bair lab / uw neurobiology & biophysics / two active projects
from living cortex
to learned weights
dec 2024 - present / surgical assistance + computational neuroscience
The two projects ask the same broad question at different levels: how does a visual system build and combine selectivity? XORI measures nonlinear suppression across depth in macaque V1. MechV1 works backward through trained CNN weights to find the connectivity patterns that predict color, form, and texture selectivity.
cross-orientation suppression across cortical depth
Two-photon calcium imaging in anesthetized macaque V1. Orthogonal plaids are compared with the linear sum predicted from their component gratings, revealing how facilitation, suppression, and tuning structure change through layer 2/3.
One analysis path from stimulus presentation to a depth-resolved account of nonlinear integration.
imaging
Two-photon calcium imaging in anesthetized macaque V1 using PHP.eB-CAG-GCaMP6s. Drifting gratings and orthogonal plaids are presented at 50% contrast, 4 Hz, and 4 cycles per degree.
- 28 fields of view, 140 to 518 µm deep
- Superficial through deep layer 2/3
- Collaboration with the Allen Institute and WANPRC
ROI extraction
Suite2p performs automated cell detection, adapted for primate cortical data, with neuropil correction and signal-quality checks applied to each trace.
- 4,785 ROIs across 28 fields of view
- Neuropil subtraction and signal cleanup
- SNR filtering at multiple thresholds
XORI metric
For each ROI, a linear prediction is assembled from the component grating responses, shifted by 90 degrees and summed with baseline correction, then compared with the observed plaid response.
- S: plaid response divided by linear prediction
- S > 1 indicates facilitation; S < 1 indicates suppression
- C: Pearson correlation between predicted and observed tuning
statistics
Mixed-effects models account for ROIs nested within fields and sessions. Mediation analysis tests how much of the depth relationship is carried by tuning covariates.
- Linear mixed-effects models in statsmodels
- Random intercepts for session and field
- Mediation estimates: spatial frequency roughly 40%; bandwidth roughly 21%
xori / current results
The current analysis supports a gradual laminar change, not a binary layer boundary.
I assist with non-human-primate surgical preparation and optical-window procedures, then own the XORI analysis path: ROI processing, metric computation, mixed-effects modeling, mediation analyses, and scientific figures.
mechv1: reading selectivity from network weights
A trained CNN already computes its activations exactly. The scientific question is different: can its connectivity alone reveal what a unit will care about, and can those weight motifs explain how color, form, and texture selectivity are assembled across layers?
The decoder is a probe, not the explanation. Its job is to prove that predictive structure exists before tracing which connectivity motifs carry it.
reproducible network identity
The analysis pins exact weight files and layer definitions. Caffe AlexNet is treated separately from torchvision AlexNet because similar architectures do not imply interchangeable unit identities or selectivity labels.
independent functional labels
Hand-labeled color-sensitivity (CSI), form/outline, and texture indices provide a functional target independent of the weight decoder. Self-generated labels remain a separate diagnostic lane.
weight-only decoding
Ridge models use only incoming weight tensors and traced upstream summaries. Leave-one-unit-out validation, shuffled-label controls, 1,000-permutation tests, and a 39-test validation suite separate a real signal from memorization or pipeline drift.
from prediction to mechanism
Depth tracing asks whether earlier connectivity improves prediction. Coefficients, sparse alternatives, and architecture-aware traces are then inspected for motifs that can be stated as a mechanism rather than a black-box score.
mechv1 / verified internal analysis
These are active-project results, not a finished publication. The strongest signal is color selectivity; texture and architecture-aware tracing remain open work.
I built and validated the weight-only decoding experiments, depth profiles, permutation controls, cross-network comparisons, and V1-reference bridge. Dr. Wyeth Bair supplies the historical network assets, selectivity measurements, and scientific direction.
outputs + timeline
Analysis of Cross-Orientation Responses as a Function of Depth in Macaque Primary Visual Cortex
Mudit Agarwal et al.; first author.
Spatial Patterns Across Macaque Primary Visual Cortex
Gaku Hatanaka, Mudit Agarwal, et al.; second author.
MechV1 methods project
Weight-only selectivity decoding, cross-architecture validation, and V1 comparison.
UW Research Symposium talk
Cross-orientation suppression across cortical depth in macaque V1.
watch the talk ↗Society for Neuroscience poster
San Diego, California.
WANPRC talk + poster
Washington National Primate Research Center, Seattle.
XORI poster
The full-detail poster presented at the Society for Neuroscience and WANPRC meetings.
XORI source code
Suite2p integration, metric computation, mixed-effects models, mediation analysis, and scientific figures.