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Title: Neural Latents Benchmark ’21: Evaluating latent variable models of neural population activity
Advances in neural recording present increasing opportunities to study neural activity in unprecedented detail. Latent variable models (LVMs) are promising tools for analyzing this rich activity across diverse neural systems and behaviors, as LVMs do not depend on known relationships between the activity and external experimental variables. However, progress with LVMs for neuronal population activity is currently impeded by a lack of standardization, resulting in methods being developed and compared in an ad hoc manner. To coordinate these modeling efforts, we introduce a benchmark suite for latent variable modeling of neural population activity. We curate four datasets of neural spiking activity from cognitive, sensory, and motor areas to promote models that apply to the wide variety of activity seen across these areas. We identify unsupervised evaluation as a common framework for evaluating models across datasets, and apply several baselines that demonstrate benchmark diversity. We release this benchmark through EvalAI. http://neurallatents.github.io/
Authors:
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Award ID(s):
1835364
Publication Date:
NSF-PAR ID:
10317255
Journal Name:
Advances in Neural Information Processing Systems (NeurIPS), Track on Datasets and Benchmarks
Volume:
34
Sponsoring Org:
National Science Foundation
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