TY - JOUR
T1 - Computing by Robust Transience
T2 - How the Fronto-Parietal Network Performs Sequential, Category-Based Decisions
AU - Chaisangmongkon, Warasinee
AU - Swaminathan, Sruthi K.
AU - Freedman, David J.
AU - Wang, Xiao Jing
N1 - Funding Information:
This work was supported by NIH grants R01MH062349 and R01MH092927, NSF-NCS grant 1631571, and STCSM grants 14JC1404900 and 15JC1400104. We thank John Assad for valuable contributions during all phases of the neurophysiological studies, which produced the data examined here. We thank John Murray, Francis Song, and William Gaines for intellectual and helpful discussions.
PY - 2017/3/22
Y1 - 2017/3/22
N2 - Decision making involves dynamic interplay between internal judgements and external perception, which has been investigated in delayed match-to-category (DMC) experiments. Our analysis of neural recordings shows that, during DMC tasks, LIP and PFC neurons demonstrate mixed, time-varying, and heterogeneous selectivity, but previous theoretical work has not established the link between these neural characteristics and population-level computations. We trained a recurrent network model to perform DMC tasks and found that the model can remarkably reproduce key features of neuronal selectivity at the single-neuron and population levels. Analysis of the trained networks elucidates that robust transient trajectories of the neural population are the key driver of sequential categorical decisions. The directions of trajectories are governed by network self-organized connectivity, defining a “neural landscape” consisting of a task-tailored arrangement of slow states and dynamical tunnels. With this model, we can identify functionally relevant circuit motifs and generalize the framework to solve other categorization tasks.
AB - Decision making involves dynamic interplay between internal judgements and external perception, which has been investigated in delayed match-to-category (DMC) experiments. Our analysis of neural recordings shows that, during DMC tasks, LIP and PFC neurons demonstrate mixed, time-varying, and heterogeneous selectivity, but previous theoretical work has not established the link between these neural characteristics and population-level computations. We trained a recurrent network model to perform DMC tasks and found that the model can remarkably reproduce key features of neuronal selectivity at the single-neuron and population levels. Analysis of the trained networks elucidates that robust transient trajectories of the neural population are the key driver of sequential categorical decisions. The directions of trajectories are governed by network self-organized connectivity, defining a “neural landscape” consisting of a task-tailored arrangement of slow states and dynamical tunnels. With this model, we can identify functionally relevant circuit motifs and generalize the framework to solve other categorization tasks.
KW - LIP
KW - PFC
KW - category learning
KW - decision making
KW - delayed match-to-category task
KW - hessian-free algorithm
KW - lateral intraparietal cortex
KW - prefrontal cortex
KW - recurrent neural network
KW - working memory
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U2 - 10.1016/j.neuron.2017.03.002
DO - 10.1016/j.neuron.2017.03.002
M3 - Article
C2 - 28334612
AN - SCOPUS:85015992229
VL - 93
SP - 1504-1517.e4
JO - Neuron
JF - Neuron
SN - 0896-6273
IS - 6
ER -