IBM Research

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).

IBM Research released Granite Timeseries Ensemble, a default forecasting recipe that combines four open Granite time-series models with different architectures and returns median and quantile forecasts without retraining or fitting ensemble weights. Previously a user had to judge which single model suited a given series. Across 97 GIFT-Eval configurations the probabilistic ensemble reaches a MASE of 0.683 against 0.685 for the strongest member, PatchTST-FM-r2, a small margin whose main benefit is not having to pick a model in advance.

IBM Research released Granite Timeseries Ensemble, a default forecasting recipe that combines four open Granite time-series models with different architectures and returns median and quantile forecasts without retraining or fitting ensemble weights. Previously a user had to judge which single model suited a given series. Across 97 GIFT-Eval configurations the probabilistic ensemble reaches a MASE of 0.683 against 0.685 for the strongest member, PatchTST-FM-r2, a small margin whose main benefit is not having to pick a model in advance.