It has been known for almost a century that when animals learn new routines that the synaptic strength within the brain, especially within the neocortex, is systematically altered (Hebb 1949; Kandel 2006). Enhancement of synaptic strength has been demonstrated for human subjects learning a new language. A group of Japanese university students, who were moderately bilingual, were enrolled in a 4-month intensive language course to improve their English (Hosoda et al. 2013). During this period, they learned ~ 1000 new English words which they used in various spoken and written contexts. The learning was followed by a weekly test. To learn the 1000 words, it is estimated that 0.0006 bits per second of information were transmitted over the 4-month period [1.5 bits per letter x 4 letters/word x 1000 words/16 weeks], a rate that (not surprisingly) falls well short of the 40 bits per second transmitted by a competent communicator of English (Reed and Durlach 1998); hence learning takes longer than the execution of a learned act. Additionally, it was discovered that the pathway between Broca’s area and Wernicka’s area was enhanced in the students as evidenced by diffusion tensor imaging (Hosoda et al. 2013). Such enhancement during learning has been attributed to increased myelination and synaptogenesis (Blumenfeld-Katzir et al. 2011; Kalil et al. 2014; Kitamura et al. 2017). A central reason for this understanding is that the minimal circuit for language learning has long been known to exist between Wernicke’s and Broca’s areas of the human brain based on lesion, stimulation, and neural recording experiments (Kimura 1993; Ojemann 1991; Metzger et al. 2023; Penfield and Roberts 1966).
With the use of modern methods (e.g., wide-field two-photon calcium imaging and optogenetic activation and inhibition), we can now delineate, with a high degree of precision, minimal cortical circuits that are involved in the learning of new tasks in animals (e.g., Esmaeili, Tamura et al., 2021, see attached Fig. 1). The next step is to measure the changes in synaptic formation via learning to assess the amount of new information added to neocortex [which in humans has an estimated capacity to store 1.6 x 10^14 bits of information or 2 ^ (1.6 x 10^14) possibilities, Tehovnik, Hasanbegović 2024]. This will address whether the neocortex has an unlimited capacity for information storage or whether the addition of new information replaces the old information as related to previous learning that utilized the minimal circuit (the same will need to be done for corresponding cerebellar circuits that contain the executable code based on stored declarative information, Tehovnik, Hasanbegović, Chen 2024). We have argued that uniqueness across individual organisms is predicated on both genetics and learning history (thereby making the hard problem of consciousness irrelevant). Soon investigators will track the learning history of an individual organism to assess how the brain creates (and updates) a unique library of learning per organism thereby helping us understand how genetics and learning history created, for example, Einstein, Kasparov, and Pelé.
Figure 1: A minimal neocortical circuit is illustrated for mice trained to perform a delayed go-no-go licking task before (Novice) and after learning (Expert). As with the minimal circuit for language acquisition in humans, this circuit can now be subjected to detailed synaptic analysis by which to quantify how learning occurs at the synapses (Hebb 1949; Kandel 1996); this quantification can be used to estimate how many bits of information the new connections represent and then to compare the amount of new information added to the animal’s behavioral repertoire (Tehovnik, Hasabegović, Chen 2024). Illustration from Fig. 8 of Esmaeli, Tamura et al. (2021).
The bits are considered as a measurement as the number of action potentials over time is a measurement. So, comparing the brain to a computer is not the intention here.
Dear R. Poznansky:
After checking out some of your writings, I sense that your understanding of physics is far superior to mine. So, here is the question: it is known that the neurons in the neocortex of mammals expend some 20 times more energy per neurons than is the case for neurons in the cerebellum (Herculano-Houzel 2011). Synaptic differences per neuron do not seem to account for this difference (the difference is 3-fold rather than 20-fold). Some have attributed the high energy demand per neocortical neuron to the cost of sustained consciousness (Herculano-Houzel 2011), which is never turned off during waking state as experienced by us and as correctly noted by Chomsky (2023). What is your view of this difference in energy consumption per neuron between neocortex and cerebellum (which together account for over 95% of the neurons in the brain). Thank you in advance.
Ed Tehovnik
Kind of a potential chicken and egg problem? What decides what information gets processed by the "cognitive brain machine". Thinking about thinking is a similar problem. Who's running the show??
in humans has an estimated capacity to store 1.6 x 10^14 bits of information or 2 ^ (1.6 x 10^14) possibilities, Tehovnik, Hasanbegović 2024]."
That is how computers work, but humans don't have "bits" of information but fluctuations that evolve into experienceable forms. See DOT of consciousness as the now best theory of consciousness after the pseudoscience ITT has been debunked by world community of brain researchers
Using information theory is no different from using a yard stick. You are welcome to use your own metrics.