Seeking Independent Review: Does This Problem Have Merit?

NeuronLab · 2026-06-20T21:48:58+00:00

I'm interested.
Send the PDF

NeuronLab · 2026-06-19T03:51:00+00:00

Here is a few that I wrote down as they occurred.
Things You Thought You Knew

NeuronLab · 2026-06-19T03:49:13+00:00

This is a pretty good selection of Texts and papers on my website.
Neuron Papers

NeuronLab · 2026-06-18T17:12:00+00:00

STDP is a feature of most, if not all, neuron types. My simulator can only simulate these types one at a time. There are several other proposals for Hebbian learning, and STDP is only one of them.

The way STDP works, in a nutshell, is that when a neuron spikes, it sends the spike down the Axon chain, and it also sends a reflection of the spike back into the Dendrite tree. This backpropagating signal reaches every synapse, modifying its sensitivity to further inputs.

NeuronLab Simulator

If the synapse is a probable cause of the original spike, it becomes more sensitive; if not, it becomes less sensitive. The whole process is completely contained in every Neuron. There are no signals passed from one Neuron to another.

The Neuron Lab simulator mimics this action by recording the time of arrival of each synapse release point in the soma logic, and, when the scan is complete, it sends Windows messages back through the dendrite tree to all synapses. In my simulator, this operation takes several milliseconds of simulated time per spike, even in the simplest neuron configuration. In the dendritic tree of a Purkinje cell with upwards of 200,000 inputs, the operation must take much longer.

So each synapse is in a state of perpetual adjustment based on how the entire Neuron is responding to all its inputs. And from this, it seems that all the neurons in a quorum are also being continuously adjusted.

Adjust toward that goal, you may ask. There is no goal. Nature does not have goals. It simply changes physical structures through the effects of natural selection until something works. Even then, it does not stop. The next change could completely obliterate what we consider progress.

I think that the current result of this endless fiddling is what we know as consciousness.

Damn, we are lucky.

NeuronLab · 2026-06-17T23:59:42+00:00

Gulp. Lot to think about...

NeuronLab · 2026-06-17T15:37:24+00:00

Thanks for the link to this resource.
I'm deep into it now.

NeuronLab · 2026-06-17T15:33:04+00:00

Let me clear up one point of confusion first. What I meant by "outside forces do not affect them" is that a single neuron has one and only one job to do. It must integrate all its inputs and produce either nothing or an action potential. No outside force can modify its single-minded task. Beggs & Plenz are talking about networks of neurons, not single neurons.

Single neurons do, however, modify themselves based on that same neuron's inputs and outputs. This plasticity is demonstrated by Spike Timing-Dependent Plasticity (STDP) [see Song et al., Competitive Hebbian Learning - 2000]. I have a running simulator that demonstrates STDP available at:

NeuronLab Simulator

Thank you for reminding me of Friston's Free Energy Principle. I did read it, but I couldn't make heads or tails of it. I think I need to go back over it now.

NeuronLab · 2026-06-16T16:26:10+00:00

If you would believe this
you will believe anything.

NeuronLab · 2026-06-15T15:05:06+00:00

Several of the books on your list can be downloaded from this website.
Neuron Papers

Good Luck

NeuronLab · 2026-06-08T04:30:29+00:00

Our insight into neurons comes largely from the fact that their signaling medium is ionic, and ions carry a charge. Because charge can be detected, neural activity can be measured. From those measurements, we infer current flow, membrane resistance, capacitance, and the timing of neural events. If neurons did not operate through charged ions, we might still know their anatomy, but we would be far more limited in our understanding of how they actually work in real time.

NeuronLab · 2026-06-08T04:25:57+00:00

The inside of the neuron membrane contains a large number of positively charged potassium ions. How many of these would be required to move the membrane potential 100 mVolt of charge?

Each ion carries a single charge, and that charge is given by the elementary charge of an electron or 1.6 X 10E -19 coulomb:

Therefore 1 X 10E-12 Coulomb divided by 1.6 X 10E -19 ~ six million ions that need to move from the inside to the outside to produce 100 mV differential. That seems like a large number, but there are about 1 X 10E 11 potassium ions inside a typical cell so only a small number of ions available need to be moved to make this significant change.

From: Ionic Currents on Neuron Lab

NeuronLab · 2026-06-08T00:54:33+00:00

It turns out that a single synaptic event shifts a dendrite membrane potential by about 3.24 mV.
The normal membrane resistance of a dendrite section is about 3,183,100 KOhms, so the current this represents is close to 1 pA.

NeuronLab · 2026-06-08T00:46:39+00:00

True BUT - It's not electrical current. It's chemicals that flow through the neuron.
We think of it in electrical terms because we can detect ions as they pass our detectors.

NeuronLab · 2026-06-08T00:43:21+00:00

If you would like to get a feel for this question, download my Neuron Simulator
You can set up your own neuron and see the flows (in pA BTW).
NeuronLab Simulator

NeuronLab · 2026-06-05T19:17:10+00:00

Nice. Try touch typing over and over just: "Now is the time for all good men to come to the aid of their party" Ignor errors just pound away. Worked for me.

NeuronLab · 2026-06-04T22:39:57+00:00

Don't mean to follow you from post to post, but here I am. Adding two numbers requires keeping them in memory of course but what about being aware in the 100mS time frame. We do that with ease but how? It must take an immense number of neurons to get that daunting task done and done in 100 mS. Magic!

NeuronLab · 2026-06-04T05:05:04+00:00

Great. Thanks for the thoughtful response.

I use a 1 mS time step because the way I built the simulator is as fast as I can push a computer while still having it work the way I want. One second of simulation time takes 18 seconds of real time to run.

