Toni Westbrook dot Com

Sharing Software Development Knowledge With You

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.

Another quick update - I fixed some synapse timing issues in the Temporal Frame engine and finished up the axoaxonic code this weekend. I had a succesful test of sensitization as well, demonstrating the non-Hebbian learning capabilities of a neural matrix. Due to axoaxonic connections, a presynaptic neuron can now cause a direct increase in the synaptic weight of the postsynaptic neuron's axon terminal (This postsynaptic neuron itself being a presynaptic neuron in another relationship).

The test was performed by generating a 3 neuron matrix. Milo's left touch sensor was sent as input into neuron 1, while Milo's right touch sensor was sent as input into neuron 2. Neuron 1 was connected via an axoaxonic connection to neuron 2's axon terminal - the axon terminal creating the synapse between neuron 2 and neuron 3 in a standard axodendritic configuration. Neuron 3's output was sent to Milo's speaker.

The treshold rate of Neuron 3 was set higher than the synaptic weight between neuron 2 and 3, hence if Milo's right antenna was pressed he would not beep. However, upon touching Milo's left antenna a few times, via the phenomenon of sensization the synaptic weight between Neuron 2 and 3 was increased, and subsequent pressings of Milo's right antenna was enough alone to cause Milo to beep, now the synaptic weight had grown strong enough to pass Neuron 3's threshold.

Cool stuff!