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Related Concept Videos

Integration of Synaptic Events01:28

Integration of Synaptic Events

Synaptic integration mainly includes the summation of graded potentials. Graded potentials, regardless of their type, cause subtle alterations in membrane voltage, resulting in either depolarization or hyperpolarization. These incremental changes, when combined or summed, can propel the neuron toward its threshold. Consider, for example, a membrane experiencing a +15 mV shift, causing it to depolarize from -70 mV to -55 mV. In this scenario, graded potentials govern the membrane's ability to...
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...
Chemical Synapses01:26

Chemical Synapses

Chemical synapses are specialized sites between two neurons or between a neuron and a non-neuronal cell like a muscle, glandular or sensory cell.
Because chemical synapses depend on the release of neurotransmitter molecules from synaptic vesicles to pass on their signal, there is an approximately one millisecond delay between when the axon potential reaches the presynaptic terminal and when the neurotransmitter leads to opening of postsynaptic ion channels. Additionally, this signaling is...
Chemical Synapses01:26

Chemical Synapses

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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Synaptic Signaling01:12

Synaptic Signaling

Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.

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Related Experiment Video

Updated: May 11, 2026

Investigation of Synaptic Tagging/Capture and Cross-capture using Acute Hippocampal Slices from Rodents
11:29

Investigation of Synaptic Tagging/Capture and Cross-capture using Acute Hippocampal Slices from Rodents

Published on: September 4, 2015

Synaptic encoding of temporal contiguity.

Srdjan Ostojic1, Stefano Fusi

  • 1Department of Neuroscience, Center for Theoretical Neuroscience, Columbia University Medical Center New York, NY, USA ; Department Etudes Cognitives, CNRS, Group for Neural Theory, LNC INSERM U960, Ecole Normale Superieure Paris, France.

Frontiers in Computational Neuroscience
|May 4, 2013
PubMed
Summary
This summary is machine-generated.

Synapses encode transition probabilities, crucial for predicting future events in stochastic environments. This finding suggests temporal contiguity is fundamental to neural circuit function.

Keywords:
Markov processesforgettinglearning and memorysynaptic plasticitytemporal contiguity

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Area of Science:

  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Learning in stochastic environments requires predicting future events.
  • Temporal contiguity, the likelihood of one event following another, is key to sequence statistics.
  • Encoding these transition probabilities in memory is vital for adaptive behavior.

Purpose of the Study:

  • To investigate how synaptic plasticity models encode transition probabilities.
  • To determine the relationship between synaptic strengths and temporal contiguity.
  • To explore the read-out mechanisms of this encoded information in neural circuits.

Main Methods:

  • Analysis of a large class of synaptic plasticity models.
  • Mathematical modeling of multi-state synapses with hard bounds.
  • Investigation of synaptic dynamics dependent on pairs of contiguous events.
  • Simulation of information read-out by a rate-based neural network.

Main Results:

  • Synaptic weight distributions monotonically encode transition probabilities under general assumptions.
  • Synaptic convergence to probability distributions is robust to correlations in modifications.
  • The encoding transforms from a smooth function (bistable synapses) to a step function with increasing synaptic states.
  • Information stored in synaptic weights is accessible to simple neural networks.

Conclusions:

  • Synapses generally encode transition probabilities, highlighting the importance of temporal contiguity.
  • This encoding mechanism is likely widespread across neural circuits in the brain.
  • Synaptic plasticity provides a mechanism for learning and predicting event sequences.