Correlations and Neuronal Population Information

By Adam Kohn, Ruben Coen-Cagli, Ingmar Kanitscheider, Alexandre Pouget. 2016

361 citations recorded by the source corpus.

Brain function involves the activity of neuronal populations. Much recent effort has been devoted to measuring the activity of neuronal populations in different parts of the brain under various experimental conditions. Population activity patterns contain rich structure, yet many studies have focused on measuring pairwise relationships between members of a larger population---termed noise correlations. Here we review recent progress in understanding how these correlations affect population information, how information should be quantified, and what mechanisms may give rise to correlations. As population coding theory has improved, it has made clear that some forms of correlation are more important for information than others. We argue that this is a critical lesson for those interested in neuronal population responses more generally: Descriptions of population responses should be motivated by and linked to well-specified function. Within this context, we offer suggestions of where current theoretical frameworks fall short.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Statistical and Nonlinear Physics

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

1. N. A. Cayco-Gajic
2. Saurabh Saxena
3. John P. Cunningham
4. R. Silver

In Cognitive Neuroscience

1. N. A. Cayco-Gajic
2. Saurabh Saxena
3. R. Silver
4. John P. Cunningham

In Statistical and Nonlinear Physics

1. N. A. Cayco-Gajic
2. Saurabh Saxena
3. John P. Cunningham
4. R. Silver

Citations in the corpus

• Re-evaluating circuit mechanisms underlying pattern separation (2019, positive): “Theoretical work has highlighted the strong impact that correlations in neural activity can have on information processing and population coding in neural circuits (Averbeck et al., 2006; Kohn et al., 2016).”
• Towards the neural population doctrine. (2019, background): “In fact, pairwise correlations in various brain areas are affected by attention, learning, arousal, and adaptation (see [17] for a review).”