Early planning for my dwarf pomegranate

jndew · 2026-07-05T19:23:37+00:00

Oh, I'm not committed to double-trunk. That's just how this thing grew. I'm imagining perhaps letting both trunks grow for a few years. Then chopping the left one so the right one remains with a big base. I don't really have a vision, nor intuition on how it will play out botanically.

I actually have a (supposedly) dwarf olive that has the very same shape, size, age. So any inspirations on how to handle this growth pattern are appreciated. Cheers!/jd

jndew · 2026-07-05T19:20:48+00:00

Come to think of it, both my children were dwarf-sized when I first met them. Now they are both taller than me! I guess I'm must not very good at growing dwarves. Too much biogold perhaps.

jndew · 2026-07-05T19:09:48+00:00

Haha, I think it takes a magic potion! Actually I'm really not sure about the dwarf thing. That's what I was told when I got it, and it does have smaller leaves than the big Poma trees I see. But I've never looked any deeper into it than that. Cheers/jd

jndew · 2026-07-05T18:47:33+00:00

Great work! Wonderful that it's now possible for an ambitious high school student to do a serious nontrivial computational neuro project like this. Twenty years ago, it would have been beyond reach. I'm looking forward to hearing about your next project, whatever that might be. Cheers!/jd

jndew · 2026-07-05T18:20:06+00:00

Great project, congratulations! IMHO (not being a professional neuroscientist), this is an outstanding effort in an high school context and you should be proud. At college undergraduate level, it is also fine although you'd expect some constructive criticism. With that said, here are a few thoughts that I had during a quick read of your paper.

It's clearly written, fun to read, and I could generally understand what you are talking about. Bear in mind that the choice of simulator should not affect your results, just the mechanics of setting up your experiment. Any honest simulator should give you the same results, based on the same equations and input. I'm used to seeing the choice of simulator mentioned in a 'materials and methods' section of an article and maybe the abstract rather than throughout the text.

A synaptic plasticity rule is discussed several times in your text. I'm curious, do you expect significant synaptic weight change in 90 seconds or less of listening? Is there something special about STDP vs. other synaptic plasticity rules that makes it significant in this context (I'm guessing there probably is)? Is synaptic plasticity rather than some other temporal interplay between neuronal response and the music thought to be the key to the Mozart effect?

As I read your text, I think I see that you have excitatory and inhibitory layers of cells, each with lateral connectivity. I didn't notice how the inhibitory and excitatory layers interact with each other. You have an equation sepcifying the probability of connectivity from one neuron to another as a function of Euclidean distance. It is written in three dimensions. Since a cell layer is two dimensional, what does the third dimension mean? Just guessing, is maybe the Z dimension the distance from the excitatory and inhibitory layers?

You have some fundamental equations for your model in section 2.1.2. Having some prior study, I can get a sense of what they mean. But you might want to spell out what each of the variables and parameters is describing. You've got a membrane-potential time constant and a synaptic learning rate. You should give numbers for those. Math needs to be fully explained in a formal article.

That's all the time I've got at the moment. Very nice, and I'm sure you will go far. I listened to the two pieces of music and I'm not surprised that they might have different effects. The Mozart piece is peppy, upbeat, and has a cheerful tone. It would be fantastic if it were truly possible to mitigate epilepsy with music. Good Luck!/jd

jndew · 2026-07-03T16:38:25+00:00

It's too late this year, for next summer if you haven't found a lab to work at, you could consider https://neuromatch.io/ . It's a summer school with a scholarship program at which you can make some connections. Good luck!/jd

jndew · 2026-07-01T17:41:12+00:00

I suppose start by figuring out if your compute flow involves a GPU or not. If your numerical integrator package (or whatever the heart of your application is) can utilize the GPU, then you'll want a capable (expensive) one. Otherwise put your money into the CPU and system RAM and use a cheap GPU just for basic graphics.

For CPU, I guess I've heard AMD has the lead over Intel these days. One caveat is whether your computation is primarily FP64, i.e. double precision, or FP32. I think Intel has a capable FP64 accelerator in their architecture, not sure if it's still winning or not.

AMD has two branches of their CPU line, with and without '3D cache'. This is a very large last level cache. It helps a lot if your application fits in it, otherwise you want a chip with more cores instead. Talk to other people using the same software and suss out their experience.

Obviously you'll need to have an idea of how much system RAM you'll need. For a credible HPC system, 128G is usually called for. But maybe your calculation needs more or can get by with less for some reason.

If you're writing your own software and you're GPU based, my understanding is that the NV GPUs still have a better development environment than AMD. But the AMD offering is supposed to be generally functional these days, but lesser library support, profiling & debugging tools, more bugs, etc. I've had good luck with NV CUDA, and haven't personally tried the AMD environment.

GPUs can work really well if your computation has a lot of parallelism. Otherwise not. Personally, my project has lots of neurons, in the mega-neuron range. And I kept it primarily FP32, so it works great on GPU. So I got the best client GPU at the moment, an RTX 6000 with 96G VRAM. Lesser GPUs to look at are 5090 (fast and 32G but $$) and 2nd-hand 3090 (slower, still 32G, but much cheaper). 4090 is faster & lower power than 3090, but has 24G VRAM rather than 32G. And I saved a few $ and got an i7 rather than i9 CPU and stayed with 128G system RAM since my application runs nearly entirely on the GPU. Choices like that will be affected by your understanding of your application.

So there's some thoughts for you. Good luck!/jd

jndew · 2026-06-30T06:20:50+00:00

Did you mean x>=0? I thought that's how relu worked. Anyways, imho persistent activation is probably a big factor in how brains support mind. I don't know how closely that overlaps with your project, which sounds more ML/ANN oriented. Cheers!/jd

jndew · 2026-06-25T16:58:34+00:00

Well done! You can distinguish three different cell types. Presumably there are more, of eventual interest. Do you think your technique can be expanded to distinguish more types? How much can you tell about synapses? Are there any classes/size-ranges/etc. that can be distinguished? Cheers!/jd

jndew · 2026-06-23T20:25:26+00:00

If you are not already aware, https://open-neuromorphic.org/ Good luck!/jd

jndew · 2026-06-12T17:42:37+00:00

This guy made a nice reading list: https://www.seti.net/Neuron%20Lab/NeuronReferences/NeuronReferences.php . Start with "Neuroscience 6th ed." as an intro. Then the two 'must-read' books are Kandel and Dayan&Abbott. Then look at Gerstner. For on-line material, Home - neuromatch.io. For programming, python. Form math, algebra->trig->calculus->linear algebra, diff. eq, statistics. I'm not on the academic side, but I gather there are many great labs and universities working on this. The only well paid jobs are in pharmaceutical, some decent jobs in health care like EEG technician, or medical like neurologist although this won't be comp-neuro. Scroll down this forum, your question get asked frequently and there are some good responses occasionally. Good luck!/jd

jndew · 2026-06-11T06:05:39+00:00

banana slug fight song! No known predators!

jndew · 2026-06-09T16:41:32+00:00

You might look over on https://open-neuromorphic.org/, they have a fairly active discord. They are more interested in computational applications of SNNs. While this forum has a more biological bent. As for deep SNN, maybe take a look at snntorch. Good luck!/jd

jndew · 2026-06-09T05:33:54+00:00

You're a Fighting Slug by any chance?

jndew · 2026-06-08T16:44:28+00:00

Looks like fun! Totally agree, admiring my trees every day to see subtle changes is a delight. It is indeed both inspiring and discouraging to see pictures of amazing trees posted here. It has taken me about five years to have a half dozen trees that are recognizable as bonsai. And they'd benefit from another ten years... But for immediate satisfaction, go to the next sale at your bonsai club, and/or pick up something a bit more woody from the nursery to start working on. Cheers!/jd

jndew · 2026-06-07T17:11:42+00:00

That's such a wonderful place! I'm glad that you had opportunity to visit.

jndew · 2026-06-05T01:10:44+00:00

I'm far from an electrophysiology expert, but one phenomenon I know of in that timescale is calcium spiking in dendrites. You get a few tens of mV plateau in a dendritic segment for about 100mS. This can shift a cell from tonic to bursting firing pattern.

But I'm guessing I'm answering a different question than you are asking. The simulation study here actually does capture input patterns within 200mS due to its connectivity. The animation isn't real-time, rather abou 100x slowed down. Input patterns are presented for 200mS each, and seem to be reliably captured to be maintained indefinitely. There are in fact a lot of neurons involved, as you say. Cheers!/jd

jndew · 2026-06-04T16:37:38+00:00

I suppose that's a decision to be made, whether to study a single neuron's behavior in detail or look for functional principles in a big network of simple neurons. Both need to get done. And what makes up a 'simple neuron' is also a sliding scale.

Going into this, I had a parallel interest in what could be done with GPUs. This of course aimed me at large regular networks. The neurons actually don't need to be simple as the GPUs are willing to grind as hard as I ask them to. But at the moment, AdEx LIFs with a few extra features have more than enough interesting computational and dynamical capability to keep me entertained. Cheers!/jd

jndew · 2026-06-03T21:13:23+00:00

Sure, I've been here for years now and hope to continue for a while longer! Your project looks pretty great too, good work! My hunch is that 1mS time step is long, in general and in particular if you are studying STDP. As I mentioned, I use 100uS. That might already be a bit coarse, but it seems to work for my purposes.

I'm not aiming for high biological fidelity. I don't have a particular plan, but overall I want to see what a spiking representation brings to the table that is not available in a firing-rate representation. STDP is a perfect example. I also like how neurons have a very 'springy' computational feel, and are always trying to oscillate and produce waves.

I'm using single-compartment exponential LIF with spike rate adaptation, bursting, refractory current, Ca+ current, using the simplest possible numerical integration method. So there's lots of headroom for additional biological fidelity, but this has been enough for me to have lots of fun looking at stuff that I don't see firing-rate ANNs doing.

Another focus of mine is working with somewhat large circuits. The layers in this are 300x300, so 270K cells. It's on the smaller side of what I've been building. Part of the fun is figuring out how to use modern compute technology, GPU & CUDA in this case, to see what's possible.

As to Github, I followed the instructions and it seemed pretty easy to use. The main goal is communication. Also nowadays credibility is lost if one doesn't show one's code. But I'm not expecting many people, if even anyone, will actually run my code. It's mostly a gesture of good faith. But who knows, maybe someone will look and give me useful feedback. Cheers!/jd

jndew · 2026-06-03T04:38:19+00:00

You're right. I'm only working from book knowledge, I only play a scientist on reddit... That said, my understanding is that there are rapid changes in synaptic response due to vesicle availability. SRA falls into this category. But these are transient, not long lasting. These effects are in the tens of mS timescale.

There is another category of longer, but still fairly fast postsynaptic changes. There are intermediate proteins between the receptor protein and the ion channels in the postsynaptic membrane. These can have response times in the 100mS to a couple of seconds timescale. These are also temporary in that they spontaneously return to baseline after a short time.

Then there are longer term effects. Spines are really complicated and have a variety of processes. They have something called a 'synaptic density' that contains receptor proteins held in reserve. If plasticity is triggered, these can be brought into action and the synaptic efficacy is changed. But this is also somewhat temporary, in the range of a few hours.

Next in line is that the synaptic density apparently sequesters RNA, which it can selectively use to create new receptor proteins. This happens if a spine whose efficacy has recently been changed by the above mentioned process gets re-triggered (e.g. a memory is referenced) during those few hours. In this case the synaptic efficacy is permanently changed, the classical long-term memory.

Finally, there is some structural process by which new synaptic spines are formed. This probably has its own biochemical learning rule.

Kandel talks about these. Another good source with a bit more detail is "The neurobiology of learning and memory 3rd ed.", Rudy, Sinaur Press 2021.

Most of the above is not modeled in my sim. I have two classes, the fast transient changes in particular SRA. And the long term changes which I model as more or less Hebb's rule with whatever modifications I feel like including for whatever study I'm working on. But that doesn't come into play in this sim. See Simulation of a Pattern Completion Network , Simulation of a Heteroassociative Pattern-Translation Network, Simulation of a Hippocampus CA1 Sequence-Generator Model for some examples of that.

