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Sensory input signals can span many orders of magnitude while dynamically changing, and this requires neurons to adapt their bandwidth-limited response behaviour efficiently1,2,3. We derive from first principles a Bayesian model of neural adaptation to the instantaneous input statistics, and show how existing intracellular data can be explained by...
Experimental evidence at the behavioural-level shows that the brain makes Bayes-optimal decisions (Körding & Wolpert 2004, Nature; Ernst & Banks, 2002, Nature), yet at the circuit level little is known about how brains may implement Bayesian learning and inference (but see (Ma et al. 2006, Nat Neurosci)). At the same time, subcellular sources of no...
Experimental evidence at the behavioural-level shows that brain make Bayes optimal decisions1,2, yet at the circuit level little is known experimentally about how brains may implement simulatenously Bayesian learning and inference (but see 3). Here we show how wiring anatomy and local synaptic learning rules can work together with molecular sources...