Podcast with Stuart Wilson on self-organization and cortical maps

How does the brain build its own maps, and what constrains the patterns that evolution can produce? Computational neuroscientist Stuart Wilson argues that cortical arealization emerges from self-organizing processes operating within the design space defined by reaction-diffusion dynamics , not from a genetic blueprint that specifies each area independently. Subscribe for more from the Convergent Science Network podcast series. Stuart Wilson joins Paul Verschure and Tony Prescott to discuss how self-organization and natural selection interact to produce the diverse cortical maps observed across mammalian species. Drawing on Stuart Kauffman's framework and Alan Turing's reaction-diffusion mathematics, Wilson proposes that gene expression gradients across the developing cortex are themselves generated by self-organizing processes constrained by boundary shape and diffusion constants. Only certain patterns are possible for a given cortical geometry, and natural selection works within this limited design space rather than engineering maps from scratch. The conversation probes the methodology of building models that bridge abstract mathematical principles and messy biological reality. Wilson describes a collaboration with biologists Leah Krubitzer and Kelly Huffman, where software tools simulate self-organizing processes on arbitrary boundary shapes derived from actual cortical drawings across species. His strategy for validation is explicit: fit the model to reproduce observed variability in cortical boundaries across all catalogued species, then systematically remove components until the model breaks , identifying the minimal set of mechanisms required. Prescott and Verschure push on whether adult boundary shape is sufficient as a constraint, given that the cortex changes shape during development, and whether the model can generate predictions that biologists can test. Key topics include why the Jonas and Kording microprocessor paper matters for modelers, how knockout experiments reveal a minimal gene interaction network of approximately five genes driving cortical patterning, the relationship between tissue growth and successive self-organizing modes during development, and why the simplest model that accounts for biological complexity is more valuable than one that matches it. Part of the Convergent Science Network podcast series from the BCBT Summer School.