Toni Westbrook dot Com

The Joy of Computer Science and Engineering

You may be wondering what TFNN is - it stands for Temporal Frame Neural Network, an artificial intelligence project of mine to accurately simulate the biological brain. Sadly, I haven’t worked on it in a few years for a number of reasons - reasons that were good at the time (and I wouldn’t change, the whole learning experience thing), but ones that aren’t so important now.

What the Heck Happened?

Jumping off topic a bit, but relevant to why I’m starting back up again, I realized a few weeks ago that I’m not really happy with the way my life is going. I mean, don’t get me wrong, overall things are great and I’m doing okay, but there is definitely something off. There are a few reasons, but one of the biggies was I was always doing things I felt obligated to do and never did things for fun anymore. I was always taking on a project to advance somehow, and never did it for the art or to enjoy it. I was always working hard, but honestly not really wanting the outcome, so it would never really go anywhere. I’m not a business man - I don’t like or want to play the game (There’s another article in here about not always turning your hobbies into something you get paid for, but that’s for another day). There’s nothing wrong with being a business man mind you - I’m just not one.

So I made the decision to just stop worrying about “succeeding” in these classic ways that are good for some people, but not for me. I learned something big from Shredz64 - I will never make any money off the project, but I had an incredible amount of fun doing it, and I have made so many connections with people because of it - it’s just amazing. I want to keep doing that all the time - I want to make things - not worry about marketing or selling them - I just want to create and share. I’ll save the rest of my thoughts for another post, but the bottom line is, I’ve already started working a ton more on my projects and I’ve been much happier because of it.

Back to Virtual DNA

SO, that being said, I recently made a 10 hour drive to and from Toronto, and it gave me a lot of time to think. Some of that thought was dedicated to the TFNN project. While the “neurophysiology” of the TFNN works great on a neuron and connection level, the overall issue remains in how those synaptic connections are made. Their configuration. Biology has a great thing going for it with DNA that controls neural development - during the neurulation phase when the neuroectoderm forms a lot of things happen, but at the end of the day through migration, axon paths and some other tricks, neurons are placed into their proper locations and form appropriate connections. Regardless of the nature vs nurture argument, there is definitely prewiring that is done. It’s the reason why a cat will never develop the ability to speak Romanian and why rabbits breathe without being trained to do so - it’s millions and millions of years of neurological evolution packed into a double helix.

Therein lies the problem - I have the materials with TFNN, but no blueprint I can use to construct something. I can make very small and specific networks, or very large, random ones, but neither of those will accomplish the goal of creating animal intelligence. So a blueprint is needed. Life has DNA, but what does TFNN have?

Use Real DNA?

My first idea to conquer this issue (as outlined on the project page) was to use some of the sequence databases that are available online - there are a couple species that have a very full nucleotide sequence documentation available. I won’t even bother mentioning all the reasons why this was never going to work, because the biggest reason is, I’m not a molecular geneticist, and while I have a good understanding of how DNA works, I don’t come close to having enough understanding to use DNA sequencing information to form a TFNN. It’s another project that I would love to start one day to build my understanding of the process, but not right now.

Let the Turing Machine Do What it Does Best

What I decided on the car ride was instead of using real DNA, it would be more realistic (relatively speaking) to create a virtual (accelerated) environment where evolution could take place and form virtual DNA. The TFNN already has rudimentary functionality for building neural networks from a list of instructions, so this is doable. Here’s the very lofty plan:

