# Population geometry

Neuralign places neural recordings from different sessions and animals, with different sampled neurons, into one tensor decomposition in which each component shares a single temporal curve while trial strength and neuron weights are estimated per session, so no matched neurons or matched trials are needed. On 93 brain-wide recordings from 19 mice, an unsupervised fit yielded a single component that separated the three anterior lateral motor cortex (ALM) optogenetic inhibition conditions, with balanced accuracy of 0.647 on held-out sessions against a chance level of 1/3. That component's change showed a modest association with the drop in behavioural accuracy (ρ = 0.266 over 180 session–inhibition observations).

Neuralign places neural recordings from different sessions and animals, with different sampled neurons, into one tensor decomposition in which each component shares a single temporal curve while trial strength and neuron weights are estimated per session, so no matched neurons or matched trials are needed. On 93 brain-wide recordings from 19 mice, an unsupervised fit yielded a single component that separated the three anterior lateral motor cortex (ALM) optogenetic inhibition conditions, with balanced accuracy of 0.647 on held-out sessions against a chance level of 1/3. That component's change showed a modest association with the drop in behavioural accuracy (ρ = 0.266 over 180 session–inhibition observations).

Differences between individuals in perceptual decision-making may be different mixtures of a few shared computational components. A low-rank neural network learned shared components and participant-specific weights jointly from trial-level choices; on the Bernoulli Clicks Task, out-of-sample prediction selected a six-dimensional representation of individual differences in the first dataset of 155 participants: two dimensions of temporal evidence weighting, two of choice-outcome history dependence, plus participant-specific side bias and signal-to-noise ratio. This low-dimensional organization reappeared in an independent replication dataset of 102 participants.

Differences between individuals in perceptual decision-making may be different mixtures of a few shared computational components. A low-rank neural network learned shared components and participant-specific weights jointly from trial-level choices; on the Bernoulli Clicks Task, out-of-sample prediction selected a six-dimensional representation of individual differences in the first dataset of 155 participants: two dimensions of temporal evidence weighting, two of choice-outcome history dependence, plus participant-specific side bias and signal-to-noise ratio. This low-dimensional organization reappeared in an independent replication dataset of 102 participants.