Mechanization of Cognition - Biomimetics: Biologically Inspired Technologies - page 116

Figure 3.A.7 Synapse strengthening — the fundamental storage mechanism of cortical knowledge links. Sub-
figure A illustrates a weak, unreliable, unstrengthened synapse making a connection from a transponder neuron
axon to a target neuron dendrite. The theory hypothesizes that roughly 99% of human cortical synapses with this
connectivity are unstrengthened. Subfigure B illustrates the same synapse after learning (i.e., the progression from
short-term memory to medium-term memory to long-term memory has been completed). Now, the synapse has
blossomed into three parallel synapses, each physically much larger than the original one. This multi-synapse
(perhaps what has been recently termed a ribbon synapse) is more reliable and has an efficacy ranging from
perhaps 10 to 60 times that of the original unstrengthened synapse (learning always yields a great increase
in efficacy — the theory posits that there are no such knowledge storage synapses which are only slightly
strengthened).

range problem mentioned above, but it is also a key part of making confabulation work (as we
will see below)!
So, given the above estimates and hypothesis, let us determine the base b of the logarithms used
for synaptic knowledge coding in the human cerebral cortex, as well as the constant c (actually,
we will instead estimate a ¼ log b (c)). We want p(c j l) ¼ 0.0001 to be represented by a synaptic
strength of 10; and we want p(c j l) ¼ 1.0 to be represented by a synaptic strength of 60. In other
words, we need to find positive constants a and b such that:

a þ log b (0 : 0001) ¼ 10 (3A : 2)

and

a þ log b (1 : 0) ¼ 60 (3A : 3)

Clearly, from the second equation, a ¼ 60 (since the log of 1 is zero for every b). Then the first
equation yields b ¼ 1.2023. Thus, when a highly excited transponder neuron representing source
symbol c delivers its signal to a neuron of answer lexicon symbol l, the signal delivered to that
neuron will be proportional to a þ log b (p(c j l)) (where the constant of proportionality is postulated
to be the same for all target neurons of a single module; and where nearby modules typically have
very similar proportionality constants).
You might wonder why the signal delivered is not the ''product'' of the transponder neuron
output signal and the synaptic efficacy (as was common in classical ''neural network'' models such
as the Perceptron [Hecht-Nielsen, 2004]). Well, it is! However, exploring this aspect of the theory
would quickly take us beyond the scope of this introductory sketch. Since transponder neurons