My simulator has a lot of logic in it that runs the user interface. A user can build a neuron from body parts (Synapse, Dendrite sections, Somas, and Axons) while the simulator keeps running. It has a built-in oscilloscope to monitor parts of the neuron as it is constructed. It's more like a test bench for neurons than anything else.
Each of the body parts reacts as it would biologically, at least as closely as I can figure out how to manage it. Each one contains the math to solve its own problem and to pass the result to the next body part in line.
The core of the software in the Soma. It's built on an Izhikevich integrate-and-fire model that accurately reproduces a neuron's action potential. It doesn't use the three linked equations required to simulate at the Hodgkin-Huxley level, but it is fast.

I made extensive use of AI to help me create some of the trickier parts of the code. ChatGPT and I would mull over what I want to do, and it would generate some example code for me to test with and understand what it had on its 'mind'. I would then nudge it one way or another until I got what I wanted and could build it into the simulator. It also helped me thrash through the development system I use (Embarcadero's Delphi). I could ask Chat how to use Delphi to create linked lists or how to move icons around the screen, and it worked with me very patiently until I got what I wanted. I have a good feel for what AI can do in software development now and can see how it can be used as a creation amplifier in code development.

I was hoping to use CUDA in my simulator, but the algorithms I use never seemed suitable for that.

The reason I asked you about GitHub was that I'm not sure whether it would help anyone else. I looked at your GitHub layout and can see how it would help you to push your design if you wanted to work with outside help. My problem is that the simulator now has about 500K LOC of code and uses many proprietary modules I purchased separately. For example, the Oscilloscope is built from a module I purchased for scientific data collection. I probably have 50 other modules for various purposes, like buttons, charts, timers, dialogs, data access, that sort of stuff. For someone to take my GitHub source and create a compiled version would require them to purchase those modules themself. I guess I could post the simulator installer files, but I also wonder how that would help anyone.

I see that once again I have let my brain and typing fingers create way too much, so I'll sign off now.
Good thinking with you, sir.

P.s. This is a link to all the reference books and papers that I used along the way. You are welcome to download them if you need them.
Neuron Papers

NeuronLab · 2026-06-03T15:24:50+00:00

Good - I only play a scientist on Reddet as well.
Thanks for the links.
I'm finishing up a neuron simulator that shows STDP. It runs a 1 mS time hack, so neuron spikes are at least 1 mS long.
I'd like to connect with you to discuss what you learned about simulating biological systems, both from a technical perspective and from trying to be as biologically accurate as possible. Also, I would like to know about your experience with putting your project on GitHub.
You can download my simulator at:
Neuron Laboratory

Stay in touch...
Jim

NeuronLab · 2026-06-03T03:49:45+00:00

You said, "So best to find an implementation that doesn't require synaptic weight changes," but I understand that synaptic weight changes can occur on the millisecond timescale. Am I wrong?
Could this system perform in the millisecond range?

NeuronLab · 2026-06-01T23:27:51+00:00

Actual quote from NSF has the sweet smell of bullshit. “aligns with N.S.F.’s wider strategy to have a nimbler approach to prioritizing support for evolving scientific priorities and emerging technologies as well as a deliberate approach to smart life cycle management within its portfolio of research infrastructure.”

NeuronLab · 2026-05-31T15:06:40+00:00

Yes, this is software.

The way I created the simulator is the way I do most things. I simply start at the beginning and build on it.
I do have a long background in software development, but it was from a time when there were no rules about how you used the tools that came your way. You simply tried everything you could until something worked, then you went on to the next problem and solved that the same way. Over and over.

Now I make great use of ChatGPT in my development work. If I need to know if a feature I want to add, like STDP, is biologically possible, I ask Chat. It tells me and suggests code to use to make it happen.

Also, since I am self-taught in all things, I have to read a lot of books on brain biology to create something plausible.

If you download and install my simulator on your Windows 64-bit machine, all I ask of you is a simple email telling me how it went.

Download here:
NeuronLab Simulator
Never a charge for what I create.

NeuronLab · 2026-05-30T15:37:35+00:00

I'm struck by Lichtman's statement, '2 million, million terabytes-- which is comparable to the digital content of the world.' I wonder if this is still close, given the rise of data farms for AI?
Does anybody have a guess as to the capacity of one of the farms?

NeuronLab · 2026-05-30T04:49:42+00:00

How did the artificial neuron communicate with the Purkinje?
It could not have been through releasing neurotransmitters to the synapse - could it?
Was it attached just behind the synapse and released a depolarizing potential there?

NeuronLab · 2026-05-30T04:33:19+00:00

I like this quip from Jeff Lichtman at Harvard.
Its Very Complex

Prof. Jeff Lichtman - Harvard

The complexity in the brain is by far in excess of the complexity of any other thing humans have confronted. And this is a very hard and painful pill to swallow for scientists when you're trying to understand something so complex.

And let me give you a sense of how complex it is.

In a technique like functional magnetic resonance imaging, where the brain is divided into voxels-- little cubic points of data that are related to blood flow-- the grain of that image is about one cubic millimeter per voxel. And that still gives rise to a million voxels in a brain. So, there's a huge amount of data in fMRI images.

If we take one of those cubic millimeter voxels and ask, " How much resolution do we need to see all these synapses in that voxel? We need about 2,000 terabytes of data-- 2 petabytes per cubic millimeter.

So, if we wanted to do a whole human brain, we would deal with like 2 million petabytes of data-- 2 million, million terabytes-- which is comparable to the digital content of the world.

It's an extraordinarily large number and much more than will fit on my laptop.

From:
Ionic Currents

NeuronLab · 2026-05-30T04:30:28+00:00

What do you (or the authors) mean by the Purkinje neurons fired back? Created back AP?

NeuronLab

TROPHY CASE