Tensor formalism for predicting synaptic connections with ensemble modeling or optimization

TL;DR

This paper introduces a tensor formalism approach to unify optimization and ensemble modeling for predicting synaptic connectivity, providing analytical solutions and geometric insights applicable to neuroscience and other scientific problems.

Contribution

It develops a tensor-based framework that reduces complex nonlinear problems in neural connectivity prediction to solvable linear problems, unifying different modeling approaches.

Findings

Derived analytical solutions for synaptic weight configurations.

Applied the framework to zebrafish neural response data.

Provided geometric insights for constrained optimization in neural modeling.

Abstract

Theoretical neuroscientists often try to understand how the structure of a neural network relates to its function by focusing on structural features that would either follow from optimization or occur consistently across possible implementations. Both optimization theories and ensemble modeling approaches have repeatedly proven their worth, and it would simplify theory building considerably if predictions from both theory types could be derived and tested simultaneously. Here we show how tensor formalism from theoretical physics can be used to unify and solve many optimization and ensemble modeling approaches to predicting synaptic connectivity from neuronal responses. We specifically focus on analyzing the solution space of synaptic weights that allow a threshold-linear neural network to respond in a prescribed way to a limited number of input conditions. For optimization purposes, we compute the synaptic weight vector that minimizes an arbitrary quadratic loss function. For ensemble modeling, we identify synaptic weight features that occur consistently across all solutions bounded by an arbitrary ellipsoid. We derive a common solution to this suite of nonlinear problems by showing how each of them reduces to an equivalent linear problem that can be solved analytically. Although identifying the equivalent linear problem is nontrivial, our tensor formalism provides an elegant geometrical perspective that allows us to solve the problem approximately in an analytical way or exactly using numeric methods. The final algorithm is applicable to a wide range of interesting neuroscience problems, and the associated geometric insights may carry over to other scientific problems that require constrained optimization. We conclude by applying and testing our ensemble modeling framework to whole-brain recordings of larval zebrafish performing optomotor and optokinetic responses.