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Okun, M.

Publications and source records attributed to Okun, M..

2 recordsLinked to original sources

Dopaminergic and frontal signals for decisions guided by sensory evidence and reward value

Making efficient decisions requires combining present sensory evidence with previous reward values, and learning from the resulting outcome. To establish the underlying neural processes, we trained mice in a task that probed such decisions. Mouse choices conformed to a reinforcement learning model that estimates predicted value (reward value times sensory confidence) and prediction error (outcome minus predicted value). Predicted value was encoded in the pre-outcome activity of prelimbic frontal neurons and midbrain dopamine neurons. Prediction error was encoded in the post-outcome activity of dopamine neurons, which reflected not only reward value but also sensory confidence. Manipulations of these signals spared ongoing choices but profoundly affected subsequent learning. Learning depended on the pre-outcome activity of prelimbic neurons, but not dopamine neurons. Learning also depended on the post-outcome activity of dopamine neurons, but not prelimbic neurons. These results reveal the distinct roles of frontal and dopamine neurons in learning under uncertainty.

neuroscience

Distinct structure of cortical population activity on fast and infraslow timescales

Cortical activity is organised across multiple spatial and temporal scales. Most research on the dynamics of neuronal spiking is concerned with timescales of 1 ms - 1 s, and little is known about spiking dynamics on timescales of tens of seconds and minutes. Here, we used frequency domain analyses to study the structure of individual neurons spiking activity and its coupling to local population rate and to arousal level across frequencies ranging from 0.01 to 100 Hz. In mouse medial prefrontal cortex (mPFC), the spiking dynamics of individual neurons could be quantitatively captured by a combination of interspike interval and firing rate power spectrum distributions. The relative strength of coherence with local population often differed across timescales: a neuron strongly coupled to population rate on fast timescales could be weakly coupled on slow timescales, and vice versa. On slow but not fast timescales, a substantial proportion of neurons showed firing anti-correlated with the population. Infraslow firing rate changes were largely determined by arousal rather than by local factors, which could explain the timescale dependence of population coupling strength of individual neurons. These observations demonstrate how individual neurons simultaneously partake in fast local dynamics, and slow brain-wide dynamics, extending our understanding of infraslow cortical activity beyond the mesoscale resolution of fMRI studies.

neuroscience