Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Unified, Scalable Framework for Neural Population Decoding
Authors: Mehdi Azabou, Vinam Arora, Venkataramana Ganesh, Ximeng Mao, Santosh Nachimuthu, Michael Mendelson, Blake Richards, Matthew Perich, Guillaume Lajoie, Eva Dyer
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In a number of different tasks, we demonstrate that our pretrained model can be rapidly adapted to new, unseen sessions with unspecified neuron correspondence, enabling few-shot performance with minimal labels. This work presents a powerful new approach for building deep learning tools to analyze neural data and stakes out a clear path to training at scale. We evaluate the performance of our proposed approach on data from over 158 sessions from open electrophysiology datasets from seven non-human primates (NHPs), spanning over 27,373 units and 100 hours of recordings. We demonstrate that through pretraining on large amounts of data, we can transfer with very few samples (few-shot learning) and thus improve overall brain decoding performance. |
| Researcher Affiliation | Academia | 1 Georgia Tech, 2 Mila, 3 Université de Montréal, 4 Mc Gill University |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Contact: EMAIL. Project page and code: https://poyo-brain.github.io |
| Open Datasets | Yes | We curated a multi-lab dataset with electrophysiological recordings from motor cortical regions, where neural population activity has been extensively studied [10], and deep learning tools and benchmarks have recently been established [27]. In total, we aggregated 178 sessions worth of data, spanning 29,453 units from the primary motor (M1), premotor (PMd), and primary somatosensory (S1) regions in the cortex of 9 nonhuman primates (see Table 1). We make these datasets publicly available on the DANDI open repository [29]. |
| Dataset Splits | Yes | For every session, we holdout 20% of the trials for testing, and 10% for validation. |
| Hardware Specification | Yes | The large models are trained on a machine with an AMD EPYC 7452 32-Core Processor and 8 Nvidia A40 GPUs (48Gb memory), POYO-mp was trained for 2 days, and POYO-1 was trained for 5 days (both for a total of 400 epochs). Single-session models are trained with a single Nvidia Ge Force RTX 3090 GPU, and take less than an hour to train. Finetuning models is also done with a single GPU. |
| Software Dependencies | No | The paper mentions using the LAMB optimizer [28] but does not specify version numbers for any software dependencies or libraries (e.g., PyTorch, TensorFlow, Python). |
| Experiment Setup | Yes | We train the model with N = 512 latent tokens and a dimension D = 128. We use the LAMB optimizer [28], and employ a cosine decay of the learning rate at the end of training. For every session, we holdout 20% of the trials for testing, and 10% for validation. We use 1-GPU and 8-GPU setups for single-session and multi-session models, respectively. All details can be found in Appendix A. The model is trained using the LAMB optimizer [28] with weight decay. The learning rate is held constant, then decayed towards the end of training (last 25% of epochs), using a cosine decay schedule. Single-session models are trained on a single GPU with a batch size of 128 while large models are trained with 8 GPUs with a total batch size of 1400. Table 4: Hyperparameter Value Embedding Dimension 128 Head Dimension 64 Number of Latents 128 Depth 6 Number of Heads 8 FFN Dropout 0.3 Linear Dropout 0.3 Attention Dropout 0.3 Weight Decay 1e-4 Learning Rate 4e-3 Batch Size 128 |