Measuring and interpreting neuronal correlations

By Marlene R. Cohen, Adam Kohn. Nature Neuroscience, 2011

1,108 citations recorded by the source corpus.

Mounting evidence suggests that understanding how the brain encodes information and performs computations will require studying the correlations between neurons. The recent advent of recording techniques such as multielectrode arrays and two-photon imaging has made it easier to measure correlations, opening the door for detailed exploration of their properties and contributions to cortical processing. However, studies have reported discrepant findings, providing a confusing picture. Here we briefly review these studies and conduct simulations to explore the influence of several experimental and physiological factors on correlation measurements. Differences in response strength, the time window over which spikes are counted, spike sorting conventions and internal states can all markedly affect measured correlations and systematically bias estimates. Given these complicating factors, we offer guidelines for interpreting correlation data and a discussion of how best to evaluate the effect of correlations on cortical processing.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/925ae88a46f16d3fb4c142534a6f48f3c4ffdeb1

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

In Cognitive Neuroscience

Citations in the corpus

The human connectome: Origins and challenges (2013, background): “At the microscale, an increasing number of studies have reported on the relation of synaptic connectivity in neuronal circuits and observed patterns in neural dynamics, especially the correlations observed in large neuronal populations (Cohen and Kohn, 2011; Feldt et al., 2011).”
Waking State: Rapid Variations Modulate Neural and Behavioral Responses (2015, neutral): “In contrast to many previous studies (e.g., Kohn and Smith, 2005; Smith and Kohn, 2008), Ecker et al. (2010) found only very weak pairwise noise correlations between well-isolated single units in V1 in the awake monkey, stimulating lively debate in the field (see Cohen and Kohn, 2011).”
Dimensionality in recurrent spiking networks: Global trends in activity and local origins in connectivity (2018, background): “3b and 3c we 311 highlight it here as it helps to reconcile two observations often seen in the literature: 312 relatively weak activity correlations [59, 34, 43] yet relatively low activity dimension [1].”
Spontaneous Behaviors Drive Multidimensional, Brain-wide Activity (2019, background): “1, C and D), suggesting structured activity (30).”
Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding (2014, positive): “We therefore calculated noise correlations, which may determine the efficiency of population coding (Cohen and Kohn, 2011; Ecker et al., 2010), for each epoch (Material and Methods).”
Odor representations in olfactory cortex: distributed rate coding and decorrelated population activity (2012, neutral): “Therefore, near-zero noise correlations in aPC were not a consequence of low firing rates (Cohen and Kohn, 2011; de la Rocha et al., 2007; Kohn and Smith, 2005).”
Intrinsic Membrane Hyperexcitability of ALS Patient-Derived Motor Neurons (2014, background): “Action potentials were sorted by spike morphology and timing to derive clusters corresponding to individual neurons (Figure S1) (Cohen and Kohn, 2011), and a significantly higher average mean firing rate was observed in the SOD1 A4V/+ neurons (p < 10 À15 , t test; Figures 1D and 1E).”
Neural population coding: combining insights from microscopic and mass signals (2015, background): “The computational properties of a population code depend also on the pattern of response correlations between neurons [39].”
Dimensionality reduction for large-scale neural recordings (2014, background): “For decades, studies have considered how the activity of pairs of neurons covaries 21,85,86 .”
Correlations and Neuronal Population Information (2016, background): “251”
Shared cortex-cerebellum dynamics in the execution and learning of a motor task (2019, neutral): “First, dynamics and correlations will vary by the timescale of analysis (Cohen and Kohn, 2011; Kadmon and Sompolinsky, 2015), which in our data are set by the relatively slow GCaMP kinetics.”