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Related Experiment Video

Updated: Mar 5, 2026

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

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Mean First Passage Memory Lifetimes by Reducing Complex Synapses to Simple Synapses.

Terry Elliott1

  • 1Department of Electronics and Computer Science, University of Southampton, Highfield, Southampton, SO17 1BJ, U.K. te@ecs.soton.ac.uk.

Neural Computation
|March 24, 2017
PubMed
Summary
This summary is machine-generated.

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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.
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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.
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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.
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Neurons communicate with one another by passing on their electrical signals to other neurons. A synapse is the location where two neurons meet to exchange signals. At the synapse, the neuron that sends the signal is called the presynaptic cell, while the neuron that receives the message is called the postsynaptic cell. Note that most neurons can be both presynaptic and postsynaptic, as they both transmit and receive information.
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Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
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Stability against fluctuations: a two-dimensional study of scaling, bifurcations and spontaneous symmetry breaking in stochastic models of synaptic plasticity.

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The Impact of Sparse Coding on Memory Lifetimes in Simple and Complex Models of Synaptic Plasticity.

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First Passage Time Memory Lifetimes for Multistate, Filter-Based Synapses.

Neural computation·2020
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Dynamic Integrative Synaptic Plasticity Explains the Spacing Effect in the Transition from Short- to Long-Term Memory.

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First Passage Time Memory Lifetimes for Simple, Multistate Synapses: Beyond the Eigenvector Requirement.

Neural computation·2018
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First Passage Time Memory Lifetimes for Simple, Multistate Synapses.

Neural computation·2017

Complex memory models with internal synaptic states can be simplified. This allows for better analysis of memory lifetimes and dynamics in synaptic plasticity, enhancing memory storage capabilities.

Area of Science:

  • Computational Neuroscience
  • Theoretical Neuroscience
  • Machine Learning

Background:

  • Traditional memory models exhibit short memory lifetimes, limiting their capacity.
  • Complex synaptic plasticity models with internal states offer enhanced memory lifetimes but are difficult to analyze.

Purpose of the Study:

  • To simplify complex synaptic plasticity models by integrating out internal synaptic states.
  • To develop a framework for analyzing memory lifetimes in filter-based synaptic plasticity models.

Main Methods:

  • Developed a method to reduce complex synapses to simple synapses by focusing on synaptic strength changes.
  • Formulated master and Fokker-Planck equations for simplified synaptic dynamics.
  • Analyzed memory signal evolution as an initial transient followed by equilibrium dynamics.

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Related Experiment Videos

Last Updated: Mar 5, 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

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Preparation of Acute Hippocampal Slices from Rats and Transgenic Mice for the Study of Synaptic Alterations during Aging and Amyloid Pathology
14:57

Preparation of Acute Hippocampal Slices from Rats and Transgenic Mice for the Study of Synaptic Alterations during Aging and Amyloid Pathology

Published on: March 23, 2011

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Evaluation of Synaptic Multiplicity Using Whole-cell Patch-clamp Electrophysiology
10:52

Evaluation of Synaptic Multiplicity Using Whole-cell Patch-clamp Electrophysiology

Published on: April 23, 2019

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Main Results:

  • Showed that complex synapses can be effectively reduced to simple synapses.
  • Demonstrated that filter-based memory models exhibit an initial memory signal rise.
  • Identified Ornstein-Uhlenbeck-like dynamics governing the return to equilibrium.

Conclusions:

  • The simplification approach provides a tractable method for analyzing complex memory models.
  • This framework allows for the computation of memory lifetimes in synaptic filter-based memory storage.
  • The findings offer insights into enhancing memory capacity and stability in neural systems.