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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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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...
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Emergent spatial synaptic structure from diffusive plasticity.

Yann Sweeney1, Claudia Clopath1

  • 1Department of Bioengineering, Imperial College London, South Kensington Campus, London, SW7 2AZ, UK.

The European Journal of Neuroscience
|May 22, 2016
PubMed
Summary
This summary is machine-generated.

Diffusive neurotransmission, an alternative to synaptic transmission, can shape neural network structure. This study shows how diffusive plasticity creates spatial correlations in neural connections and stimulus preference, mimicking sensory cortex development.

Keywords:
diffusive neurotransmittersneural networkssynaptic connectivitysynaptic plasticityvolume transmission

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Neurotransmitters can diffuse freely, influencing neurons beyond direct synaptic connections.
  • This diffusive transmission offers an alternative communication pathway impacting neural network information processing.

Purpose of the Study:

  • To investigate if diffusive neurotransmission influences synaptic connectivity structure in plastic neural networks.
  • To propose and explore a Hebbian synaptic plasticity mechanism mediated by a diffusive neurotransmitter.

Main Methods:

  • A novel Hebbian synaptic plasticity model where individual synapse modification triggers similar changes in neighboring synapses.
  • Simulations using rate-based neural networks to analyze the effects of diffusive plasticity.
  • Exploration in a feedforward network model for receptive field development.

Main Results:

  • Diffusive plasticity leads to the emergence of spatial structure in synaptic connectivity.
  • This spatial structure coexists with other connectivity patterns, like those from correlated external input.
  • In a receptive field model, diffusive plasticity induces spatially correlated neuronal stimulus preferences, mirroring sensory cortex organization.

Conclusions:

  • Diffusive plasticity is an efficient mechanism for generating spatial correlations in neural networks.
  • This mechanism can flexibly interact with other forms of synaptic organization.
  • The findings provide insights into how neural network structure, particularly in sensory systems, can develop and be influenced by non-synaptic communication.