Unraveling Functional Diversity of Cortical Synaptic Architecture Through the Lens of Population Coding

Abstract

The synaptic inputs to single cortical neurons exhibit substantial diversity in their sensory-driven activity. What this diversity reflects is unclear, and appears counter-productive in generating selective somatic responses to specific stimuli. One possibility is that this diversity reflects the propagation of information from one neural population to another. To test this possibility, we bridge population coding theory with measurements of synaptic inputs recorded in vivo with two-photon calcium imaging. We construct a probabilistic decoder to estimate the stimulus orientation from the responses of a realistic, hypothetical input population of neurons to compare with synaptic inputs onto individual neurons of ferret primary visual cortex (V1) recorded with two-photon calcium imaging in vivo. We find that optimal decoding requires diverse input weights and provides a straightforward mapping from the decoder weights to excitatory synapses. Analytically derived weights for biologically realistic input populations closely matched the functional heterogeneity of dendritic spines imaged in vivo with two-photon calcium imaging. Our results indicate that synaptic diversity is a necessary component of information transmission and reframes studies of connectivity through the lens of probabilistic population codes. These results suggest that the mapping from synaptic inputs to somatic selectivity may not be directly interpretable without considering input covariance and highlights the importance of population codes in pursuit of the cortical connectome.

Synaptic weights that correlate with presynaptic selectivity increase decoding performance

“…The same orientation θ is shown to all input neurons and the firing rate of presynaptic neuron i is given by the shown stimulus and their respective tuning curve, defined by their PO and width κ i . A decoder receives inputs from all neurons in this population and estimates the shown orientation based on their tuning curves and firing rates [ 27 ]. We then derive a decoder, which is based on maximum likelihood inference (ML decoder, see Methods for details) [ 28 ].…”

Synaptic weights that correlate with presynaptic selectivity increase decoding performance

“…The same orientation θ is shown to all input neurons and the firing rate of presynaptic neuron i is given by the shown stimulus and their respective tuning curve, defined by their PO and width κ i . A decoder receives inputs from all neurons in this population and estimates the shown orientation based on their tuning curves and firing rates (Yates and Scholl 2022). We then derive a decoder, which is based on maximum likelihood inference (ML decoder, see Methods for details) (Abbott and Dayan 2005).…”

Cellular and laminar architecture: A short history and commentary

“…That is, in mouse V2M, thalamic input from the lateroposterior nucleus was associated preferentially with apical tufts in layer 1, but cortical inputs from orbital frontal and anterior cingulate areas (aka FBK) targeted basal, oblique, and tuft branches, with tuft input assessed as the weakest. Input heterogeneity at the synaptic level has been proposed as a necessary component of information transmission in the context of populational coding (Yates & Scholl, 2022).…”