Biomaterial Database

Spike timing-dependent plasticity and memory. 2023

Dominique Debanne, and Yanis Inglebert

UNIS, UMR-1072, INSERM, Aix-Marseille Université, Marseille, France. Electronic address: dominique.debanne@inserm.fr.

Spike timing-dependent plasticity (STDP) is a bidirectional form of synaptic plasticity discovered about 30 years ago and based on the relative timing of pre- and post-synaptic spiking activity with a millisecond precision. STDP is thought to be involved in the formation of memory but the millisecond-precision spike-timing required for STDP is difficult to reconcile with the much slower timescales of behavioral learning. This review therefore aims to expose and discuss recent findings about i) the multiple STDP learning rules at both excitatory and inhibitory synapses in vitro, ii) the contribution of STDP-like synaptic plasticity in the formation of memory in vivo and iii) the implementation of STDP rules in artificial neural networks and memristive devices.

UI	MeSH Term	Description	Entries
D007858	Learning	Relatively permanent change in behavior that is the result of past experience or practice. The concept includes the acquisition of knowledge.	Phenomenography
D009473	Neuronal Plasticity	The capacity of the NERVOUS SYSTEM to change its reactivity as the result of successive activations.	Brain Plasticity,Plasticity, Neuronal,Axon Pruning,Axonal Pruning,Dendrite Arborization,Dendrite Pruning,Dendritic Arborization,Dendritic Pruning,Dendritic Remodeling,Neural Plasticity,Neurite Pruning,Neuronal Arborization,Neuronal Network Remodeling,Neuronal Pruning,Neuronal Remodeling,Neuroplasticity,Synaptic Plasticity,Synaptic Pruning,Arborization, Dendrite,Arborization, Dendritic,Arborization, Neuronal,Arborizations, Dendrite,Arborizations, Dendritic,Arborizations, Neuronal,Axon Prunings,Axonal Prunings,Brain Plasticities,Dendrite Arborizations,Dendrite Prunings,Dendritic Arborizations,Dendritic Prunings,Dendritic Remodelings,Network Remodeling, Neuronal,Network Remodelings, Neuronal,Neural Plasticities,Neurite Prunings,Neuronal Arborizations,Neuronal Network Remodelings,Neuronal Plasticities,Neuronal Prunings,Neuronal Remodelings,Neuroplasticities,Plasticities, Brain,Plasticities, Neural,Plasticities, Neuronal,Plasticities, Synaptic,Plasticity, Brain,Plasticity, Neural,Plasticity, Synaptic,Pruning, Axon,Pruning, Axonal,Pruning, Dendrite,Pruning, Dendritic,Pruning, Neurite,Pruning, Neuronal,Pruning, Synaptic,Prunings, Axon,Prunings, Axonal,Prunings, Dendrite,Prunings, Dendritic,Prunings, Neurite,Prunings, Neuronal,Prunings, Synaptic,Remodeling, Dendritic,Remodeling, Neuronal,Remodeling, Neuronal Network,Remodelings, Dendritic,Remodelings, Neuronal,Remodelings, Neuronal Network,Synaptic Plasticities,Synaptic Prunings
D009474	Neurons	The basic cellular units of nervous tissue. Each neuron consists of a body, an axon, and dendrites. Their purpose is to receive, conduct, and transmit impulses in the NERVOUS SYSTEM.	Nerve Cells,Cell, Nerve,Cells, Nerve,Nerve Cell,Neuron
D000200	Action Potentials	Abrupt changes in the membrane potential that sweep along the CELL MEMBRANE of excitable cells in response to excitation stimuli.	Spike Potentials,Nerve Impulses,Action Potential,Impulse, Nerve,Impulses, Nerve,Nerve Impulse,Potential, Action,Potential, Spike,Potentials, Action,Potentials, Spike,Spike Potential
D013569	Synapses	Specialized junctions at which a neuron communicates with a target cell. At classical synapses, a neuron's presynaptic terminal releases a chemical transmitter stored in synaptic vesicles which diffuses across a narrow synaptic cleft and activates receptors on the postsynaptic membrane of the target cell. The target may be a dendrite, cell body, or axon of another neuron, or a specialized region of a muscle or secretory cell. Neurons may also communicate via direct electrical coupling with ELECTRICAL SYNAPSES. Several other non-synaptic chemical or electric signal transmitting processes occur via extracellular mediated interactions.	Synapse
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron