If you’re anything like me, or many of the programmers and hardware hackers out there, you have a deep urge to constantly be creating something. While this presents the opportunity to try new and fun stuff, it can also be a curse in the fact that sometimes it’s hard to complete projects before jumping into a new one. I constantly have this issue, and in general I’ve tried to be good about not staring a new project before completing my existing one. And if you’ve known me for any period of time, you know there is one project that is the big one for me – the one that I’ve been working on for years, and the one that really drives me as a computer scientist – that is my quest to fully emulate the biological neural network (easy, right?). Well, after years of constantly putting it aside while working on other projects, the last 4 months I’ve been very good about focusing on it.

Goodbye TFNN, Hello SynthNet

The problem with emulating the biological brain is – it is extremely complicated to say the least, and there is still a library of information we don’t understand about neuroscience. However – there is also a huge amount of information we DO understand. I’ve had the disadvantage that I do not have a formal education in the biological sciences, let alone the specifics of neurophysiology. Because of that, the process for me of emulating it has been difficult. I have had to do a lot of catchup research to equal what the average graduate would have. This is very apparent looking at the work I’ve done now as compared to earlier versions of the emulator (TFNN) – you can see as much going back to older blog entries on this site. I am by no means an expert now, but I was less so of one back then. In the last year or two, I’ve really hit the books and tried to learn everything I can. And in doing so, I’ve learned that I got so much wrong before, that it was easier to start over again than try to repair what I had. And with that, comes the newest revision of the emulator, SynthNet.

What SynthNet Does So Far

At this point, SynthNet does the following:

1. Emulates virtual major cellular structures, such as neuron soma, dendrites and denritic arbors, axons, terminals/boutons, synapses, etc – each with the full functionality (when applicable) of the following:
2. Physical properties such as position, surface area, and cellular membranes.
3. The ability to contain substances, including ions such as Sodium, Potassium, Chloride, and Calcium, as well as neurotransmitters and modulators, such as Glutamate, Serotonin, Norepinephrine, etc, both intracellular and extracellular.
4. For all substances, current concentration is stored (with resolution to nanomoles), homeostatic concentrations, and valance of any ion substances
5. Cellular membranes contain channels, both to the extracellular space, as well as gap junctions to the intracellular space of other cellular structures.
6. Each channel stores permeability, what substance it is permeable, and tag information for synaptic tagging or other secondary messenger processes.
7. Both leak channels and active pumps are supported
8. Channels can also have gates, including voltage gates, inactivation gates, and ligand gates. Voltage gates activate at specified membrane potential, inactivation gates close either voltage or ligand gates after a certain amount of time, and ligand gates open in response to a specific concentration of a specific substance
9. Membrane voltage is calculated using the Goldman-Hodgkin-Katz Voltage Equation modified for the inclusion of divalent ions (this may need a little tweaking though, converting this over to make use of Spangler’s equation from Ala J Med Sci, 9:218-223, 1972)
10. Ion flux across the membrane is calculated using the Goldman-Hodgkin-Katz Flux Equation, with a membrane surface area coefficient.
11. All substance flux is virtually processed in an N+1 parallel fashion across all neurons simultaneously
12. The emulation of myelin sheaths via the elimination of channels/permeability in specific axonal segments, and an increase in intracellular trans-segment permeability across axonal segments.
13. CSV export functionality for analysis within Excel, LiveGraph, or other tools

So at this point, it handles ions and substances as a whole pretty well, calculating flux across a substance’s electrochemical gradient fairly accurately (for our purposes). At this point, we can setup typical ion concentrations for a mammalian neuron, setup leak, pump, and voltage channels, and trigger action potientials with the expected results (still tweaking some of the values).

To Do:

What we don’t have yet, but will have:

1. The regulation of extracellular substances via astroglia. This is the next thing I’m working on
2. Any kind of protein synthesis or activation, such as kinase phosphorylation. After I get some of the glial cell work done, this will be the next big addition to the emulator. This is critical for the mediation of Hebbian plasticity and other types of learning. The genetic engine of the emulator will allow any sequence of instructions to be run under the specified protein activation – so this will cover everything from the addition of AMPA receptors due to NMDA receptor activation, to neurite growth due to nitric oxide as a retrograde messenger, and the entire neurogenesis process as a whole. Very excited to get started on this.
3. Visualization engine, as a kind of virtual fMRI, for the purposes of graphical analysis
4. A separate engine to mutate genetic code across generations for the purposes of natural selection (more on this later, a whole different phase of the project)
5. A lot of other details, those are the biggies for now