Abstract
With the availability of cellular-resolution connectivity maps, connectomes, from the mammalian nervous system, it is in question how informative such massive connectomic data can be for the distinction of local circuit models in the mammalian cerebral cortex. Here, we investigated whether cellular-resolution connectomic data can in principle allow model discrimination for local circuit modules in layer 4 of mouse primary somatosensory cortex. We used approximate Bayesian model selection based on a set of simple connectome statistics to compute the posterior probability over proposed models given a to-be-measured connectome. We find that the distinction of the investigated local cortical models is faithfully possible based on purely structural connectomic data with an accuracy of more than 90%, and that such distinction is stable against substantial errors in the connectome measurement. Furthermore, mapping a fraction of only 10% of the local connectome is sufficient for connectome-based model distinction under realistic experimental constraints. Together, these results show for a concrete local circuit example that connectomic data allows model selection in the cerebral cortex and define the experimental strategy for obtaining such connectomic data.
Highlights
With the availability of cellular-resolution connectivity maps, connectomes, from the mammalian nervous system, it is in question how informative such massive connectomic data can be for the distinction of local circuit models in the mammalian cerebral cortex
We developed and tested a model selection approach on the main models proposed so far for local cortical circuits (Fig. 1b) ranging from pairwise random Erdős–Rényi (ER16) to highly structured “deep” layered networks used in machine learning[17,18]
To develop our approach we focus on a cortical module in mouse somatosensory cortex, a “barrel” in layer 4 (L4), a main input layer to the sensory cortex[19,20,21]
Summary
With the availability of cellular-resolution connectivity maps, connectomes, from the mammalian nervous system, it is in question how informative such massive connectomic data can be for the distinction of local circuit models in the mammalian cerebral cortex. For the mammalian cerebral cortex, the situation can be considered more complicated: it can be argued that it is not even known which computation a given cortical area or local circuit module carries out In this situation, hypotheses about the potentially relevant computations and about their concrete implementations are to be explored simultaneously. This illustrates that while it is impossible to uniquely equate computations with their possible circuit-level implementations, the ability to discriminate between proposed models would allow to narrow down the hypothesis space both about computations and their circuit-level implementations in the cortex With this background, the question whether purely structural connectomic data is sufficiently informative to discriminate between several possible previously proposed models and a range of possible cortical computations is of interest. We asked whether for a concrete cortical circuit module, the “barrel” of a cortical column in mouse somatosensory cortex, the measurement of the local connectome can in principle serve as an arbiter for a set of possibly implemented local cortical models and their associated computations
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