Podcast with Gary Marcus on canonical microcircuit and variable binding

What if the search for a single canonical cortical microcircuit is leading neuroscience in the wrong direction? Cognitive scientist Gary Marcus argues that the brain's apparent uniformity masks functionally critical variations , and that understanding higher cognition requires computational primitives we have barely begun to identify. Subscribe for more from the Convergent Science Network podcast series. Gary Marcus joins Paul Verschure and Tony Prescott at the BCBT summer school to challenge the dominant idea that a single repeated circuit underlies all cortical computation. Drawing on evolutionary biology's principle of duplication and divergence, Marcus argues that cortical areas may share a common template but differ in ways that are functionally decisive , much like a hand and a foot share most of their genes yet serve very different purposes. He contends that the field's attraction to parsimony, while productive in physics, is misleading in biology where complexity is the rule. The discussion identifies what Marcus sees as the most critical gap in computational neuroscience: variable binding. While hierarchical feature detection is reasonably well understood and modeled, the ability to represent variables, instantiate them with particular values, maintain structured representations, and distinguish types from tokens remains unexplained at the neural level. Marcus argues these operations are non-negotiable for higher cognition, particularly language, and that no current neural network architecture adequately captures them. He also revisits his earlier claim about tree structures, now arguing that humans lack true location-addressable memory, which limits our ability to represent unbounded hierarchical structures. Key topics include why the cortex appears uniform under a magnifying glass but differs in functionally important ways, how duplication and divergence applies to cortical circuit evolution, what variable binding is and why it matters for language and reasoning, the limitations of simple recurrent networks for capturing syntax, why labeled-line architectures cannot scale to handle novel representations, and how a phylogenetic approach to cortical circuit types could advance our understanding of human cognition. Part of the Convergent Science Network podcast series from the BCBT Summer School.