How Neural Populations Communicate

This piece, by Onno Berkan, was published on 03/14/25. The original text, by Gokçen et al., was submitted to NeurIPS 2023.

This CMU study introduces mDLAG (multi-population Delayed Latents Across Groups) to better understand how different brain regions communicate with each other. It uses simultaneous recordings from primary, secondary, and tertiary visual cortex.

mDLAG is meant to solve three key problems: identifying which brain regions communicate, determining the direction of information flow between regions, and understanding how these signals change over time. While focusing mainly on the visual system in the context of this study, it is meant to eventually expand into the whole brain. This is particularly important because many brain regions often communicate back and forth simultaneously, making it difficult to separate overlapping signals.

Researchers first tested it on simulated data where they knew the answers. The mDLAG approach successfully identified communication patterns between different neural populations and accurately measured the timing of signals passing between regions. They then applied their method to actual brain recordings from three visual areas in monkeys (called V1, V2, and V3d). This analysis revealed that regions with overlapping visual receptive fields (which process information from the same part of visual space) communicated more strongly.

One of the key findings was that brain regions with aligned visual receptive fields showed unique patterns of communication that weren't present between non-aligned regions. The study found fast, stimulus-related signals and slower, more general communication patterns between these brain areas. Some signals flowed forward (from V1 to V3d), while others flowed backward (from V3d to V2 to V1).

The researchers emphasize that their method improves upon previous approaches in several ways. Unlike earlier techniques, mDLAG can handle multiple brain regions simultaneously and detect precise timing differences in how signals flow between areas. It's also flexible enough to be used with different types of brain recordings and can even incorporate behavioral data.

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