Journal
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
Volume 112, Issue 8, Pages 2533-2538Publisher
NATL ACAD SCIENCES
DOI: 10.1073/pnas.1418092112
Keywords
neural coding; information theory; phase transitions; scaling
Categories
Funding
- NIH [R01EY019493, R01EY022933, P30EY019005]
- National Science Foundation (NSF) CAREER Award [1254123]
- Salk Institute Innovations program
- McKnight Scholarships
- Sloan Fellowships
- Ray Thomas Edwards Award
- Pew Charitable Trusts
- E. Matilda Ziegler Foundation
- Stanford Medical Scientist Training Program
- NSF Integrative Graduate Education and Research Traineeship graduate fellowship
- Division Of Physics
- Direct For Mathematical & Physical Scien [1427654] Funding Source: National Science Foundation
- Division Of Physics
- Direct For Mathematical & Physical Scien [1308264] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1254123] Funding Source: National Science Foundation
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Computation in the brain involves multiple types of neurons, yet the organizing principles for how these neurons work together remain unclear. Information theory has offered explanations for how different types of neurons can maximize the transmitted information by encoding different stimulus features. However, recent experiments indicate that separate neuronal types exist that encode the same filtered version of the stimulus, but then the different cell types signal the presence of that stimulus feature with different thresholds. Here we show that the emergence of these neuronal types can be quantitatively described by the theory of transitions between different phases of matter. The two key parameters that control the separation of neurons into subclasses are the mean and standard deviation (SD) of noise affecting neural responses. The average noise across the neural population plays the role of temperature in the classic theory of phase transitions, whereas the SD is equivalent to pressure or magnetic field, in the case of liquid-gas and magnetic transitions, respectively. Our results account for properties of two recently discovered types of salamander Off retinal ganglion cells, as well as the absence of multiple types of On cells. We further show that, across visual stimulus contrasts, retinal circuits continued to operate near the critical point whose quantitative characteristics matched those expected near a liquid-gas critical point and described by the nearest-neighbor Ising model in three dimensions. By operating near a critical point, neural circuits can maximize information transmission in a given environment while retaining the ability to quickly adapt to a new environment.
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