Topic: probabilistic population codes

Synaptic inputs onto single cortical neurons exhibit substantial diversity in their sensory-driven activity. What this diversity reflects is unclear, and appears counter-productive in generating selective somatic responses to specific stimuli. We propose that synaptic diversity supports computations that most effectively use information from upstream populations. To test this idea, we directly compare in silico synaptic weights for probabilistic decoder with in vivo synaptic input measurements. We define a decoder for a single sensory variable, orientation, that reads out the stimulus orientat

Research in machine learning has realized large performance gains on a variety of tasks by expanding the target of learning from the mean reward, as in traditional reinforcement learning (RL), to the entire distribution of rewards, an approach known as distributional RL. Dopamine (DA) neurons projecting from the midbrain to the striatum have long been thought to drive traditional RL in the mammalian brain. Moreover, a recent analysis of the response diversity of these neurons shows they have the appropriate properties to support distributional RL, and thus the learning of complete reward distr