Abstract
The neocortex performs a wide range of functions, including working memory, sensory perception, and motor planning. Despite this diversity in function, evidence suggests that the neocortex is made up of repeating subunits (“macrocolumns”), each of which is largely identical in circuitry. As such, the specific computations performed by these macrocolumns are of great interest to neuroscientists and AI researchers. Leading theories of this microcircuit include models of predictive coding, hierarchical temporal memory (HTM), and Adaptive Resonance Theory (ART). However, these models have not yet explained: (1) how microcircuits learn sequences input with delay (i.e., working memory); (2) how networks of columns coordinate processing on precise timescales; or (3) how top-down attention modulates sensory processing. I provide a theory of the neocortical microcircuit that extends prior models in all three ways. Additionally, this theory provides a novel working memory circuit that extends prior models to support simultaneous multi-item storage without disrupting ongoing sensory processing. I then use this theory to explain the functional origin of a diverse set of experimental findings, such as cortical oscillations.
Highlights
Understanding the exact computations performed by the mammalian neocortex has been a ‘‘Holy Grail’’ of Neuroscience for over 100 years
I seek to present a model that can perform the same computations of prior hierarchical temporal memory (HTM) models, but can : (1) perform working memory and connect sequences separated by long time intervals; (2) coordinate its activity and processing with other macrocolumns and structures on extremely precise time intervals; and (3) can be modulated by attention
In parts of the neocortex with extensive lateral inhibition, this will lead to neurons that were in a predicted state firing action potentials, but those that were in inactive states not firing at all because they get rapidly inhibited before they have a chance to depolarize
Summary
Understanding the exact computations performed by the mammalian neocortex has been a ‘‘Holy Grail’’ of Neuroscience for over 100 years. In parts of the neocortex with extensive lateral inhibition, this will lead to neurons that were in a predicted state firing action potentials, but those that were in inactive states not firing at all because they get rapidly inhibited before they have a chance to depolarize This dynamic is an essential computational motif in HTM (Hawkins and Ahmad, 2016). These L6a-CT projections are generally thought of as the origin of ‘‘top-down’’ signals (Douglas and Martin, 2004) They are not able to drive action potentials in thalamic relay neurons on their own, but they can increase the firing rate of an already activated thalamic relay neuron via these modulatory synapses or put them into a subthreshold predictive state. These collaterals provide lateral inhibition to nearby relay cells (Pinault and Deschênes, 1998)
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