity Artificial Intelligence Models, Neurological Learning Reinforcement, Psychology
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Bioplausible Unsupervised Delay Learning for Extracting Spatiotemporal Features in Spiking Neural Networks.

Neural computation Jun 7, 2024
The plasticity of the conduction delay between neurons plays a fundamental role in learning temporal features that are essential for processing videos, speech, and many high-level functions. However, the exact underlying mechanisms in the brain for t...
Neurons Neuronal Plasticity Models, Neurological Action Potentials Unsupervised Machine Learning Neural Networks, Computer Brain Humans
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A robust balancing mechanism for spiking neural networks.

Chaos (Woodbury, N.Y.) Apr 1, 2024
Dynamical balance of excitation and inhibition is usually invoked to explain the irregular low firing activity observed in the cortex. We propose a robust nonlinear balancing mechanism for a random network of spiking neurons, which works also in the ...
Synapses Neural Networks, Computer Neuronal Plasticity Action Potentials Neurons Models, Neurological
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Mean-Field Approximations With Adaptive Coupling for Networks With Spike-Timing-Dependent Plasticity.

Neural computation Aug 7, 2023
Understanding the effect of spike-timing-dependent plasticity (STDP) is key to elucidating how neural networks change over long timescales and to design interventions aimed at modulating such networks in neurological disorders. However, progress is r...
Neural Networks, Computer Action Potentials Neurons Neuronal Plasticity Models, Neurological
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Real-time Neural Connectivity Inference with Presynaptic Spike-driven Spike Timing-Dependent Plasticity.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2023
Brain-like artificial intelligence in electronics can be built efficiently by understanding the connectivity of neuronal circuitry. The concept of neural connectivity inference with a two-dimensional cross-bar structure memristor array is indicated i...
Neuronal Plasticity Action Potentials Neurons Neural Networks, Computer Artificial Intelligence
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Strong Allee Effect Synaptic Plasticity Rule in an Unsupervised Learning Environment.

Neural computation Apr 18, 2023
Synaptic plasticity, or the ability of a brain to change one or more of its functions or structures at the synaptic level, has generated and is still generating a lot of interest from the scientific community especially from neuroscientists. These in...
Unsupervised Machine Learning Synapses Brain Humans Child Neuronal Plasticity
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An Area- and Energy-Efficient Spiking Neural Network With Spike-Time-Dependent Plasticity Realized With SRAM Processing-in-Memory Macro and On-Chip Unsupervised Learning.

IEEE transactions on biomedical circuits and systems Feb 1, 2023
In this article, we present a spiking neural network (SNN) based on both SRAM processing-in-memory (PIM) macro and on-chip unsupervised learning with Spike-Time-Dependent Plasticity (STDP). Co-design of algorithm and hardware for hardware-friendly SN...
Neuronal Plasticity Models, Neurological Neural Networks, Computer Algorithms Unsupervised Machine Learning
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Fear memory-associated synaptic and mitochondrial changes revealed by deep learning-based processing of electron microscopy data.

Cell reports Aug 2, 2022
Serial section electron microscopy (ssEM) can provide comprehensive 3D ultrastructural information of the brain with exceptional computational cost. Targeted reconstruction of subcellular structures from ssEM datasets is less computationally demandin...
Mitochondria Mice Synapses Animals Neuronal Plasticity Microscopy, Electron Deep Learning Fear
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