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Related Concept Videos

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.
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A synapse is a specialized structure where two neurons connect, allowing them to pass an electrical or chemical signal to another neuron. It is the point of communication between neurons. The term "synapse" is derived from the Greek word "synapsis," which means "conjunction." The entire process of neural communication revolves around the synapse. When activated, a neuron releases chemicals known as neurotransmitters into the synapse. These neurotransmitters cross the synapse and bind to...
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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
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Long-term Potentiation01:35

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Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...

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Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond
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Optimal learning rules for discrete synapses.

Adam B Barrett1, M C W van Rossum

  • 1Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, United Kingdom. abarrett@inf.ed.ac.uk

Plos Computational Biology
|December 2, 2008
PubMed
Summary
This summary is machine-generated.

Biological synapses have discrete weight states, impacting memory storage. This study finds discrete synapses can match continuous synapse capacity, challenging assumptions about learning and storage limits.

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Area of Science:

  • Neuroscience
  • Computational Biology
  • Information Theory

Background:

  • Biological synapses are believed to possess a finite number of discrete weight states.
  • This contrasts with theoretical models using unbounded, continuous weights, affecting memory overwriting dynamics.
  • Prior research has debated the implications of discrete synaptic weights on learning and storage capacity.

Purpose of the Study:

  • To quantify the information storage capacity of discrete, bounded synapses using Shannon information.
  • To optimize learning rules for discrete synaptic systems.
  • To determine how factors like synapse count, state number, and coding sparseness influence maximum information capacity.

Main Methods:

  • Calculation of storage capacity for discrete, bounded synapses based on Shannon information theory.
  • Optimization of learning rules tailored for discrete synaptic weights.
  • Analysis of the relationship between information capacity and key synaptic parameters.

Main Results:

  • Storage capacity of discrete synapses was calculated in terms of Shannon information.
  • Learning rules were optimized for discrete synaptic systems.
  • Maximum information capacity was found to be dependent on the number of synapses, synaptic states, and coding sparseness.

Conclusions:

  • Discrete, bounded synapses do not inherently possess lower storage capacity than continuous ones.
  • Below a critical number of synapses per neuron, storage capacity is comparable to systems with unbounded, continuous weights.
  • Findings suggest discrete synaptic architectures may support substantial information storage, aligning with biological observations.