SpikeProphecy decomposes spike-count forecasting performance into temporal fidelity, spatial pattern accuracy, and magnitude-invariant alignment, revealing reproducible brain-region predictability rankings and a sub-Poisson evaluation floor across seven model families on 105 Neuropixels sessions.
arXiv preprint arXiv:2108.01210 , year=
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UNVERDICTED 4representative citing papers
CalM uses a discrete tokenizer and dual-axis autoregressive transformer pretrained self-supervised on calcium traces to outperform specialized baselines on population dynamics forecasting and adapt to superior behavior decoding.
Mamba forecaster trained on next-step spikes decodes mouse choice at 75.7% and stimulus at 66.1%, beating linear decoding on raw spikes by 4-6 percentage points.
OmniMouse demonstrates data-driven scaling in multi-task brain models on a 150B-token neural dataset, achieving SOTA across prediction, decoding, and forecasting while model size gains saturate.
citing papers explorer
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SpikeProphecy: A Large-Scale Benchmark for Autoregressive Neural Population Forecasting
SpikeProphecy decomposes spike-count forecasting performance into temporal fidelity, spatial pattern accuracy, and magnitude-invariant alignment, revealing reproducible brain-region predictability rankings and a sub-Poisson evaluation floor across seven model families on 105 Neuropixels sessions.
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Self-Supervised Foundation Model for Calcium-imaging Population Dynamics
CalM uses a discrete tokenizer and dual-axis autoregressive transformer pretrained self-supervised on calcium traces to outperform specialized baselines on population dynamics forecasting and adapt to superior behavior decoding.
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Implicit Behavioral Decoding from Next-Step Spike Forecasts at Population Scale
Mamba forecaster trained on next-step spikes decodes mouse choice at 75.7% and stimulus at 66.1%, beating linear decoding on raw spikes by 4-6 percentage points.
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OmniMouse: Scaling properties of multi-modal, multi-task Brain Models on 150B Neural Tokens
OmniMouse demonstrates data-driven scaling in multi-task brain models on a 150B-token neural dataset, achieving SOTA across prediction, decoding, and forecasting while model size gains saturate.