1. Flesh out the matrix class inside of TFNN to construct neural networks as defined by encoded, segmented bit sequences. This will be some work but I have a good idea of how to accomplish it. There are already class members that control size, synaptic density, geography, and even connection specific formation within the neural matrix - the bit sequence needs to drive these member functions. The purpose of something encoded like a bit sequence as opposed to human readable scripts is to allow for easy engineering of mutation capabilities necessary for evolution
2. Find a lightweight, open source graphics/physics engine. There are a few of them out there for games - it doesn’t need to look good or even come close to being the most advanced one available, it just needs to support a number of attributes common to our world such as mass, gravity, displacement, etc. The key is lightweight as possible, we don’t want to eat up CPU maintaining the world, we need all the cycles we can get for TFNN processing
3. Engineer a method of recharging a lego NXT robot (Bit, my little LEGO robot will be the subject in these experiments) that can be initiated and completed by the robot itself. There are a number of ways to accomplish this, something tactile is preferred to force movement. Something like a magnetic connector with DC current. It would also need to produce a distinct stimulus to indicate it was a source of “nourishment” so to speak, such as producing an audible tone at a specific frequency .
4. Create a VDNA (virtual DNA, easier to type) sequence to form a neural network that dictates motor control to guide robot to its “feeding station”. It doesn’t need to have any logic outside of a straight path for the source. I’ve created simple neural networks like these before and it is doable.
5. Within the physics engine, model an environment that very simply and basically models a real world environment. The goal is by no means to have every possible physical scenario that could exist in the real world, its to offer enough obstacles and stimulus that evolution can take place, while using obstacles that are common to the environment the robot will operate in. Also include feeding stations
6. Build an engine to generate instances of TFNNs using VDNA sequences and process them. Connect them to virtual robots modeled after the NXT lego robot and place them in the virtual world. Also include functionality to take the VDNA of a specific instance and spawn a new instance of the virtual robot. We could do this asexually or start with a neural configuration that drives two robots to touch in a manner that shares VDNA for virtual reproduction - I haven’t decided on this one yet. Regardless, new VDNA is subject to random mutation or corruption in the bit sequence
7. Build in parameters that cause death in the virtual robots as well as prevent premature reproduction - most importantly that reproduction doesn’t take place if nourishment isn’t obtained.
8. Run this simulation until results are obtained
9. Take VDNA from successful virtual robot, generate instance of TFNN, connect to Bit and watch the fun

I can’t complain about being bored, that’s for sure - I will post here as I go. It may lead nowhere, but I’m extremely interested to see the results - even if it completely fails, it will still be fun science.

I thought I’d sit down and update - it’s not that I haven’t been working a lot on TFNN, I just haven’t had a chance to sit down and actually write about it!

Firstly, I implemented crude, neuron-global neuromodulator code a week ago or so. It worked under my very specific test cases, but it didn’t really accurately model how dopamine, serotonin, or norepinephrine function on the whole. I realized there was a lot of neuron-global code that really should have been axon-terminal/synaptic cleft/postsynaptic receptor specific. Can’t write too much about it, but yesterday I rewrote a lot of code dealing with neuromodulators and synapse processing so it more closely dealt with activity on the receptor level and not on the neuron level. I ran test cases with both an inhibitory and excitatory neuromodulator, both were success.

Right now however, neuromodulators will blindly increase or decrease the effect of a neurotransmitter. I would like to include code that discerns between a glutamate excitatory reaction and a GABA inhibitory reaction, and selectively affects only one.

A quick update while I’m thinking about - next time I sit down with the code I want to add a section to emulate the functionality of dopamine cells like those found within the ventral tegmental, and other neuromodulators. This is actually a major enhancement and something to give careful thought to before proceeding. At first I intended TFNN matrices to operate without global or semiglobalized synaptic modulation - IE the tfnn matrix would operate purely on the “mechanical nature” of electro-chemical reactions in axodendritic, axosomatic, and axoaxonic connections - no globalized chemical reactions within the system.

The more I study though, the more I realize how important dopamine and other neuromodulators are in the prefrontal cortex regions. Via message controlled signals, these modulators can facilitate GABA reactions, and hence temporarily “quiet” certain systems, allowing for concentration. I have a feeling that without dopamine emulation matrices would fall prey to a ubiquitous ADD of sorts, and perhaps fail to mold meaningful neural configurations in deeper matrices due to an overload of traffic on neural bridges coming from sensory thalami and cortices.

At first when I was kicking it around I was thinking of just modifying axoaxonic connection code to introduce a negative change to synaptic weights and have that emulate dopamine secretion. This isn’t accurate though, as dopamine is a modulator, not a permanent change to the synaptic weights.

I think this may call for another variable to be introduced into the neuron, one that keeps track of current affecting modulators. More space - but I also realize I have an unused integer currently in the neuron that I used during debug sessions, I’ll remap that for dopamine / other modulator use. I may use it or another variable in connection to track glutamate supply to emulate habituation effects as well. It will add very little additional calculation time.

It’s amazing how large the TFNN neuron has grown in complexity from when I first completed the code until now.