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Inferring decoding strategies for multiple correlated neural populations.

Kaushik J Lakshminarasimhan1, Alexandre Pouget2,3, Gregory C DeAngelis3

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This study reconciles conflicting findings in neural perception research by developing a new method to infer brain area function. It reveals two distinct decoding schemes in macaque monkeys, depending on neural noise correlations.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Systems Neuroscience

Background:

  • Understanding the neural basis of perception traditionally relies on separate studies of neuron-behavior correlation and causal manipulation.
  • These distinct approaches have historically yielded conflicting conclusions regarding the functional roles of specific brain areas.
  • Existing theories focusing solely on choice-related neuronal activity struggle to reconcile these discrepancies without extensive large-scale recordings of interneuronal correlations.

Purpose of the Study:

  • To develop a novel theoretical framework that integrates findings from inactivation experiments with existing neural coding theories.
  • To demonstrate a method for inferring coarse-scale decoding weights of different brain areas without needing precise knowledge of their correlation structure.
  • To apply this technique to macaque monkey neural data from a heading discrimination task to identify neural coding schemes.

Main Methods:

  • Expanding current theories of neural coding by incorporating results from neural inactivation experiments.
  • Developing a method to infer decoding weights across different brain areas at a coarse scale.
  • Analyzing neural data from macaque monkeys performing a heading discrimination task using the developed theoretical framework.

Main Results:

  • Identified two opposing decoding schemes within two different cortical areas.
  • Demonstrated that the consistency of each decoding scheme with the observed data depends on the nature of correlated neural noise.
  • Showed that the functional role of brain areas can be inferred without precise measurement of underlying noise correlations.

Conclusions:

  • The developed theory reconciles conflicting results from neuron-behavior correlation and causal manipulation studies in understanding neural perception.
  • The method allows for the inference of brain area decoding schemes by accounting for correlated noise, even without direct measurement.
  • The study proposes specific, experimentally testable predictions to differentiate between the identified decoding scenarios, advancing the understanding of neural coding.