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James DiCarlo; Next-generation models of recurrent computations in the ventral visual stream. Journal of Vision 2021;21(9):65. doi: https://doi.org/10.1167/jov.21.9.65.
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© ARVO (1962-2015); The Authors (2016-present)
Understanding mechanisms underlying visual intelligence requires combined efforts of brain and cognitive scientists, and forward engineering emulating intelligent behavior (“AI engineering”). This “reverse-engineering” approach has produced more accurate models of vision. Specifically, a family of deep artificial neural-network (ANN) architectures arose from biology’s neural network for object vision — the ventral visual stream. Engineering advances applied to this ANN family produced specific ANNs whose internal in silico “neurons” are surprisingly accurate models of individual ventral stream neurons, that now underlie artificial vision technologies. We and others have recently demonstrated a new use for these models in brain science — their ability to design patterns of light energy images on the retina that control neuronal activity deep in the brain. The reverse engineering iteration loop — respectable ANN models to new ventral stream data to even better ANN models — is accelerating. My talk will discuss this loop: experimental benchmarks for in silico ventral streams, key deviations from the biological ventral stream revealed by those benchmarks, and newer in silico ventral streams that partly close those differences. Recent experimental benchmarks argue that automatically-evoked recurrent processing is critically important to even the first 300msec of visual processing, implying that conceptually simpler, feedforward only, ANN models are no longer tenable as accurate in silico ventral streams. Our broader aim is to nurture and incentivize next generation models of the ventral stream via a community software platform termed “Brain-Score” with the goal of producing progress that individual research groups may be unable to achieve.
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