Trying to develop a Computer model of a biological Neural Network

Jeff Best jeffb at jtbest.demon.co.uk
Sat Sep 12 08:53:20 EST 1998


In article <6t84i1$2es at pmsnnews.best.ms.philips.com>, R. Storm
<rstorm at best.ms.philips.com> writes
>I’m very much fascinated by biological Neural Networks. But because I’m
>a computer scientist, and not a biological or medical, I don’t know a
>great deal of the biological aspects of Neural Networks. I’ve made a very
>simplified requirement specification of a computer model for Neural
>Networks.
>I would like to ask someone to take a look at it (form a biological point
>of view), and tell me what else I need to know, or what’s wrong in it.
>
>Any other comments are welcome

>
>Thanks, Ray
>

This is a field of some interest to me. I am a software engineering
consultant with interests in AI and simulation. 

Your model seems to be more comprehensive than mine. The following is an
unfinished paper I cobbled together a while back (it isn't very good).

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`
end

sum -r/size 26442/9338 section (from "begin" to "end")
sum -r/size 7325/6755 entire input file



>- Synapses are made during in the early life of a organism. Probably
>  when that neuron is stimulated enough connections are made with nearby
>  neurons. These connections are not reversible.

I read this week (New Scientist) that neurotrophin secretion influences
axon growth towards the secreter while semaphorin repulses the growing
axon. Secretion by neihbours can "steer" axon growth to "distant"
neurons. Cyclic nucleotides (cGMP / cAMP) can reverse these trophisms.
I've started looking at how the connectivity can be simulated. Is it
hard-wired in the genome or influenced by earliest experience, or a bit
of both.

>
>1.1.2 Information about biological neural networks
>--------------------------------------------------
>
>- Over 100 billion (10^11) neurons in a human brain.
>- Maximal 1000 synapses on the in & output of a human neuron.
>- Parallel processing.
>- Has a way of preventing information overloading or loss.
>- All neurons are positioned in a 3d space.

Simulating this bit alone is fun. I'm trying out a tetrahedral
hypergraph, with each tetrahedron splitting into smaller regions of
space. Each neuron "knows" which region of space fully encloses it,
allowing some "growth" and axon-development without having to do lots of
3D geometry.

>- Sensors are simply attached to the dendrites of a group of neurons.
>- Actuators are attached to the axon of a group of neurons.
>
>
>1.2 Simulation of Biological Type Neural Networks
>-------------------------------------------------
>
>To successfully design a simulated model of a biological neural network
>it's necessary to discuss the way of implementation of al the in paragraph
>1.1 mentioned properties:

I think there is a "fluid" in which the neurons sit. This is rich in
ions (Potassium, Sodium, etc). There is some flux between surrounding
fluid and neurons. This may just influence mood. It may the basis of
discharging neural state after receiving signals inadequate to fire a
response. It may also provide a pool (pun unintended) of resource which
allows a delayed firing based upon earlier signal receipt and an influx
of ions.

I would expect there to be an ion gradient, possibly allowing
dissipation of "mood" effects. Concentrations in the neural fluid may
also act as inhibitors to firing.

>
>1.2.1 Computational Model of a biological neuron
>------------------------------------------------
>
>- Each neuron acts on its own.
>
> This is a big problem if the model is designed for a computer with a
> single CPU. So there must be a timed method of the Neural Network class
> which can perform the actions for each neuron. This can be done in several
> ways, the best way will probably be:
> Each neuron that has been stimulated sets a flag, which indicates it
> needs to be updated. Then the method that updates the network processes
> in a standard pattern all neurons.
>
>- A neuron collects signals at it's synapses by summing all excitatory
>  and inhibitory influences upon it.
>  If the excitatory influences are dominant the neuron sends a message
>  to other neurons. This is decided by the neuron function. (e.g. step,
>  ramp, sigmoid or gaussian)
>
> When a neuron threshold value is exceeded, it uses it's numbered list with
> synapses containing pointers to next neurons. When updating a triggered
> neuron send a signal to all other neurons to which it is connected.
>
>- The neuron value will degrade through time, otherwise threshold will
>  always be exceeded.
>
> Two datamembers of the Neuron class will keep track of the reduction of
> the neuron value. The write-off delay will indicate the number of update-
> cycles there are before reduction begins. Another keeps track of the
> number of cycles that has passed since the last update. The power of
> reduction is determined via the reduction function, which can very much
> look like the neuron threshold function.
>
>- An axon carries information through series of action potentials.
>
> These series can and probably will occur between several update-cycles,
> when the neuron is triggered multiple times.
>
>- A synapse represents the junction between an axon and a dendrite.
>
> A synapse is represented either by a class or just by a list or array
> of dendrites with pointers to connected neurons.
>
>- Synapses are made during in the early life of a organism. Probably
>  when that neuron is stimulated enough connections are made with nearby
>  neurons. These connections are not reversible.
>
> When a neuron is stimulated enough, it may create a synapse with the
> most nearby neuron (possibly the one with the least activity).
> This could be made reversible. The synapse is removed by a neuron, which
> wasn't stimulated enough, and so it removes all synapses, using a
> datamember that keeps track of it's activity.
>
>1.2.2 Computational Model of biological neural networks
>-------------------------------------------------------
>
>- Over 100 billion (10^11) neurons in a human brain.
>
> This is possible, but will use lots of memory an CPU-speed, even though
> a computer is faster. Because the computer use the advantage of parallel
> processing.
>
>- Maximal 1000 synapses on the in & output of a human neuron.
>
> This can be limited by a Neuron datamember, which represents the maximum
> number of synapses.
>
>- Parallel processing.
>
> See paragraph 1.2.1, section: "Each neuron acts on its own".
>
>- Has a way of preventing information overloading or loss.
>
> The way of how information overloading is handled is described in para-
> graph 1.2.1, section "The neuron value will degrade through time,
> otherwise threshold will always be exceeded.".
> For what concerns information loss (or forgetting). Humans have short
> and long term memory. It probably has something to do with the degeneration
> of the synapse connections (caused by aging of the organism). and neuron
> thresholds (with their write-off delay). The long and short term memories
> can be simulated by gives groups of neurons different neuron value
>write-off
> delays.
>
>- All neurons are positioned in a 3d space.
>
> The neurons will be ordered in memory (through arrays or lists) in
> a box-like representation, of which the length, height, depth are
> variable.
>
>- Sensors are simply attached to the dendrites of a group of neurons.
>
> Add a physical attribute to the main application (organism) to connect
> input organs (keyboard, bitmap) to the neural network. The neural network
> must have means (read: a method) to connect the sensor to the neural
> network.
>
>- Actuators are attached to the axon of a group of neurons.
>
> Add a physical attribute to the main application (organism) to connect
> output organs (screen, sound, printer) to the neural network. The neural
> network must have means (read: a method) to connect the actuator to the
> neural network.
>
>
>                            ---=== End ===---
>
>

-- 
Jeff Best
jeffb at jtbest.demon.co.uk




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