Editing AI for Neuroscience (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ AI Tools in Neuroscience Applications
! Application !! AI Approach !! Maturity !! Key Dataset/Tool
|-
| Spike sorting || Clustering + CNNs || Deployed (Kilosort) || Allen Brain Atlas recordings
|-
| Cell segmentation || U-Net, CellPose || Deployed || Mouse visual cortex imaging
|-
| fMRI decoding || Linear + deep decoders || Research || HCP, NSD datasets
|-
| BCI decoding || LDA, RNN, CNNs || Clinical (BrainGate) || BCI Competition datasets
|-
| Neural representational models || CNN as brain model || Research || NSD, THINGS EEG
|-
| Connectome analysis || GNNs, graph analysis || Research || C. elegans, FlyWire
|}

'''Failure modes''': EEG BCI systems are sensitive to electrode placement, skin impedance, and user fatigue — models degrade rapidly outside calibration conditions. fMRI decoders overfit to individual subjects (highly individual brain organization). Neural recording instability — electrode signals drift over days/weeks, causing model degradation. Conflating model performance with mechanistic understanding.
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