The atoms of neural computation
Venue
Science, vol. 346 (2014), pp. 551-552
Publication Year
2014
Authors
Gary Marcus, Adam Marblestone, Tom Dean
BibTeX
Abstract
The human cerebral cortex is central to a wide array of cognitive functions, from
vision to language, reasoning, decision-making, and motor control. Yet, nearly a
century after the neuroanatomical organization of the cortex was first defined, its
basic logic remains unknown. One hypothesis is that cortical neurons form a single,
massively repeated “canonical” circuit, characterized as a kind of a “nonlinear
spatiotemporal filter with adaptive properties” (1). In this classic view, it was
“assumed that these…properties are identical for all neocortical areas.” Nearly
four decades later, there is still no consensus about whether such a canonical
circuit exists, either in terms of its anatomical basis or its function. Likewise,
there is little evidence that such uniform architectures can capture the diversity
of cortical function in simple mammals, let alone characteristically human
processes such as language and abstract thinking (2). Analogous software
implementations in artificial intelligence (e.g., deep learning networks) have
proven effective in certain pattern classification tasks, such as speech and image
recognition, but likewise have made little inroads in areas such as reasoning and
natural language understanding. Is the search for a single canonical cortical
circuit misguided?
