Computational neuroscience, Physics of Complex Systems, Bio-Inspired Intelligence, Foundations of Physical Computing
https://neurovium.science/
https://compneuro.mit.edu/home
Nima Dehghani
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The usual approach is functional connectivity: correlation, coherence, PLV, mutual information, graph edges, etc.
These methods ask whether two signals are statistically related.
SMR asks a different question:
Can one electrode’s signal be reconstructed from the rest of the array?
2/n 🧵👇
In this paper we introduce “Spatially Masked Regression” (SMR) as a reconstruction-based framework for separating local redundancy from distributed predictability in electrophysiological recordings. I...
As local neighborhoods were progressively excluded, performance decreased.
So local electrodes carry the strongest predictive information.
But reconstruction remained above zero even under strict local masking.
The signal is locally anchored, but not purely local.
5/n 🧵👇
