Behavioral time scale synaptic plasticity underlies CA1 place fields

By Katie C. Bittner, Aaron D. Milstein, Christine Grienberger, S. Romani, Jeffrey C. Magee. Science, 2017

629 citations recorded by the source corpus.

A different form of synaptic plasticity How do synaptic or other neuronal changes support learning? This subject has been dominated by Hebb's postulate of synaptic change. Although there is strong experimental support for Hebbian plasticity in a number of preparations, alternative ideas have also been developed over the years. Bittner et al. provide in vivo, in vitro, and modeling data to support the view that non-Hebbian plasticity may underlie the formation of hippocampal place fields (see the Perspective by Krupic). Instead of multiple pairings, a single strong Ca2+ plateau potential in neuronal dendrites paired with spatial inputs may be sufficient to produce place cells. Science, this issue p. 1033; see also p. 974 A particular type of long–time scale plasticity shapes the formation of stable place fields in the hippocampus. Learning is primarily mediated by activity-dependent modifications of synaptic strength within neuronal circuits. We discovered that place fields in hippocampal area CA1 are produced by a synaptic potentiation notably different from Hebbian plasticity. Place fields could be produced in vivo in a single trial by potentiation of input that arrived seconds before and after complex spiking. The potentiated synaptic input was not initially coincident with action potentials or depolarization. This rule, named behavioral time scale synaptic plasticity, abruptly modifies inputs that were neither causal nor close in time to postsynaptic activation. In slices, five pairings of subthreshold presynaptic activity and calcium (Ca2+) plateau potentials produced a large potentiation with an asymmetric seconds-long time course. This plasticity efficiently stores entire behavioral sequences within synaptic weights to produce predictive place cell activity.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/b0e8c9a57c8873abee1e62d959e45ee3bb9228cc

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Citations in the corpus

Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules (2018, background): “The second study combined in vivo with in vitro data (Bittner et al., 2017).”
Burst-dependent synaptic plasticity can coordinate learning in hierarchical circuits (2020, background): “Here, ⌧pre is typically much smaller than ⌧avg, with ⌧pre ⇠ 10 ms, but it could possibly be made larger to accommodate plasticity rules with slower dynamics [12].”
Synaptic Plasticity Forms and Functions. (2020, background): “…of a silent CA1 cell, and the large, long-lasting depolarization associated with the plateau effectively spreads throughout the neuron (Grienberger et al. 2014), inducing a potentiation of excitatory synaptic inputs that arrive around the plateau (Bittner et al. 2015, 2017) ( Figure 4 b ).”
The content of hippocampal “replay” (2018, background): “…may arise from a number of physiological origins, including enhanced synaptic plasticity due to increased release of a reward neuromodulator as the rat approaches the salient location (van der Meer & Redish, 2011), or a recently identified non-Hebbian form of plasticity (Bittner et al., 2017).”
Towards deep learning with segregated dendrites (2016, background): “Instead, they could be achieved by having two distinct dendritic compartments in each hidden layer neuron: a ‘basal’ compartment, strongly coupled to the soma for integrating bottom-up sensory information, and an ‘apical’ compartment for integrating top-down feedback in order calculate credit assign”