Do hippocampal neuronal functions arise from simple computations?

Determine whether the principal functions of mouse hippocampal CA1 neurons—encoding the animal’s location, mapping environmental features, and storing memories of past events—arise from simple computations that depend only on direct input-output dependencies without requiring interactions between inputs (i.e., perceptron-like logistic computations).

Background

The paper investigates whether neuronal activity can be explained by minimal computations that capture only direct dependencies between a neuron’s output and each input individually, without modeling higher-order interactions. In the context of the mouse hippocampus, CA1 neurons are known to support navigation and memory-related functions. The authors note an explicit uncertainty about whether these complex functions could be produced by such simple, low-dimensional computations.

To address this, they develop a maximum-entropy framework that yields a logistic neuron model consistent with measured pairwise dependencies and then empirically test its sufficiency across large-scale recordings. The open question focuses specifically on whether the hippocampus’s functional roles can be attributed to these minimal computations without input interactions.