AIMC Topic: Neuronal Plasticity

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Hypergraph-Based Numerical Spiking Neural Membrane Systems with Novel Repartition Protocols.

International journal of neural systems May 8, 2024
The classic spiking neural P (SN P) systems abstract the real biological neural network into a simple structure based on graphs, where neurons can only communicate on the plane. This study proposes the hypergraph-based numerical spiking neural membra...
Computer Simulation Animals Humans Neural Networks, Computer Action Potentials Neurons Synapses Neuronal Plasticity Models, Neurological
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A sparse quantized hopfield network for online-continual memory.

Nature communications May 2, 2024
An important difference between brains and deep neural networks is the way they learn. Nervous systems learn online where a stream of noisy data points are presented in a non-independent, identically distributed way. Further, synaptic plasticity in t...
Deep Learning Neuronal Plasticity Memory Models, Neurological Algorithms Brain Humans Neural Networks, Computer
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Minicolumn-Based Episodic Memory Model With Spiking Neurons, Dendrites and Delays.

IEEE transactions on neural networks and learning systems May 2, 2024
Episodic memory is fundamental to the brain's cognitive function, but how neuronal activity is temporally organized during its encoding and retrieval is still unknown. In this article, combining hippocampus structure with a spiking neural network (SN...
Humans Neuronal Plasticity Models, Neurological Neural Networks, Computer Hippocampus Action Potentials Memory, Episodic Algorithms Neurons Dendrites
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Using deep learning to quantify neuronal activation from single-cell and spatial transcriptomic data.

Nature communications Jan 26, 2024
Neuronal activity-dependent transcription directs molecular processes that regulate synaptic plasticity, brain circuit development, behavioral adaptation, and long-term memory. Single cell RNA-sequencing technologies (scRNAseq) are rapidly developing...
Neurons Male Gene Expression Profiling Mice Neuronal Plasticity Brain Deep Learning Animals
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Inferring neural activity before plasticity as a foundation for learning beyond backpropagation.

Nature neuroscience Jan 3, 2024
For both humans and machines, the essence of learning is to pinpoint which components in its information processing pipeline are responsible for an error in its output, a challenge that is known as 'credit assignment'. It has long been assumed that c...
Prospective Studies Animals Rats Neuronal Plasticity Neural Networks, Computer Machine Learning Humans
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A bi-functional three-terminal memristor applicable as an artificial synapse and neuron.

Nanoscale Nov 2, 2023
Due to their significant resemblance to the biological brain, spiking neural networks (SNNs) show promise in handling spatiotemporal information with high time and energy efficiency. Two-terminal memristors have the capability to achieve both synapti...
Neural Networks, Computer Brain Neuronal Plasticity Neurons Synapses
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Signatures of task learning in neural representations.

Current opinion in neurobiology Sep 12, 2023
While neural plasticity has long been studied as the basis of learning, the growth of large-scale neural recording techniques provides a unique opportunity to study how learning-induced activity changes are coordinated across neurons within the same ...
Neural Networks, Computer Neuronal Plasticity Neurons Learning
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Synchronization in STDP-driven memristive neural networks with time-varying topology.

Journal of biological physics Sep 1, 2023
Synchronization is a widespread phenomenon in the brain. Despite numerous studies, the specific parameter configurations of the synaptic network structure and learning rules needed to achieve robust and enduring synchronization in neurons driven by s...
Synapses Models, Neurological Neural Networks, Computer Neurons Brain Neuronal Plasticity
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Representational drift as a window into neural and behavioural plasticity.

Current opinion in neurobiology Jun 29, 2023
Large-scale recordings of neural activity over days and weeks have revealed that neural representations of familiar tasks, precepts and actions continually evolve without obvious changes in behaviour. We hypothesise that this steady drift in neural a...
Learning Neural Networks, Computer Neuronal Plasticity
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Coherent noise enables probabilistic sequence replay in spiking neuronal networks.

PLoS computational biology May 2, 2023
Animals rely on different decision strategies when faced with ambiguous or uncertain cues. Depending on the context, decisions may be biased towards events that were most frequently experienced in the past, or be more explorative. A particular type o...
Mental Recall Models, Neurological Animals Action Potentials Neurons Neuronal Plasticity Neural Networks, Computer Memory Learning
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