By Giorgio A. Ascoli, Michael Hawrylycz, Hesham Ali, Deepak Khazanchi, Yong Shi
This booklet constitutes the refereed complaints of the overseas convention on mind and wellbeing and fitness Informatics, BHI 2016, held in Omaha, united states, in October 2016.
The 37 revised complete papers, together with workshop papers from BAI 2016, provided have been rigorously reviewed and chosen for inclusion within the publication. The papers are equipped in topical sections on cognitive and computational foundations of mind technological know-how; investigations of human info processing structures; mind tremendous facts analytics, curation and administration; new methodologies for mind and psychological overall healthiness; brain-inspired intelligence and computing; mind and synthetic intelligence.
Read or Download Brain Informatics and Health: International Conference, BIH 2016, Omaha, NE, USA, October 13-16, 2016 Proceedings PDF
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Extra resources for Brain Informatics and Health: International Conference, BIH 2016, Omaha, NE, USA, October 13-16, 2016 Proceedings
The proposed model explains recent behavioural results where obstructing astrocytes leads to deﬁciencies in learning new knowledge without aﬀecting ability to recall existing knowledge. The model is also a novel Bayesian brain theory which uniquely addresses the cellular and synaptic levels. Keywords: Glia 1 · Astrocytes · Bayesian brain · Neural networks Introduction An article published this year in Nature Neuroscience  describes a recent 3rd wave in the debate about information processing by astrocytes, the principal type of glia in the cortex.
9, 310 (2015) 22. : Glial biology in learning and cognition. Neuroscientist 20, 426–431 (2014) 23. : Motor skill learning requires active central myelination. Science 346, 318–322 (2014) 24. : GLIA: oligodendrocytes rev up motor learning. Nat. Rev. Neurosci. 15, 766–767 (2014) 25. : Experience-driven brain plasticity: beyond the synapse. Neuron Glia Biol. 1, 351 (2005) Computational Role of Astrocytes in Bayesian Inference 33 26. : Artiﬁcial astrocytes improve neural network performance. PLoS ONE 6, e19109 (2011) 27.
Based on likelihood derived from propagating new data through a neural network, the astrocyte learns an empirical prior distribution over synapses in its micro-domain for the functional interpretation of that likelihood/neural network. 2. The synapses of a neural network are continually sampled from the prior distribution and thus continually ﬂuctuate. This implies a level of constant noise in the neural network. There may be a link between the near-Poisson nature of experimentally known neural noise  and the Poisson model of waves breaking thresholds at particular points in their domains .