Information transfer by the brain is dependent on the connectivity between neurons as mediated by learning (Hebb 1949). This connectivity is limited by the biophysical properties of neurons (based on axon diameter and myelination), such that an upper limit is set on the information transferred, which depends on the neuronal refractory period, axon length, and terminal density (Johnson and Winlow 2017; Raymond and Lettvin 1978; Swadlow et al. 1980; also see Yeomans and Tehovnik 1988). The increase in myelination after learning (Blumenfeld-Katzir 2011; Hosoda et al. 2013) may augment the reliability of the information transmitted by reducing the conduction failures (Raymond and Lettvin 1978; Swadlow et al. 1980), thereby enhancing the bits per second transferred per axon activated (Tehovnik and Chen 2015) when developing declarative, conscious units (Tehovnik, Hasanbegović, Chen 2024). Yet some believe that fundamental changes during learning, such as of language, occur at the subcellular level beyond the limitations set by synaptic time and axonal conduction velocity (Chomsky 2019—lecture delivered at MIT; also see Kandel 2006). Supra and subcellular changes due to learning need not to be separate affairs, however.
Finally, we have a paradox: why is it that once a learned behavior becomes automated that less neural tissue (or energy) is used for the execution of a learned behavior (Chen and Wise 1995ab; Hikosaka et al. 2002; Lehericy et al. 2005; Ojemann 1983), even though the synapses and axonal conduction has been enhanced by learning? Is it possible that the lack of failure at the synapse prevents axonal conduction (which also consumes energy) being wasted on half-learned behaviors?
- Badges
- Science topic
- Similar topics
- Philosophy
- Philosophy Of Science
- Reasoning