A modelling of the neuro-energetics of Bayesian learning.

Validation of energetic cost of neuroplasticity as an important tradeoff in reward/cost evaluations that instruct behaviour and learning by modelling empirically obtained biological parameters within a Bayesian framework. An investigation into how Bayesian learning overlaps with energy efficient learning through principles such as maximum entropy encoding.

This would motivate an inquiry into general bottlenecks and populational heterogeneity in metabolically efficient learning. Implications of this research are an understanding of how energetic cost imposes a requisite for informational prioritisation effecting learning and behavioural tendencies. There is much research outlining how Alzheimers is related to oxidative stress, metabolic dysfunction and pathology in vascular exchange; therefore, understanding the metabolic demands of learning may further elucidate the etiologic of neurodegenerative diseases such as Alzheimers.

This project falls within the EPSRC computational neuroscience research area. Juxtaposing the efficient encoding hypothesis with the Bayesian brain hypothesis is novel. Considering the metabolic cost of plasticity as a factor in information prioritisation and decision making is novel.