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An appealing new principle for neural population codes is that correlationsamong neurons organize neural activity patterns into a discrete set ofclusters, which can each be viewed as a noise-robust population "codeword".Previous studies assumed that these codewords corresponded geometrically withlocal peaks in the probability landscape of neural population responses. Here,we analyze multiple datasets of the responses of ~150 retinal ganglion cellsand show that local probability peaks are absent under broad, non-repeatedstimulus ensembles, which are characteristic of natural behavior. However, wefind that neural activity still forms noise-robust clusters in this regime,albeit clusters with a different geometry. We start by defining a soft localmaximum, which is a local probability maximum when constrained to a fixed spikecount. Next, we show that soft local maxima are robustly present, and canmoreover be linked across different spike count levels in the probabilitylandscape to form a "ridge". We found that these ridges are comprised ofcombinations of spiking and silence in the neural population such that all ofthe spiking neurons are members of the same neuronal community, a notion fromnetwork theory. We argue that a neuronal community shares many of theproperties of Donald Hebb's classic cell assembly, and show that a simple,biologically plausible decoding algorithm can recognize the presence of aspecific neuronal community.

Noise-Robust Modes of the Retinal Population Code have the Geometry of "Ridges" and Correspond with Neuronal Communities