New Granger Framework Maps How Neurons Turn Sound Into Behavior

Every time you recognize a friend’s voice in a noisy room, billions of neurons across your cortex are receiving, transforming, and relaying information, ultimately converting patterns of sound waves into a decision. Neuroscientists have long tried to trace this journey, but the tools they use tend to fragment the problem into disconnected pieces. One study might ask which neurons respond to a stimulus, another might ask how neural activity predicts behavior, and a third might map which cells influence which. A new study published in PLOS Computational Biology argues that this fragmentation has been holding the field back, and it offers a unified statistical framework that captures all three questions at once.

The work, led by Sahar Khosravi and Behtash Babadi of the University of Maryland together with Nikolas Francis and Patrick Kanold, introduces what the authors call a Granger sensori-behavioral functional taxonomy, or G-taxonomy, for neuronal ensembles recorded with two-photon calcium imaging. The central idea is deceptively simple: instead of treating encoding, connectivity, and behavioral readout as separate analyses, the framework extracts all of them from the same data in a single coherent model, using the language of Granger causality to describe how information flows between stimuli, neurons, and behavior.

Granger causality, a concept borrowed from econometrics, defines a directional influence in terms of temporal predictability. If the past activity of neuron A improves predictions of neuron B beyond what B’s own past already reveals, then A is said to Granger-cause B. Applied to neuroscience, this notion promises something remarkable: a wiring diagram of functional influence that is estimated directly from data, without requiring the invasive perturbation of individual cells. But applying it to two-photon calcium imaging has proven notoriously difficult, and the new paper confronts those difficulties head-on.

The problem begins with the physics of the measurement itself. Two-photon microscopy does not record spikes, the electrical impulses neurons use to communicate. Instead, it tracks fluorescent calcium indicators whose glow rises and falls on a much slower timescale than the underlying spiking activity. Calcium transients are sluggish, indirect, and corrupted by noise, which means the fast temporal structure that Granger analysis depends on is largely hidden from view. On top of this, the relationship between spikes and calcium is nonlinear, and the recorded signals reflect a mixture of genuine neural dynamics and the latent interplay between external stimuli and endogenous brain processes.

To overcome these obstacles, the researchers built their framework on an integration of several sophisticated statistical techniques. State-space modeling provides a mathematical scaffold for inferring the latent spiking activity that generated the observed calcium traces, treating the unobserved spikes as hidden states that evolve over time. Variational inference, a modern computational approach popular in machine learning, makes the estimation of these hidden states tractable even for large populations of neurons. Point-process models then describe the stochastic timing of spikes in a statistically principled way, allowing the framework to work with the discrete, event-like nature of neural firing rather than pretending spikes are smooth continuous signals.

With this machinery in place, the framework computes Granger causal effects along three distinct axes: from neuron to neuron, capturing functional connectivity within the recorded ensemble; from external stimuli to neurons, capturing sensory encoding; and from neurons to behavior, capturing the readout side of the circuit. Inspired by the intersection information framework, the method goes one step further and identifies neurons that encode specific features of sensory stimuli which are actually relevant to the animal’s behavioral report, a distinction that most encoding analyses ignore entirely.

The result is a taxonomy that sorts neurons into functionally distinct groups based on their sensori-behavioral relevance. Some cells may encode stimulus features without any apparent link to behavior, others may influence their neighbors without strong sensory tuning, and a select subset may sit at the critical junction, carrying information about the stimulus that demonstrably informs the animal’s decision. This classification, the authors suggest, offers a far richer picture of cortical computation than simple tuning curves or correlation-based connectivity maps can provide.

Before trusting the method with real brains, the team validated it on simulated data, where the ground truth was known. These simulation studies revealed significant improvements over existing techniques, suggesting that the framework recovers directional interactions more faithfully than previous approaches that either ignored the indirect nature of calcium measurements or treated encoding and connectivity in isolation. The simulations also demonstrated that the method remains robust in the presence of the noise levels and slow dynamics characteristic of real imaging experiments.

The researchers then applied their framework to experimentally recorded two-photon imaging data from the mouse auditory cortex, area A1, during two behavioral conditions: passive listening to tones and active tone discrimination. In the passive condition, animals simply heard sounds; in the active condition, they had to discriminate between tones and report their choice, allowing the experimenters to link neural activity to both stimulus and behavior on a trial-by-trial basis. The analysis identified distinct groups of cells with diverse sensori-behavioral roles, confirming that even within a single cortical area, neurons occupy strikingly different positions in the flow from sensation to action.

Perhaps most intriguingly, the framework revealed changes in functional connectivity associated with correct versus incorrect behavioral trials. When mice performed the discrimination task accurately, the pattern of Granger influences among neurons differed from trials in which they erred, hinting that the moment-to-moment state of a cortical network, not just its average properties, shapes whether sensory information is successfully transformed into the right decision. Such trial-level differences are exactly the kind of signal that fragmented analyses tend to miss, because they emerge only when encoding, connectivity, and behavior are examined within the same statistical model.

The implications extend well beyond the auditory cortex. Two-photon calcium imaging has become one of the most widely used tools in modern neuroscience, generating massive datasets from visual cortex, hippocampus, frontal areas, and beyond. A general-purpose method for extracting directional, behaviorally relevant structure from such data could reshape how laboratories interpret their recordings, turning raw fluorescence movies into functional maps of information flow. The framework’s data-driven character means it makes few assumptions about what the circuit should look like, letting the statistics speak for themselves.

There are, of course, important caveats that the authors and the broader field continue to weigh. Granger causality inferred from observational data reflects predictive relationships, not necessarily direct physical connections, and calcium imaging still imposes temporal limits that even the best state-space methods cannot fully erase. The framework also requires substantial computational resources, since variational inference over large populations is demanding. Yet the study’s combination of rigorous simulation benchmarks and successful application to real experimental data suggests these hurdles are surmountable, and that the approach can deliver on its promise in practice.

What makes this work resonate beyond its technical contributions is the question it ultimately addresses: how does a distributed population of neurons transform sensory inputs into behaviorally relevant representations? By refusing to split that question into disconnected sub-problems, the G-taxonomy framework offers a principled answer in the form of a single, unified statistical picture, one in which every neuron’s role, from stimulus encoding to influence on neighbors to behavioral readout, can be quantified from the same recording. As imaging technology continues to scale up to ever larger ensembles, tools like this one may become essential for reading the grammar of cortical circuits, and for understanding not just which neurons fire, but why it matters for what the animal does next.

Subject of Research: A unified Granger causal framework for extracting stimulus-to-neuron, neuron-to-neuron, and neuron-to-behavior interactions from two-photon calcium imaging data

Article Title: Granger sensori-behavioral functional taxonomy of neuronal ensemble activity from two-photon calcium imaging data

Article References: Khosravi, S., Francis, N. A., Kanold, P. O., & Babadi, B. (2026). Granger sensori-behavioral functional taxonomy of neuronal ensemble activity from two-photon calcium imaging data. PLOS Computational Biology, 22(9), e1014820. https://doi.org/10.1371/journal.pcbi.1014820

Image Credits: AI Generated

DOI: 10.1371/journal.pcbi.1014820

Keywords: two-photon calcium imaging, Granger causality, functional connectivity, neural encoding, auditory cortex, state-space modeling, variational inference, point processes, mouse behavior, computational neuroscience, neuronal ensembles, sensory discrimination