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Related Concept Videos

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 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 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 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.
Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
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...

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Related Experiment Video

Updated: Jun 21, 2026

Electrophysiological Investigations of Retinogeniculate and Corticogeniculate Synapse Function
09:09

Electrophysiological Investigations of Retinogeniculate and Corticogeniculate Synapse Function

Published on: August 7, 2019

Recurrent networks with short term synaptic depression.

Lawrence Christopher York1, Mark C W van Rossum

  • 1Informatics Forum, Edinburgh, EH8 9AB, UK. s0570135@sms.ed.ac.uk

Journal of Computational Neuroscience
|July 7, 2009
PubMed
Summary
This summary is machine-generated.

Ring attractor networks with short-term synaptic depression exhibit novel behaviors. These networks can function as tunable oscillators or pattern generators, with activity states influenced by background current.

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Area of Science:

  • Computational neuroscience
  • Neural network modeling

Background:

  • Recurrent connections are abundant in cortical circuitry.
  • Ring attractor networks model diverse neural phenomena like working memory and head direction cells.
  • Existing models often use static synapses, neglecting short-term synaptic plasticity.

Purpose of the Study:

  • To investigate the behavior of ring attractor networks with short-term synaptic depression.
  • To explore the impact of synaptic plasticity on network dynamics.
  • To assess the potential applications of these networks as tunable oscillators or pattern generators.

Main Methods:

  • Simulated ring attractor networks with short-term synaptic depression.
  • Introduced a uniform background current to modulate network activity.
  • Analyzed network states including stationary, uniform, and rotating attractors.
  • Extended simulations to two-dimensional network fields.

Main Results:

  • Network activity can settle into stationary, uniform, or rotating attractor states.
  • The speed of rotating attractors is controllable via background current.
  • Two-dimensional simulations reveal a rich repertoire of network behaviors.
  • Short-term synaptic depression introduces dynamic states not present in static models.

Conclusions:

  • Short-term synaptic depression significantly alters ring attractor network dynamics.
  • These networks can serve as adaptable frequency oscillators and pattern generators.
  • The findings offer insights into neural computation and potential applications in artificial intelligence.