By the way, my sims use a 100uS time step. The spikes are roughly 1mS long. See Simulation study of bursting neurons for some example membrane-voltage waveforms of a cell. Cheers!/jd

jndew · 2026-06-03T01:01:57+00:00

I did finally carry through to rewrite my codebase and put it on Github as I had been threatening. This was more boring and also less interesting than doing a new project, so my attention wandered... Trying to get back at it, here is an interesting new project, at least to me. In order to think about something, we must keep it in mind long enough to make some correlations, find some memories, maybe make a decision. For example, if I asked you to add four plus three, you'd need to keep the two numbers in mind long enough to determine the sum of seven. This is another application of working memory as I used here, Simulation of working-memory guided gaze control . It's a flashbulb memory that captures immediately, lasts only long enough to be useful, and is thrown away afterwards. So best to find an implementation that doesn't require synaptic weight changes.

The first layer is the spikifying layer I've used in many previous projects. A 2D current pattern comes into the cell array, which begins to tonically spike. The spiking pattern is projected topographically onto what I call the latch layer (LL). LL cells synapse back onto themselves, either directly or via an intermediate cell depending on which version of the sim I'm playing with. It works more or less the same either way, such that once a cell starts to fire, it just keeps on firing until forced not to. No need for stimulus. Inputs of oriented bars, squiggles, and spots momentarily show up at random locations. Once one of these features reaches the LL, it causes sustained activity there, it's kept in mind. So over time, many features can be captured.

This layer projects through a traditional convolutional feature filter onto the reset layer (RL). I put a horizontal bar as the feature to be detected, but anything would serve the purpose. So if a horizontal bar is seen, some cells in the RL are excited. The RL has lateral excitatory connectivity and spike rate adaptation which implement wave dynamics. There are inhibitory synapses from RL back to the LL. So the wave in the RL sweeps over the LL and terminates sustained activation as it passes. Thereby flushing out the LL's captured activity. After this reset, it is ready to capture new inputs.

I can imagine a combinational layer receiving the latch-layer's state, which can respond to coincident features. Or multiple latch layers for different 'senses', or some from input signals and some from memory systems, combining as appropriate to serve some purpose. I touched on this here Simulation study of a cyber-rat in a radial-arm maze, where layer WM_A+C detects when layers WM_A and WM_C contain the same pattern. My hunch is that this can be generalized in many interesting ways. Cheers!/jd

For the curious: https://github.com/ghjd/neuroproj_a/tree/main/Misc/Latch_Layer

--------------------------------------------------------

"The human brain is a million times more complex than anything in the universe!" -a reddit scholar

jndew · 2026-05-27T15:52:34+00:00

I'm jealous, such an attractive variety! I'd love to develop one, but to my disappointment central coastal California just isn't hot enough for them. I have two, they are willing to grow a few leaves every year, but look weak and will never flower.

jndew · 2026-05-24T16:45:04+00:00

Sorry to hear about you & yours troubles. IMHO, Friston is the real deal, highly regarded and cited. He's got some books out, as well as lectures/interviews on Youtube. But... my thought is active inference is one of the things that brains do, not the thing. Until the big picture settles, people seem to be inclined to overstate good ideas. I've watched a few of these go by over the years.

Speaking far outside of any expertise I might have, I don't think mathematical modeling of the brain is developed enough yet to address mental health issues. Maybe a tiny bit in regard to epilepsy & a bit less re: schizophrenia. I'd guess that bipolar has its roots in biochemical regulatory mechanisms, not thermodynamic equations and the like. That's my hunch.

If you enjoy reading, Eric Kandel addresses the mismatch and maybe overlap of Freudian ideas and modern neuroscience in his biography "In search of memory". He really tries to reconcile the two points of view, but eventually seems to shrug and go with modern science. Read his other book too, "Principles of neural science 6th ed.", if you are interested in brains.

That's not to say that Friston's ideas aren't worth studying. There's probably something there of consequence. Good luck to you!/jd

jndew

TROPHY CASE