Authors:
Alexei Mikhailov
and
Mikhail Karavay
Affiliation:
Institute of Control Problems, Russian Acad. of Sciences, Profsoyuznaya Street, 65, Moscow, Russia
Keyword(s):
Machine Learning, Cortical Column, Pattern Recognition, Numeric Inverted Index.
Abstract:
Columnar organization of the neocortex is widely adopted to explain the cortical processing of information (Mountcastle, V., 1957, Mountcastle, V., 1997, DeFelipe, J., 2012). Neurons within a minicolumn (feature column) simultaneously respond to a specific feature, whereas neurons within a macrocolumn respond to all values of receptive field parameters (Horton, J., Adams, D., 2005). Hypotheses for a cortical column function envisage a massively repeated “canonical” circuit or a spatiotemporal filter (Bastos, A. et al., 2012). However, nearly a century after the neuroanatomical organization of the cortex was first defined, there is still no consensus about what a function of the cortical column is (Marcus, G., Marblestone, A., Dean, T., 2014). That is, why are cortical pyramidal neurons arranged into columns? Here we propose what the function of the neocortical column is using both neuro-physiological and computational evidence. This conjecture of the column’s function helped find a w
ay of evaluating the memory capacity of a cortical region in terms of patterns as a solution to a suggested connectivity equation. Also, it allowed introducing a connectivity-based machine learning model that accounted for pattern recognition accuracy, noise tolerance and showed how to build practically instant learning pattern recognition systems.
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