The Structure of Multi-Neuron Firing Patterns in Primate Retina

TLDR

Current understanding of neural circuits is limited by the difficulty of exploring vast interactions, and pairwise measurements alone cannot determine the prevalence of multi‑neuron firing patterns that may convey distinct visual messages. The authors present a new approach that dramatically reduces the complexity of exploring multi‑neuron firing patterns. Large‑scale multi‑electrode recordings of hundreds of ON and OFF parasol retinal ganglion cells were analyzed under the assumption that multi‑cell firing patterns arise from pairwise interactions limited to adjacent cells. Parasol cells showed substantial pairwise correlations, and maximum‑entropy modeling revealed that pairwise and adjacent interactions explain ~98 % of deviations from independence, providing a way to limit network interaction complexity.

Abstract

Current understanding of many neural circuits is limited by our ability to explore the vast number of potential interactions between different cells. We present a new approach that dramatically reduces the complexity of this problem. Large-scale multi-electrode recordings were used to measure electrical activity in nearly complete, regularly spaced mosaics of several hundred ON and OFF parasol retinal ganglion cells in macaque monkey retina. Parasol cells exhibited substantial pairwise correlations, as has been observed in other species, indicating functional connectivity. However, pairwise measurements alone are insufficient to determine the prevalence of multi-neuron firing patterns, which would be predicted from widely diverging common inputs and have been hypothesized to convey distinct visual messages to the brain. The number of possible multi-neuron firing patterns is far too large to study exhaustively, but this problem may be circumvented if two simple rules of connectivity can be established: (1) multi-cell firing patterns arise from multiple pairwise interactions, and (2) interactions are limited to adjacent cells in the mosaic. Using maximum entropy methods from statistical mechanics, we show that pairwise and adjacent interactions accurately accounted for the structure and prevalence of multi-neuron firing patterns, explaining ∼98% of the departures from statistical independence in parasol cells and ∼99% of the departures that were reproducible in repeated measurements. This approach provides a way to define limits on the complexity of network interactions and thus may be relevant for probing the function of many neural circuits.