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When an action potential reaches the presynaptic axon terminal, it releases neurotransmitters from the neuron into the synaptic cleft at a chemical synapse. The released neurotransmitter can be excitatory or inhibitory. The critical criteria commonly used to determine whether a molecule is a neurotransmitter at a chemical synapse are the molecule's presence in the presynaptic neuron. Second, its release is in response to strong presynaptic depolarization. And lastly, the presence of...
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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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Force Learning in Balanced Cortical E-I Networks.

Takashi Kanamaru1,2, Kazuyuki Aihara1,3

  • 1International Research Center for Neurointelligence, University of Tokyo 113-0033, Japan.

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|March 5, 2026
PubMed
Summary
This summary is machine-generated.

Force learning, a method for training recurrent neural networks (RNNs), was applied to brain-inspired excitatory-inhibitory (E-I) networks. Optimal E-I balance near chaos maximizes force learning efficiency, suggesting neural cooperation is key for brain computation.

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Area of Science:

  • Computational Neuroscience
  • Machine Learning
  • Artificial Neural Networks

Background:

  • Force learning is a training method for recurrent neural networks (RNNs), a type of artificial neural network, related to reservoir computing (RC).
  • While effective in machine learning, the brain's capacity for force learning remains underexplored.
  • Reservoir computing typically uses fixed random weights, whereas force learning trains these synaptic weights.

Purpose of the Study:

  • To investigate the feasibility of force learning in biological neural networks.
  • To model the cerebral cortex using an excitatory-inhibitory (E-I) network and apply force learning.
  • To determine the conditions under which force learning is most effective in an E-I network.

Main Methods:

  • An excitatory-inhibitory (E-I) network, modeling the cerebral cortex, was constructed with multiple modules.
  • A readout mechanism calculated network output based on filtered firing rates of excitatory neurons.
  • Feedback connections were introduced, and the network was trained using force learning to generate sinusoidal signals.

Main Results:

  • The E-I network exhibited transitive chaotic synchronization.
  • Force learning efficiency was maximized at an optimal E-I balance, near the edge of chaos.
  • The study demonstrated that force learning effectiveness is enhanced by the interplay between excitatory and inhibitory neurons.

Conclusions:

  • The cooperation between excitatory and inhibitory neurons is crucial for effective force learning in neural networks.
  • This finding suggests a potential mechanism for complex dynamics generation in the brain through E-I network interactions.
  • Force learning in E-I networks offers insights into brain computation and learning.