AIMC Topic: Neuronal Plasticity

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Tinnitus-like "hallucinations" elicited by sensory deprivation in an entropy maximization recurrent neural network.

PLoS computational biology
Sensory deprivation has long been known to cause hallucinations or "phantom" sensations, the most common of which is tinnitus induced by hearing loss, affecting 10-20% of the population. An observable hearing loss, causing auditory sensory deprivatio...

Accuracy Versus Simplification in an Approximate Logic Neural Model.

IEEE transactions on neural networks and learning systems
An approximate logic neural model (ALNM) is a novel single-neuron model with plastic dendritic morphology. During the training process, the model can eliminate unnecessary synapses and useless branches of dendrites. It will produce a specific dendrit...

Constrained plasticity reserve as a natural way to control frequency and weights in spiking neural networks.

Neural networks : the official journal of the International Neural Network Society
Biological neurons have adaptive nature and perform complex computations involving the filtering of redundant information. However, most common neural cell models, including biologically plausible, such as Hodgkin-Huxley or Izhikevich, do not possess...

Online Learning and Memory of Neural Trajectory Replays for Prefrontal Persistent and Dynamic Representations in the Irregular Asynchronous State.

Frontiers in neural circuits
In the prefrontal cortex (PFC), higher-order cognitive functions and adaptive flexible behaviors rely on continuous dynamical sequences of spiking activity that constitute neural trajectories in the state space of activity. Neural trajectories subser...

Intrinsic Plasticity-Based Neuroadptive Control With Both Weights and Excitability Tuning.

IEEE transactions on neural networks and learning systems
This brief presents an intrinsic plasticity (IP)-driven neural-network-based tracking control approach for a class of nonlinear uncertain systems. Inspired by the neural plasticity mechanism of individual neuron in nervous systems, a learning rule re...

Biological constraints on neural network models of cognitive function.

Nature reviews. Neuroscience
Neural network models are potential tools for improving our understanding of complex brain functions. To address this goal, these models need to be neurobiologically realistic. However, although neural networks have advanced dramatically in recent ye...

From synapse to network: models of information storage and retrieval in neural circuits.

Current opinion in neurobiology
The mechanisms of information storage and retrieval in brain circuits are still the subject of debate. It is widely believed that information is stored at least in part through changes in synaptic connectivity in networks that encode this information...

Multimodal Tuning of Synaptic Plasticity Using Persistent Luminescent Memitters.

Advanced materials (Deerfield Beach, Fla.)
Mimicking memory processes, including encoding, storing, and retrieving information, is critical for neuromorphic computing and artificial intelligence. Synaptic behavior simulations through electronic, magnetic, or photonic devices based on metal ox...

Computing Primitive of Fully VCSEL-Based All-Optical Spiking Neural Network for Supervised Learning and Pattern Classification.

IEEE transactions on neural networks and learning systems
We propose computing primitive for an all-optical spiking neural network (SNN) based on vertical-cavity surface-emitting lasers (VCSELs) for supervised learning by using biologically plausible mechanisms. The spike-timing-dependent plasticity (STDP) ...

Spine dynamics in the brain, mental disorders and artificial neural networks.

Nature reviews. Neuroscience
In the brain, most synapses are formed on minute protrusions known as dendritic spines. Unlike their artificial intelligence counterparts, spines are not merely tuneable memory elements: they also embody algorithms that implement the brain's ability ...