Scientific Understanding of Consciousness
Diverse Coupling of Neurons to Populations of Neurons
Nature 521, 511–515 (28 May 2015)
Diverse coupling of neurons to populations in sensory cortex
Michael Okun, et.al.
UCL Institute of Neurology, University College London, London WC1N 3BG, UK
Department of Neuroscience, Physiology and Pharmacology, University College London, London WC1E 6DE, UK
UCL Institute of Ophthalmology, University College London, London EC1V 9EL, UK
Howard Hughes Medical Institute and Department of Neurobiology, Stanford University, Stanford, California 94305-5125, USA
Biozentrum, University of Basel, Klingelbergstrasse 50/70, CH-4056 Basel, Switzerland
Center for Molecular and Behavioral Neuroscience, Rutgers University, 197 University Avenue, Newark, New Jersey 07102, USA
A large population of neurons can, in principle, produce an astronomical number of distinct firing patterns. In cortex, however, these patterns lie in a space of lower dimension, as if individual neurons were “obedient members of a huge orchestra”. Here we use recordings from the visual cortex of mouse (Mus musculus) and monkey (Macaca mulatta) to investigate the relationship between individual neurons and the population, and to establish the underlying circuit mechanisms. We show that neighbouring neurons can differ in their coupling to the overall firing of the population, ranging from strongly coupled ‘choristers’ to weakly coupled ‘soloists’. Population coupling is largely independent of sensory preferences, and it is a fixed cellular attribute, invariant to stimulus conditions. Neurons with high population coupling are more strongly affected by non-sensory behavioural variables such as motor intention. Population coupling reflects a causal relationship, predicting the response of a neuron to optogenetically driven increases in local activity. Moreover, population coupling indicates synaptic connectivity; the population coupling of a neuron, measured in vivo, predicted subsequent in vitro estimates of the number of synapses received from its neighbours. Finally, population coupling provides a compact summary of population activity; knowledge of the population couplings of n neurons predicts a substantial portion of their n2 pairwise correlations. Population coupling therefore represents a novel, simple measure that characterizes the relationship of each neuron to a larger population, explaining seemingly complex network firing patterns in terms of basic circuit variables.
The cortex represents its computations through the joint activity of multiple neurons. This activity can be remarkably diverse even among neighbouring neurons, belonging to the same morphological and laminar cell class. In sensory cortex, neighbouring neurons not only respond to diverse stimulus, but also use diverse strategies to encode information. For example, mean firing rate differs by orders of magnitude across neurons, and it appears to constitute an invariant property of each cell, persisting across multiple stimulus conditions and spontaneous activity. We asked whether there are other invariant dimensions that characterize the diversity of firing of cortical neurons. Ideally, such dimensions would not only help explain the complex patterns of activity produced by cortical populations, but also relate directly to underlying circuit variables.
These results indicate that much of the pairwise correlation in the population is explained by the coupling of each neuron to population rate, with coupling strengths that vary between neurons. The underlying model is parsimonious, requiring only order n parameters to predict order n2 pairwise correlations. Moreover, the model is intuitive, involving procedures among the simplest in neuroscience—summing the activity of multiple neurons, and correlating the spike train of each neuron with the result.
Population coupling constitutes a previously unappreciated dimension characterizing the relationship of individual neurons to population activity. Strongly coupled neurons (choristers) are more strongly activated during multiple conditions that nonspecifically increase the activity of their local network: not only sensory stimuli, but also spontaneous fluctuations, polysynaptic optogenetic stimulation, and top-down modulation. Conversely, weakly coupled neurons (soloists) are more immune to these population-wide events. Population coupling differs both across and within cell classes; it remains to be determined whether this within-class diversity reflects further subdivisions of these classes (such as pyramidal cells with different long-range axonal targets), or continuous within-class variation in cellular parameters such as input connection probability. Moreover, a neuron’s population coupling need not be fixed for life; neurons that rats learn to use while controlling a brain–machine interface show increased correlation with LFP, indicating increased population coupling, and thus suggesting an increase in mean synaptic input strength. This single, simple variable relating each neuron’s participation in the population code to underlying circuit connectivity may prove critical to understanding cortical computation and plasticity.
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