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Neural Circuits

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...

Long-term Potentiation

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.

Long-term Potentiation

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...

Integration of Synaptic Events

Integration of Synaptic Events

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...

Neuroplasticity

Neuroplasticity

Neuroplasticity reflects the brain's remarkable capacity to adapt and evolve, responding dynamically to learning, experiences, or injury by reorganizing its neural circuitry. This reorganization involves creating new neural connections and refining old ones through a series of biological processes that contribute to the brain's lifelong development and adaptability.

The Role of Ion Channels in Neuronal Computation

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.

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Related Experiment Video

Updated: Jul 19, 2026

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

Recurrent neural network architecture with pre-synaptic inhibition for incremental learning.

Hiroyuki Ohta¹, Yukio Pegio Gunji

¹Graduate School of Science and Technology, Kobe University, Rokkodai, Nada, Kobe, Japan. 001d897n@stu.kobe-u.ac.jp

Neural Networks : the Official Journal of the International Neural Network Society

|September 23, 2006

Summary

This study introduces a novel recurrent neural network for incremental learning. The model preserves existing knowledge by inhibiting incorrect pathways, enabling adaptation to new data without overwriting.

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A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

Published on: June 22, 2015

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Last Updated: Jul 19, 2026

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Real-time Electrophysiology: Using Closed-loop Protocols to Probe Neuronal Dynamics and Beyond

Published on: June 24, 2015

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

A Method for Remotely Silencing Neural Activity in Rodents During Discrete Phases of Learning

Published on: June 22, 2015

Area of Science:

Computational Neuroscience
Artificial Intelligence

Background:

Incremental learning in artificial neural networks faces challenges in maintaining existing knowledge when encountering new data.
Traditional models often overwrite internal representations, leading to catastrophic forgetting.

Purpose of the Study:

To propose a recurrent neural network architecture capable of effective incremental learning.
To address the challenge of unknown consistency between existing representations and new sequences in incremental learning.

Main Methods:

A recurrent neural network architecture is proposed that preserves parallel pathways from input to output.
Incorrect pathways are inhibited by previously activated pathways, allowing the network to explore alternative routes.
The model avoids overwriting existing internal representations.

Main Results:

The proposed network demonstrates performance in incremental learning scenarios.
The inhibition mechanism allows for adaptive pathway selection without catastrophic forgetting.
The model's approach contrasts with state-space integration views of brain function.

Conclusions:

The developed recurrent neural network architecture facilitates robust incremental learning.
This pathway-based inhibition approach offers a viable alternative to traditional incremental learning methods.
The model shows potential for extension to higher cognitive functions like decision-making.