Inferring Mesoscale Models of Neural Computation

TL;DR

This paper advocates for developing mesoscale models of neural computation to bridge the gap between microscale neural activity and macroscale behavior, utilizing machine learning to infer circuit structure and function.

Contribution

It introduces a framework for automatically inferring mesoscale neural models by leveraging structural and functional data with machine learning tools, emphasizing regularity and motifs.

Findings

Proposes a blueprint for mesoscale neural modeling.

Highlights the importance of circuit geometry in computation.

Provides tools for designing and testing mesoscale theories.

Abstract

Recent years have seen dramatic progress in the development of techniques for measuring the activity and connectivity of large populations of neurons in the brain. However, as these techniques grow ever more powerful---allowing us to even contemplate measuring every neuron in entire brain---a new problem arises: how do we make sense of the mountains of data that these techniques produce? Here, we argue that the time is ripe for building an intermediate or "mesoscale" computational theory that can bridge between single-cell (microscale) accounts of neural function and behavioral (macroscale) accounts of animal cognition and environmental complexity. Just as digital accounts of computation in conventional computers abstract away the non-essential dynamics of the analog circuits that implementing gates and registers, so too a computational account of animal cognition can afford to abstract from the non-essential dynamics of neurons. We argue that the geometry of neural circuits is essential in explaining the computational limitations and technological innovations inherent in biological information processing. We propose a blueprint for how to employ tools from modern machine learning to automatically infer a satisfying mesoscale account of neural computation that combines functional and structural data, with an emphasis on learning and exploiting regularity and repeating motifs in neuronal circuits. Rather than suggest a specific theory, we present a new class of scientific instruments that can enable neuroscientists to design, propose, implement and test mesoscale theories of neural computation.