mojo-ssl-neural-decoding

name	mojo-ssl-neural-decoding
description	MOJO (Masked autOencoder-based JOint training) framework for decoding neural population activity using self-supervised learning with unlabelled data. Use when: working with limited labelled neural data, needing cross-session generalization, or wanting to leverage unlabelled spiking data for brain-computer interfaces.

MOJO: Masked autOencoder-based JOint Training

Overview

MOJO is a training framework for spike-tokenizing neural models that jointly leverages self-supervised learning (SSL) via masked autoencoding and supervised learning (SL) objectives. This approach addresses the limitation of current spike-based models that are restricted to supervised learning, which limits training to datasets with paired behavioral labels.

Key Benefits

• Superior performance over purely SL-trained models, especially with limited labelled data
• Few-shot finetuning capability where only small amounts of labelled data from new sessions are available
• More interpretable neuronal representations improving brain region classification and spike-statistics prediction
• Cross-modality generalization beyond spiking data to human electrocorticography during speech
• Comparable performance to neuro-foundation models (NFMs) designed specifically for continuous signals

Implementation Steps

1. Data Preparation: Tokenize neural data at the spike level to facilitate multi-session pretraining
2. Model Architecture: Implement a transformer-based architecture capable of handling spike-tokenized sequences
3. Joint Training Objective: Combine SSL via masked autoencoding with SL objectives:
   • SSL: Randomly mask spike tokens and train model to reconstruct them
   • SL: Train on available labelled behavioral data for decoding tasks
4. Training Protocol:
   • Pretrain on large unlabelled datasets using SSL objective
   • Finetune on limited labelled data using combined SSL+SL objectives
5. Evaluation: Test on multiple neural datasets (monkey motor cortex, mouse multi-regional recordings, human ECoG)

Use Cases

• Brain-computer interfaces with limited calibration data
• Cross-session neural decoding without extensive retraining
• Multi-species neural data analysis leveraging unlabelled recordings
• Neuro-foundation model pretraining for flexible downstream applications

Evaluation Datasets

• Monkey motor cortex during reaching tasks
• Multi-regional mouse recordings during vision and decision making tasks
• Human electrocorticography during speech production

Activation Keywords

neural decoding, spike-tokenizing, self-supervised learning, few-shot finetuning, brain-computer interface, neuro-foundation models, unlabelled neural data

References

• arXiv:2607.14086 - "Leveraging unlabelled data for generalizable neural population decoding"
• Subjects: Machine Learning (cs.LG), Neurons and Cognition (q-bio.NC)