A modelling of the neuro-energetics of Bayesian learning.

验证神经可塑性的能量成本作为奖励/成本评估的重要权衡，通过在贝叶斯框架内模拟经验获得的生物参数来指导行为和学习。通过最大熵编码等原理研究贝叶斯学习如何与节能学习重叠。这将激发对代谢效率学习的一般瓶颈和群体异质性的研究。本研究的含义是对能量成本如何对影响学习和行为倾向的信息优先级施加必要条件的理解。有很多研究概述了阿尔茨海默病与血管交换中的氧化应激、代谢功能障碍和病理之间的关系；因此，了解学习的代谢需求可以进一步阐明阿尔茨海默病等神经退行性疾病的病因学。该项目属于EPSRC计算神经科学研究领域。将有效编码假说与贝叶斯脑假说并置是一种新颖的方法。将可塑性的代谢成本作为信息优先排序和决策的一个因素是新颖的。

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.