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Behavioral/Systems/Cognitive Functional, But Not Anatomical, Separation of "What"

Summary: Behavioral/Systems/Cognitive
Functional, But Not Anatomical, Separation of "What"
and "When" in Prefrontal Cortex
Christian K. Machens,1 Ranulfo Romo,2 and Carlos D. Brody3,4,5
1Group for Neural Theory, Inserm U960, De´partement d'e´tudes cognitives, E´cole normale supe´rieure, 75005 Paris, France, 2Instituto de Fisiología Celular­
Neurociencias, Universidad Nacional Auto´noma de Me´xico, 04510 Me´xico D.F., Me´xico, and 3Howard Hughes Medical Institute, 4Princeton Neuroscience
Institute, and 5Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544
How does the brain store information over a short period of time? Typically, the short-term memory of items or values is thought to be
stored in the persistent activity of neurons in higher cortical areas. However, the activity of these neurons often varies strongly in time,
activity of neurons in the prefrontal cortex of monkeys performing a working memory task. As often observed in higher cortical areas,
different neurons have highly heterogeneous patterns of activity, making interpretation of the data difficult. To overcome these prob-
lems, we developed a method that finds a new representation of the data in which heterogeneity is much reduced, and time- and
memory-related activities became separate and easily interpretable. This new representation consists of a few fundamental activity
componentsthatcapture95%ofthefiringratevarianceof 800neurons.Surprisingly,thememory-relatedactivitycomponentsaccount
for 20% of this firing rate variance. The observed heterogeneity of neural responses results from random combinations of these
fundamental components. Based on these components, we constructed a generative linear model of the network activity. The model
substrate. Testable predictions of this hypothesis are proposed. We suggest that our method may be applied to data from other tasks in
which neural responses are highly heterogeneous across neurons, and dependent on more than one variable.


Source: Andrzejak, Ralph Gregor - Departament de Tecnologia, Universitat Pompeu Fabra
Machens, Christian - Group for Neural Theory, École Normale Supérieure, Paris


Collections: Biology and Medicine; Computer Technologies and Information Sciences