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A unifying dynamical theory of distributed computation and generalisation in biological and artificial neural systems

CORDIS · observation · Publication date unknown

local recordings to reveal brain-wide cognitive processes. Further, composing invariances would provide insights into the neural correlates of generalisation, with a broad impact on neuroscience and machine learning. I propose a novel mathematical theory combining abstract combinatorial dynamical systems theory and modern machine learning to infer and compose invariant latent dynamics across measurements. We will use this theory to unify large-scale cell-resolution recordings of the mouse and macaque cortex into a common model to make cell-specific predictions across several brain regions. Our results could fundamentally challenge our view on distributed cognitive computations by revealing moment-by-moment single-neuron dynamics in spatially distributed neurons. Further, my theory will help understand how the brain generalises knowledge across tasks by composing and repurposing invariances. More broadly, my theory will open new avenues for machine learning and neuroscience to interact through sharing and shaping the dynamical processes that underpin neural computations in vivo and i

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recordType

award

status

SIGNED

region

EU

value

1499508

unit

EUR