Neural Circuits and Systems

Description of Neural Circuits

The basic connection types between neurons are chemical synapses and electrical synapses. The synaptic connections always consist of dendrites, axons terminals, and glial cells, which together constitute neuropil. In this case, the neuropil between nerve cell bodies is the area where the most synaptic connections occur.

Neurons never operate in isolation but are organized into circuits or systems that process specific types of information. The establishment of synapses allows the formation of many overlapping and interconnected neural circuits. Neural circuits are composed of three basic constituents, which are afferent neurons, interneurons, and efferent neurons. Afferent neurons refer to nerve cells that transfer information towards the central nervous system (CNS), while efferent neurons mean the nerve cells carry information away from the spinal cord or brain. Interneurons are nerve cells that participate only in local aspects of the circuit. In summary, neural circuits are both anatomical and functional entities, the direction of information flow is important to understand the functions.

Principal Types of Neural Circuits

• Diverging circuit - One neuron forms a synapse with multiple postsynaptic cells.
• Converging circuit - Inputs from multiple sources converge to one output, affecting only one neuron.
• Reverberating circuit - In the process of signal transmission from one neuron to another neuron, the signal can be sent back to the initial neuron and eventually produce a repetitive output.
• Parallel after-discharge circuit - A neuron inputs to several neuron chains and finally gathered on an output neuron.

Research of Multiple Neural Circuitry

• Heart-brain's neural circuitry
• Liver-brain's neural circuitry
• Pancreas-brain's neural circuitry
• Lung-brain's neural circuitry
• Vision-brain's neural circuitry
• Anterograde and retrograde tracing

Artificial Neural Network

Inspired by biological neural networks, there are now designs of artificial neural networks. As simplified models of biological neurons, the artificial neural networks can realize logic, arithmetic, and symbolic functions. The classical artificial neural network is composed of three parts, including architecture, activation rule, and learning rule. Like other machine learning methods, artificial neural networks have been used to solve a variety of problems, such as machine vision and speech recognition

Fig.1 Schematic representation of the "brain as a computer" concept.¹

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Reference

1. Martinez, Pedro, and Simon G. Sprecher. "Of circuits and brains: the origin and diversification of neural architectures." Frontiers in Ecology and Evolution 8 (2020): 82. Distributed under Open Access license CC BY 4.0, without modification.

For Research Use Only. Not For Clinical Use.