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Related Concept Videos

Neuronal Communication01:28

Neuronal Communication

Neurons, the fundamental units of the brain and nervous system, communicate through complex electrochemical signals that underpin all cognitive and bodily functions. This communication is primarily facilitated by a process involving the generation and propagation of an action potential along the axon of the neuron. When the internal electrical charge of a neuron surpasses a certain threshold, an action potential is triggered. This rapid change in voltage travels swiftly along the axon to the...
The Synapse02:47

The Synapse

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.
The Role of Ion Channels in Neuronal Computation01:19

The Role of Ion Channels in Neuronal Computation

A postsynaptic neuron usually receives numerous impulses from several other presynaptic neurons. The axon hillock of the postsynaptic neuron integrates all these signals and determines the likelihood of firing an action potential.
Sometimes a single EPSP is strong enough to induce an action potential in the postsynaptic neuron. However, multiple presynaptic inputs must often create EPSPs around the same time for the postsynaptic neuron to be sufficiently depolarized to fire an action potential.
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...
Synaptic Signaling01:09

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.
Most synapses are chemical, meaning an electrical impulse or action potential spurs the release of chemical messengers called neurotransmitters. The neuron sending the signal is called the presynaptic neuron, and the neuron receiving the signal is the postsynaptic neuron.
The presynaptic neuron fires an action potential that...
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: Jul 19, 2026

Presynaptically Silent Synapses Studied with Light Microscopy
11:02

Presynaptically Silent Synapses Studied with Light Microscopy

Published on: January 4, 2010

Optimal information storage in noisy synapses under resource constraints.

Lav R Varshney1, Per Jesper Sjöström, Dmitri B Chklovskii

  • 1Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.

Neuron
|November 8, 2006
PubMed
Summary

This study proposes a theoretical framework for understanding neural synapses, explaining their noise, wide weight distribution, and sparse connectivity by maximizing information storage under resource constraints.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Theoretical Biology

Background:

  • Central synapses exhibit complex properties including noise, a wide distribution of synaptic weights, and sparse connectivity.
  • Previous research has explored these synaptic characteristics experimentally and theoretically.

Discussion:

  • A novel theoretical framework is presented to explain observed synaptic properties.
  • The framework integrates concepts of information theory and resource constraints, specifically neural tissue volume.

Key Insights:

  • The model successfully accounts for synaptic noise, wide synaptic weight distributions, and sparse neural connectivity.
  • It suggests that synaptic weights might change in discrete steps, a prediction consistent with experimental observations.
  • The approach is grounded in maximizing information storage capacity within biological constraints.

Outlook:

  • The theoretical framework offers a parsimonious explanation for key features of central synapses.
  • It provides a basis for further experimental validation and prediction of synaptic behavior.
  • This work could inform future models of neural computation and information processing.