Categorical encoding of decision variables in orbitofrontal cortex

TY - JOUR

T1 - Categorical encoding of decision variables in orbitofrontal cortex

AU - Onken, Arno

AU - Xie, Jue

AU - Panzeri, Stefano

AU - Padoa-Schioppa, Camillo

PY - 2019/10/14

Y1 - 2019/10/14

N2 - A fundamental and recurrent question in systems neuroscience is that of assessing what variables are encoded by a given population of neurons. Such assessments are often challenging because neurons in one brain area may encode multiple variables, and because neuronal representations might be categorical or non-categorical. These issues are particularly pertinent to the representation of decision variables in the orbitofrontal cortex (OFC)-an area implicated in economic choices. Here we present a new algorithm to assess whether a neuronal representation is categorical or non-categorical, and to identify the encoded variables if the representation is indeed categorical. The algorithm is based on two clustering procedures, one variable-independent and the other variable-based. The two partitions are then compared through adjusted mutual information. The present algorithm overcomes limitations of previous approaches and is widely applicable. We tested the algorithm on synthetic data and then used it to examine neuronal data recorded in the primate OFC during economic decisions. Confirming previous assessments, we found the neuronal representation in OFC to be categorical in nature. We also found that neurons in this area encode the value of individual offers, the binary choice outcome and the chosen value. In other words, during economic choice, neurons in the primate OFC encode decision variables in a categorical way.

AB - A fundamental and recurrent question in systems neuroscience is that of assessing what variables are encoded by a given population of neurons. Such assessments are often challenging because neurons in one brain area may encode multiple variables, and because neuronal representations might be categorical or non-categorical. These issues are particularly pertinent to the representation of decision variables in the orbitofrontal cortex (OFC)-an area implicated in economic choices. Here we present a new algorithm to assess whether a neuronal representation is categorical or non-categorical, and to identify the encoded variables if the representation is indeed categorical. The algorithm is based on two clustering procedures, one variable-independent and the other variable-based. The two partitions are then compared through adjusted mutual information. The present algorithm overcomes limitations of previous approaches and is widely applicable. We tested the algorithm on synthetic data and then used it to examine neuronal data recorded in the primate OFC during economic decisions. Confirming previous assessments, we found the neuronal representation in OFC to be categorical in nature. We also found that neurons in this area encode the value of individual offers, the binary choice outcome and the chosen value. In other words, during economic choice, neurons in the primate OFC encode decision variables in a categorical way.

KW - Algorithms

KW - Animals

KW - Choice Behavior/physiology

KW - Cluster Analysis

KW - Computational Biology/methods

KW - Decision Making/physiology

KW - Frontal Lobe/physiology

KW - Macaca mulatta

KW - Models, Theoretical

KW - Neurons/physiology

KW - Prefrontal Cortex/physiology

KW - Reward

U2 - 10.1371/journal.pcbi.1006667

DO - 10.1371/journal.pcbi.1006667

M3 - SCORING: Journal article

C2 - 31609973

VL - 15

JO - PLOS COMPUT BIOL

JF - PLOS COMPUT BIOL

SN - 1553-734X

IS - 10

M1 - e1006667

ER -