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Related Concept Videos

Long-term Potentiation01:35

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Long-term Potentiation01:25

Long-term Potentiation

Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
Hebbian LTP
LTP can occur when presynaptic neurons...
Long-term Depression01:03

Long-term Depression

Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
Calcium Ion Concentration Mechanism
If over time, all...
Long-term Depression01:05

Long-term Depression

Long-term depression, or LTD, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTD is the process of synaptic weakening that occurs over time between pre and postsynaptic neuronal connections. The synaptic weakening of LTD works in opposition to synaptic strengthening by long-term potentiation (LTP) and together are the main mechanisms that underlie learning and memory.
Long-Term Memory01:18

Long-Term Memory

Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
Long-term memory can be categorized into two primary types: explicit and implicit memory. Explicit memory, also known as declarative memory, involves the conscious recollection of information that we deliberately try to remember, recall, and articulate. This type of memory encompasses specific facts, events, and...
Storage01:23

Storage

A schema is a mental framework that helps individuals organize and interpret information. Schemata, formed from previous experiences, influence how we process new information: how we encode it, the inferences we make, and how we retrieve it. For instance, a schema for what a typical classroom looks like might include desks, a teacher's desk, a whiteboard, and students in such an environment. This expectation helps us quickly understand and navigate new classrooms without needing to analyze each...

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Related Experiment Video

Updated: Jun 28, 2026

Improved Preparation and Preservation of Hippocampal Mouse Slices for a Very Stable and Reproducible Recording of Long-term Potentiation
09:39

Improved Preparation and Preservation of Hippocampal Mouse Slices for a Very Stable and Reproducible Recording of Long-term Potentiation

Published on: June 26, 2013

Long memory lifetimes require complex synapses and limited sparseness.

Daniel D Ben Dayan Rubin1, Stefano Fusi

  • 1Center for Theoretical Neuroscience, Columbia University, NY USA.

Frontiers in Computational Neuroscience
|October 24, 2008
PubMed
Summary
This summary is machine-generated.

Longer memory retention in the brain is achieved through complex synapses, not sparse neural representations. Increased synaptic complexity, despite weakening initial memory traces, enables extended memory lifetimes in larger brain regions.

Keywords:
LearningSparsenessSynaptic plasticity

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Investigating Long-term Synaptic Plasticity in Interlamellar Hippocampus CA1 by Electrophysiological Field Recording

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Related Experiment Videos

Last Updated: Jun 28, 2026

Improved Preparation and Preservation of Hippocampal Mouse Slices for a Very Stable and Reproducible Recording of Long-term Potentiation
09:39

Improved Preparation and Preservation of Hippocampal Mouse Slices for a Very Stable and Reproducible Recording of Long-term Potentiation

Published on: June 26, 2013

3D Modeling of Dendritic Spines with Synaptic Plasticity
07:13

3D Modeling of Dendritic Spines with Synaptic Plasticity

Published on: May 18, 2020

Investigating Long-term Synaptic Plasticity in Interlamellar Hippocampus CA1 by Electrophysiological Field Recording
14:27

Investigating Long-term Synaptic Plasticity in Interlamellar Hippocampus CA1 by Electrophysiological Field Recording

Published on: August 11, 2019

Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Theoretical models suggest sparse neural representations enhance memory longevity by reducing interference.
  • Cortical areas with longer memory lifetimes exhibit less sparse neural representations, a paradox.
  • Synaptic plasticity and metaplasticity are crucial for memory storage and retrieval.

Purpose of the Study:

  • To resolve the paradox between theoretical predictions of sparseness and observed neural representations in memory.
  • To investigate the role of synaptic complexity and metaplasticity in memory retention.
  • To elucidate the relationship between neural sparseness, synaptic dynamics, and memory lifetime.

Main Methods:

  • Analysis of complex models of synaptic dynamics.
  • Modeling metaplastic states with varying degrees of plasticity.
  • Simulating memory retention in large-scale neural networks.

Main Results:

  • Memory retention is more efficient with increased synaptic complexity (more metaplastic states).
  • Larger brain regions retain memories longer only if synaptic complexity scales with synapse number.
  • Increased synaptic complexity and sparseness weaken the initial memory trace.

Conclusions:

  • Long memory lifetimes in larger neural networks necessitate more complex synapses.
  • Complex synapses lead to less sparse neural representations, aligning with observations in the brain.
  • A trade-off exists between initial memory trace strength and long-term memory retention via synaptic complexity.