Try it!
You can use this for any form of 2D/3D electrode array.
n/n 🍸
github.com/neurovium/Sp...
Implementation of Spatially Masked Regression (SMR), a framework that reconstructs neural signals while masking nearby electrodes to separate local redundancy from distributed structure. Includes S...
Recent work from the lab: big contrast between the synaptic dynamics of excitatory and inhibitory synapses!
Looking forward to reading this Richard @neuronaud.bsky.social
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 🧵👇
Surrogate controls showed that SMR was not simply exploiting marginal statistics.
Phase-shuffled, IAAFT, and block-shuffled surrogates all substantially reduced performance.
So reconstruction depends on structured temporal and cross-channel organization, not just spectra or amplitudes.
6/n 🧵👇
How local is a “local” field potential?
In our new paper 📜, we introduce Spatially Masked Regression (SMR), a reconstruction-based framework for asking how much of an electrode signal is locally redundant, and how much is embedded in broader distributed dynamics.
1/n 🧵👇
arxiv.org/abs/2606.11415
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 🧵👇
