Synaptic Basis of Behavioral Timescale Plasticity

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Oct. 5, 2023

Abstract Learning and memory are fundamental to adaptive behavior cognition. Various forms of synaptic plasticity have been proposed as cellular substrates for the emergence feature selectivity in neurons underlying episodic memory. However, despite decades work, our understanding how underlies encoding remains limited, largely due a shortage tools technical challenges associated with visualization at single-neuron resolution awake-behaving animals. Behavioral Timescale Synaptic Plasticity (BTSP) postulates that inputs active during seconds-long time window preceding immediately following large depolarizing plateau spike potentiated, while outside this depressed. We experimentally tested model vivo mice using an all-optical approach by inducing place fields (PFs) single CA1 pyramidal (CA1PNs) monitoring spatiotemporal tuning individual dendritic spines changes their corresponding weights. identified asymmetric kernel resulting from bidirectional modifications weights around burst induction. Surprisingly, work also uncovered compartment-specific differences magnitude temporal expression between basal oblique dendrites CA1PNs. Our results provide first experimental evidence linking rapid spatial hippocampal neurons, critical prerequisite

Language: Английский

A biochemical description of postsynaptic plasticity—with timescales ranging from milliseconds to seconds

Proceedings of the National Academy of Sciences, Journal Year: 2024, Volume and Issue: 121(7)

Published: Feb. 7, 2024

Synaptic plasticity [long-term potentiation/depression (LTP/D)], is a cellular mechanism underlying learning. Two distinct types of early LTP/D (E-LTP/D), acting on very different time scales, have been observed experimentally—spike timing dependent (STDP), scales tens ms; and behavioral scale synaptic (BTSP), seconds. BTSP candidate for rapid learning spatial location by place cells. Here, computational model the induction E-LTP/D at spine head synapse hippocampal pyramidal neuron developed. The single-compartment represents two interacting biochemical pathways activation (phosphorylation) kinase (CaMKII) with phosphatase, ion inflow through channels (NMDAR, CaV1,Na). reactions are represented deterministic system differential equations, detailed description CaMKII that includes opening compact state CaMKII. This single captures realistic responses (temporal profiles differing timescales) STDP their asymmetries. simulations distinguish several mechanisms vs. BTSP, including i) flow Ca 2 + NMDAR CaV1 channels, ii) origin in also realizes priming E-LTP induced CaV1.3 channels. Once head, this small additional opens CaMKII, placing ready subsequent LTP.

Language: Английский

Local, calcium- and reward-based synaptic learning rule that enhances dendritic nonlinearities can solve the nonlinear feature binding problem

Published: April 24, 2025

This study investigates the computational potential of single striatal projection neurons (SPN), emphasizing dendritic nonlinearities and their crucial role in solving complex integration problems. Utilizing a biophysically detailed multicompartmental model an SPN, we introduce calcium-based, local synaptic learning rule dependent on plateau potentials. According to what is known about excitatory corticostriatal synapses, governed by calcium dynamics from NMDA L-type channels dopaminergic reward signals. In order devise self-adjusting rule, which ensures stability for individual weights, metaplasticity also used. We demonstrate that this allows solve nonlinear feature binding problem, task traditionally attributed neuronal networks. detail inhibitory plasticity mechanism contributes compartmentalization, further enhancing efficiency dendrites. silico highlights neurons, providing deeper insights into information processing mechanisms brain executes computations.

Language: Английский