Thinking Allowed

medical / technology / education / art / flub

Brain Computation Is Organized via Power-of-Two-Based Permutation Logic: There is considerable scientific interest in understanding

Brain Computation Is Organized via Power-of-Two-Based Permutation Logic: There is considerable scientific interest in understanding how cell assemblies - the long-presumed computational motif - are organized so that the brain can generate cognitive behavior. The Theory of Connectivity proposes that the origin of intelligence is rooted in a power-of-two-based permutation logic (N=2i–1), giving rise to the specific-to-general cell-assembly organization capable of generating specific perceptions and memories, as well as generalized knowledge and flexible actions. We show that this power-of-two-based computational logic is widely used in cortical and subcortical circuits across animal species and is conserved for the processing of a variety of cognitive modalities including appetitive, emotional and social cognitions. However, modulatory neurons, such as dopaminergic neurons, use a simpler logic despite their distinct subtypes. Interestingly, this specific-to-general permutation logic remained largely intact despite the NMDA receptors – the synaptic switch for learning and memory – were deleted throughout adulthood, suggesting that it is likely developmentally pre-configured. Moreover, this logic is implemented in the cortex vertically via combining a random-connectivity strategy in superficial layers 2/3 with nonrandom organizations in deep layers 5/6. This randomness of layers 2/3 cliques – which preferentially encode specific and low-combinatorial features and project inter-cortically – is ideal for maximizing cross-modality novel pattern... Xie, Kun. Fox, Grace E.. Liu, Jun. Lyu, Cheng. Lee, Jason C.. Kuang, Hui. Jacobs, Stephanie. Li, Meng. Liu, Tianming. Song, Sen. Tsien, Joe Z. Frontiers in Systems Neuroscience.

Source: journal.frontiersin.org

logic power-of-two-based permutation layers specific-to-general organized computational neurons