Notes for the Lifebox, the Seashell, and the Soul - Rudy Rucker

Notes for The Lifebox, the Seashell, and the Soul, by Rudy Rucker
about simple facts like “this ball is red,” or “ head is hairy.”
I see ideas as being patterns of neuron firings, like static loops, at least in the shortterm
memory. In the long-term memory we probably have something less volatile, like
stable geometry of axon dendrites or at least maybe memory biochemicals. But the short
term idea is probably electrical.
(2a) Patterns of activation and inhibition. To keep something in mind, you keep
circling around a little loop or knot or subnetwork, activating perhaps a series of sensory
memories blue-hot-hungry-sweet-wet. Maybe inhibition plays a role, in terms of not letting
the thought energy branch off into other connections. Think of sand moats with water
sloshing around in a circle, the sand is inhibition, the water is activation.
This can arise a limit cycle of a process.
What controls the motions of activation and inhibition? Could it be an BZ-style rule?
One difficulty here is that the activator-inhibitor or reaction-diffusion rules are formulated in
terms of CA spaces, and the space of brain neurons doesn’t have that type of structure. It’s a
network.
But perhaps we could run such a rule over a network. Visualize a 3D CA running a
BZ rule and have the cells be nodes connected by strings to their neighbors and stretch a
bunch of the strings and scramble things around, maybe drop the mess onto a tabletop and
the BZ is still happening, but on casual observation you might not be able to see the scrolls.
In the same way, the network of my brain cells can be running an activator inhibitor rule
even though no type of brain scan would reveal scrolls. The spatial arrangement of the
neurons doesn’t match their connectivity. Actually there probably is some match, so one
might expect to see traces of scrolls in the brain activation patterns. [Technical problem: a
network doesn’t have the neighborhood structure of a CA, like if A connects to B and A
connects to C, that doesn’t imply B is “near” C on a network, thought it does in space.
Maybe I could set up an N-d space of some kind to have linked things close? The i-th axis
being (1-strength of connection to node i). This is too hard to think about.]
Now what is the stuff that spreads the activation and inhibition? Rather than thinking
of it as concentrations of chemicals, I could, rather think of it as excitory and inhibitory
electrical signals. Suppose each neuron is in a state between -1 and +1 (inhibited vs. excited)
and suppose that each has axons leading to other neurons, and that these axons transmit a -1
to +1 signal matching the current activation level of the cell.
One thing that makes me pause here is that in Turing stripes and BZ rules, we have
the activation and inhibition spreading at different rates. But it seems like an electrical signal
be transmitted at the same rate whatever its value. But a neuron could respond at different
speeds to different kinds of inputs, might move more slowly into activation and be able to
drop abruptly into inhibition, thus effectively giving different rates. In this connection think
about things like the Hodge rule or even Brian’s Brain which mimic excitation and inhibition
without even having two substances, they get by with simply a range of states.
If the process is something like a CA running on a network of neurons, we can ask
what is the rule in each neuron. I’ll suggest a complicated and a simple idea. The
complicated idea is to have different rules at each neuron. The simple idea is to have the
same rule at each neuron.
(2b) Complicated neuron rules. I think here of a Stuart Kaufman network. Imagine
having N cells, each with k links to randomly chosen other cells, and give each cell a random
Boolean function F used as NewVal = F(k inputs), and just put a 0 or 1 in each cell, and
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