most citedControversial stimuli: pitting neural networks against each other as models of human recognition

114 citations · 114 across the 9 of their papers we have counts for

collaborators

9 papers

q-bio.NC2026

Geometric Reliability of Neural Population Codes: Sampling Calibration and Within-Session Nonstationarity

Prashant C. Raju

Trial-to-trial variability limits how reliably neural population geometry can be estimated, while comparisons across populations depend on neuron and trial counts, response quality…

q-bio.NC2026

Geometric Phase Transition Enables Extreme Hippocampal Memory Capacity

Prashant C. Raju

Memory systems can store vastly different amounts of information despite similar hardware constraints. Here, we show that superior spatial memory emerges from a discrete stiffening…

cs.LG2026

The Geometric Canary: Predicting Steerability and Detecting Drift via Representational Stability

Prashant C. Raju

Reliable deployment of language models requires two capabilities that appear distinct but share a common geometric foundation: predicting whether a model will accept targeted behav…

q-bio.QM2026

From Syntax to Semantics: Geometric Stability as the Missing Axis of Perturbation Biology

Prashant C. Raju

The capacity to precisely edit genomes has outpaced our ability to predict the consequences. A cell can be genetically perfect and therapeutically useless: edited exactly as intend…

cs.LG2026

The Geometric Alignment Tax: Tokenization vs. Continuous Geometry in Scientific Foundation Models

Prashant C. Raju

Foundation models for biology and physics optimize predictive accuracy, but their internal representations systematically fail to preserve the continuous geometry of the systems th…

q-bio.QM2026

Directional coherence and effect magnitude in single-cell CRISPR perturbation responses

Prashant C. Raju

Single-cell CRISPR screens summarize each perturbation by how far cells move from their unperturbed state, averaging over cell-to-cell variation. Whether the cells moved together i…

cs.LG2026

Geometric Stability: The Missing Axis of Representations

Prashant C. Raju

Representational similarity methods compare the geometries of neural representations, but they do not measure how consistently the geometry of a single representation is recovered…

cs.CV2019★ 114 cited

Controversial stimuli: pitting neural networks against each other as models of human recognition

Tal Golan, Prashant C. Raju, Nikolaus Kriegeskorte

Distinct scientific theories can make similar predictions. To adjudicate between theories, we must design experiments for which the theories make distinct predictions. Here we cons…

q-bio.NC2019

A Theory on Formatting Sensory Input for Cognition

Prashant C. Raju

Over the last few decades, a lot of progress has been made in understanding different aspects of the brain's ability to form abstract representations, but a specific mechanism for…