AIMC Topic: Models, Neurological

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Replay as a Basis for Backpropagation Through Time in the Brain.

Neural computation Feb 14, 2025
How episodic memories are formed in the brain is a continuing puzzle for the neuroscience community. The brain areas that are critical for episodic learning (e.g., the hippocampus) are characterized by recurrent connectivity and generate frequent off...
Hippocampus Memory, Episodic Rats Models, Neurological Neural Networks, Computer Algorithms Brain Humans Computer Simulation Animals Time Factors Learning
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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...
Humans Neural Networks, Computer Neuronal Plasticity Models, Neurological Nerve Net Brain Time Factors Recurrent Neural Networks
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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 Models, Neurological Artificial Intelligence Neuronal Plasticity Reinforcement, Psychology Learning
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Intelligent Control to Suppress Epileptic Seizures in the Amygdala: In Silico Investigation Using a Network of Izhikevich Neurons.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society Jan 1, 2025
Closed-loop electricalstimulation of brain structures is one of the most promising techniques to suppress epileptic seizures in drug-resistant refractory patients who are also ineligible to ablative neurosurgery. In this work, an intelligent controll...
Nerve Net Amygdala Neurons Nonlinear Dynamics Epilepsy Seizures Algorithms Humans Computer Simulation Neural Networks, Computer Action Potentials Models, Neurological Electroencephalography
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Characterization of Machine Learning-Based Surrogate Models of Neural Activation Under Electrical Stimulation.

Bioelectromagnetics Jan 1, 2025
Electrical stimulation of peripheral nerves via implanted electrodes has been shown to be a promising approach to restore sensation, movement, and autonomic functions across a wide range of illnesses and injuries. While in principle computational mod...
Electric Stimulation Animals Models, Neurological Machine Learning Algorithms Humans
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Toward biologically realistic models of the motor system.

Neuron Dec 4, 2024
In this issue of Neuron, Chiappa et al. describe how neural networks can be trained to perform complex hand motor skills. A key to their approach is curriculum learning, breaking learning into stages, leading to good control.
Humans Neural Networks, Computer Motor Skills Learning Models, Neurological
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Sparse-Coding Variational Autoencoders.

Neural computation Nov 19, 2024
The sparse coding model posits that the visual system has evolved to efficiently code natural stimuli using a sparse set of features from an overcomplete dictionary. The original sparse coding model suffered from two key limitations; however: (1) com...
Animals Models, Neurological Neurons Neural Networks, Computer Algorithms Humans
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Fault-tolerant neural networks from biological error correction codes.

Physical review. E Nov 1, 2024
It has been an open question in deep learning if fault-tolerant computation is possible: can arbitrarily reliable computation be achieved using only unreliable neurons? In the grid cells of the mammalian cortex, analog error correction codes have bee...
Animals Neurons Neural Networks, Computer Models, Neurological Action Potentials
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Trainable Reference Spikes Improve Temporal Information Processing of SNNs With Supervised Learning.

Neural computation Sep 17, 2024
Spiking neural networks (SNNs) are the next-generation neural networks composed of biologically plausible neurons that communicate through trains of spikes. By modifying the plastic parameters of SNNs, including weights and time delays, SNNs can be t...
Action Potentials Time Factors Supervised Machine Learning Humans Neurons Models, Neurological Neural Networks, Computer
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Inference on the Macroscopic Dynamics of Spiking Neurons.

Neural computation Sep 17, 2024
The process of inference on networks of spiking neurons is essential to decipher the underlying mechanisms of brain computation and function. In this study, we conduct inference on parameters and dynamics of a mean-field approximation, simplifying th...
Models, Neurological Action Potentials Bayes Theorem Algorithms Humans Neurons Animals Neural Networks, Computer
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