Lateral orbitofrontal cortex anticipates choices and integrates prior with current information

Abstract

Adaptive behavior requires integrating prior with current information to anticipate upcoming events. Brain structures related to this computation should bring relevant signals from the recent past into the present. Here we report that rats can integrate the most recent prior information with sensory information, thereby improving behavior on a perceptual decision-making task with outcome-dependent past trial history. We find that anticipatory signals in the orbitofrontal cortex about upcoming choice increase over time and are even present before stimulus onset. These neuronal signals also represent the stimulus and relevant second-order combinations of past state variables. The encoding of choice, stimulus and second-order past state variables resides, up to movement onset, in overlapping populations. The neuronal representation of choice before stimulus onset and its build-up once the stimulus is presented suggest that orbitofrontal cortex plays a role in transforming immediate prior and stimulus information into choices using a compact state-space representation.

A roadmap to integrate astrocytes into Systems Neuroscience

“…The specific experimental design we propose involves the simultaneous recording of Ca 2+ activity in astrocytes with two‐photon microscopy in awake animals (Srinivasan et al, ), and Ca 2+ or electrophysiological responses in neurons (Poskanzer & Yuste, ). From previous work indicating that with tens of neurons, it is possible to predict animal choices with high accuracy (Kiani et al, ; Nogueira et al, ), we reason that tens of astrocytes will suffice to observe statistically significant trends that can be used to guide subsequent recordings and analyses. At this time, optimal selection of paradigms and analytical methods may be more helpful to make significant leaps toward understanding astrocyte‐based computations than massively increasing the number of astrocytes recorded.…”

“…From previous work indicating that with tens of neurons, it is possible to predict animal choices with high accuracy (Kiani et al, 2014;Nogueira et al, 2017), we reason that tens of astrocytes will suffice to observe statistically significant trends that can be used to guide subsequent recordings and analyses. At this time, optimal selection of paradigms and analytical methods may be more helpful to make significant leaps toward understanding astrocyte-based computations than massively increasing the number of astrocytes recorded.…”

“…However, this should not distract us from the impressive predictive power that analytical tools are achieving in Systems Neuroscience. Examples of success can be found in neuroprosthetics, where the electrical activity of the brain of a human user is decoded into motor commands (Cangelosi & Invitto, ); decision‐making, in which decision outputs can be predicted from action potentials with 80% accuracy in monkeys before a response is observed (Kiani, Cueva, Reppas, & Newsome, ), and with 70% accuracy in rats, even before stimulus onset (Nogueira et al, ), and face recognition. Here, the face seen by a Rhesus monkey can be reproduced with 90% accuracy by tracking neuronal activity in the inferior temporal cortex (Chang & Tsao, ).…”

“…During this time, the brain computes correlations between fast variables, and represents differences between the prediction based on previous experience and the real outcome as “error.” We argue that the precise computation of prediction error is key in the identification of a true association between stimulus and reward, such that varying contexts plausibly require more complex computations. Frontal areas are expected to track the mixture of relevant variables in the form of “cognitive maps.” In rat, the orbitofrontal cortex encodes the millisecond‐long fast variables (Nogueira et al, ; Rolls, Critchley, Mason, & Wakeman, ). It is unclear, however, how transitions between contexts and associated deliberations are represented at the much slower time scale of seconds.…”

“…Different statistical tools address encoding and decoding. For encoding, generalized linear models (GLMs), a generalization of multiple linear regression, regress neuronal activity against behavioral variables to determine the set of variables that explain more neuronal activity (Aljadeff et al, 2016;Nogueira et al, 2017). Decoding techniques, typically linear classifiers (Arandia-Romero, Nogueira, Mochol, & Moreno-Bote, 2017;Quian Quiroga & Panzeri, 2009), as well as more recent artificial neural networks (ANNs; Paninski & Cunningham, 2018) are used to predict, trial-by-trial, values of behavioral variables from neuronal activity, either using single neuronal activity or the individual activity of large neuronal populations recorded from multielectrode-arrays or Ca 2+ imaging.…”