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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...

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...

Synaptic Signaling

Synaptic Signaling

Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.
Most synapses are chemical, meaning an electrical impulse or action potential spurs the release of chemical messengers called neurotransmitters. The neuron sending the signal is called the presynaptic neuron, and the neuron receiving the signal is the postsynaptic neuron.
The presynaptic neuron fires an action potential that...

Synaptic Signaling

Synaptic Signaling

Neurons communicate at synapses, or junctions, to excite or inhibit the activity of other neurons or target cells, such as muscles. Synapses may be chemical or electrical.

The Synapse

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.

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

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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

Combining synaptic and cellular resonance in a feed-forward neuronal network.

Jonathan D Drover¹, Vahid Tohidi, Amitabha Bose

¹Department of Mathematical Sciences, New Jersey Insitutute of Technology, Newark NJ 07102.

Neurocomputing

|December 17, 2008

Summary

This study introduces a mathematical theory for neuronal resonance, explaining how intrinsic and synaptic properties shape responses. Findings reveal maximal neuronal responses can fall between intrinsic and synaptic resonance frequencies.

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Area of Science:

Computational Neuroscience
Mathematical Biology
Neurophysiology

Background:

Neurons exhibit subthreshold resonance, influencing signal processing.
Synaptic inputs also possess resonance properties.
Understanding the interplay between intrinsic neuronal and synaptic resonance is crucial for comprehending neural computation.

Purpose of the Study:

To develop a mathematical theory explaining the subthreshold resonance response of neurons to synaptic input.
To elucidate how intrinsic neuronal properties and synaptic characteristics interact to determine a neuron's generalized resonance response.
To compare theoretical predictions with experimental data from biological systems.

Main Methods:

Derivation of a mathematical model for neuronal resonance.
Analysis of the combined effects of intrinsic neuronal resonance and synaptic resonance.
Comparison of theoretical results with experimental data from the crab pyloric central pattern generator.

Main Results:

A mathematical framework was established to describe neuronal resonance to synaptic input.
The theory demonstrates that a neuron's resonance response is a combination of its intrinsic properties and synaptic resonance.
The maximal response frequency of a postsynaptic neuron can be located between the neuron's preferred intrinsic frequency and the synaptic resonance frequency.

Conclusions:

The derived theory provides a quantitative explanation for neuronal resonance phenomena.
The findings highlight the importance of considering both intrinsic neuronal and synaptic resonance for understanding neural signal processing.
The theoretical model aligns with experimental observations in biological neural circuits, validating its predictive power.