Hierarchical Brain

An explanation of the human brain

First published 1st February 2024. This is version 1.5 published 2nd March 2024.
Three pages are not yet published: sleep, memory and an index.
Copyright © 2024 Email info@hierarchicalbrain.com

Warning - the conclusions of this website may be disturbing for some people without a stable mental disposition or with a religious conviction.

ABCD (Absolutely Basic Coincidence Detecting) neuron

Real neurons in the human brain are complex and varied but they can be modelled for my purposes using a large number of simplified model neurons. I call these model neurons Absolutely Basic Coincidence Detecting (ABCD) neurons. An ABCD neuron has just two inputs and one output and can perform very simple coincidence detection. The purpose of this page is to show how ABCD neurons can model the coincidence detection ability of real neurons.

To be clear, ABCD neurons do not exist in reality, I have invented them as a helpful modelling tool. Using them in diagrams and descriptions makes it very much easier to show how the ability of real neurons to act as coincidence detectors can be the basis of the processing of data in the brain.

Contents of this page
The principle - the theory behind the modelling.
The detail - the details of the proposal.
An example - an example of how a real neuron could be modelled.
References - references and footnotes.

The principle

The detail

An example


References For information on references, see structure of this website - references

  1. ^ This area of research, known as computational neuroscience, aims to discover how the brain encodes information and to develop neuronal models using mathematics, but I think there have sometimes been some problems with the assumptions made about the way the brain encodes information.
    Spiking Neuron Models (Single Neurons, Populations, Plasticity) - Gerstner and Kistler 2002
    doi: 10.1017/CBO9780511815706 viewable online at Spiking Neuron Models or see GoogleScholar.
    This book is a good review of the field at the time it was published. It reviews many techniques used for modelling the neurons in the brain. The two following quotes are from the introductory chapter:
    Page 13-14 under the heading “1.4 The Problem of Neuronal Coding”, first paragraph: “In every small volume of cortex, thousands of spikes are emitted each millisecond.... What is the information contained in such a spatio-temporal pattern of pulses? What is the code used by the neurons to transmit that information? How might other neurons decode the signal? As external observers, can we read the code and understand the message of the neuronal activity pattern?”
    And two paragraphs later: “The concept of mean firing rates has been successfully applied during the last 80 years. It dates back to the pioneering work of Adrian [1920s] who showed that the firing rate of stretch receptor neurons in the muscles is related to the force applied to the muscle.”
    The second quote shows that the firing rate of neurons that are connected to muscles is relevant because it has been shown to be proportional to the force produced in the muscle. It is also true that the firing rate of incoming signals from various senses is important because it gives information about the relevance and strength of the signal. Early researchers probably therefore assumed that the firing rate or pattern (“Spatio-temporal pattern”) of neurons within the brain (“In every small volume of cortex”) was also relevant and that they somehow encoded a particular message or some specific property of a message.
    However, it is very difficult to imagine or identify a mechanism in the brain that could usefully translate a particular rate or pattern of firing into something else that could be relevant. The only thing it could be translated to would be the firing of a single other neuron, which would be pointless, why not just activate this in the first place? The only effect multiple spikes can have is a reinforcement; multiple spikes in quick succession will obviously increase the chances of the target neuron(s) raising action potentials themselves.

Page last uploaded Wed Jan 31 07:24:59 2024 MST