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Flexible information routing in neural populations through stochastic comodulation
Caroline Haimerl · Cristina Savin · Eero Simoncelli

Thu Dec 12 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #186

Humans and animals are capable of flexibly switching between a multitude of tasks, each requiring rapid, sensory-informed decision making. Incoming stimuli are processed by a hierarchy of neural circuits consisting of millions of neurons with diverse feature selectivity. At any given moment, only a small subset of these carry task-relevant information.
In principle, downstream processing stages could identify the relevant neurons through supervised learning, but this would require many example trials. Such extensive learning periods are inconsistent with the observed flexibility of humans or animals, who can adjust to changes in task parameters or structure almost immediately. Here, we propose a novel solution based on functionally-targeted stochastic modulation. It has been observed that trial-to-trial neural activity is modulated by a shared, low-dimensional, stochastic signal that introduces task-irrelevant noise. Counter-intuitively this noise is preferentially targeted towards task-informative neurons, corrupting the encoded signal. However, we hypothesize that this modulation offers a solution to the identification problem, labeling task-informative neurons so as to facilitate decoding. We simulate an encoding population of spiking neurons whose rates are modulated by a shared stochastic signal, and show that a linear decoder with readout weights approximating neuron-specific modulation strength can achieve near-optimal accuracy. Such a decoder allows fast and flexible task-dependent information routing without relying on hardwired knowledge of the task-informative neurons (as in maximum likelihood) or unrealistically many supervised training trials (as in regression).

Author Information

Caroline Haimerl (New York University)
Cristina Savin (NYU)
Eero Simoncelli (HHMI / New York University)

Eero P. Simoncelli received the B.S. degree in Physics in 1984 from Harvard University, studied applied mathematics at Cambridge University for a year and a half, and then received the M.S. degree in 1988 and the Ph.D. degree in 1993, both in Electrical Engineering from the Massachusetts Institute of Technology. He was an Assistant Professor in the Computer and Information Science department at the University of Pennsylvania from 1993 until 1996. He moved to New York University in September of 1996, where he is currently a Professor in Neural Science, Mathematics, and Psychology. In August 2000, he became an Associate Investigator of the Howard Hughes Medical Institute, under their new program in Computational Biology. In Fall 2020, he resigned his HHMI appointment to become the scientific director of the Center for Computational Neuroscience at the Flatiron Institute, of the Simons Foundation. His research interests span a wide range of topics in the representation and analysis of visual images, in both machine and biological systems.

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