| name | mamba-spike-forecasting-behavioral-decoding |
| description | Implicit Behavioral Decoding from Next-Step Spike Forecasts at Population Scale — Mamba forecaster methodology for closed-loop BCI. A single Mamba model trained on next-step spike counts at Neuropixels scale simultaneously predicts future neural activity and decodes behavioral state via a lightweight linear readout. Use when: analyzing neural population spike data, building closed-loop BCI decoders, applying state-space models (Mamba/SSM) to neural time series, or studying implicit behavioral decoding from spike forecasts. Keywords: Mamba, spike forecasting, behavioral decoding, Neuropixels, closed-loop BCI, state-space model, neural population dynamics |
Mamba Spike Forecaster for Implicit Behavioral Decoding
Methodology from arXiv:2605.12999 — "Implicit Behavioral Decoding from Next-Step Spike Forecasts at Population Scale" (May 2026, submitted to NeurIPS 2026 Neuroscience & Cognitive Science Track).
Core Idea
A single Mamba (state-space model) forecaster, trained only on next-step spike counts at Neuropixels scale, delivers both a neural activity forecast AND an implicit behavioral readout in one forward pass. A lightweight per-session linear head reading the predicted firing rates decodes behavior better than the same linear head reading raw spike counts under matched temporal context.
Key Findings
- Dual function: Mamba predicts next-step spike counts AND produces latent representations that encode behavioral state
- Benchmark: Steinmetz visual-discrimination benchmark — 39 sessions, ~27,000 neurons, 1,994 held-out trials
- Mouse choice decoding: 75.7±0.2% trial vote (~2.3x chance)
- Stimulus side decoding: 66.1±0.6% trial vote (~2x chance)
- vs. Linear baseline: Mamba beats 500ms-context linear decoder on raw spike counts by 4-6pp on both metrics
- Fast calibration: ~100-150 trials brings readout within 1-2pp of asymptote
- Real-time feasible: Full pipeline fits within 50ms bin budget on workstation GPUs
Methodology
Model Architecture
- Mamba backbone: SSM-based sequence model processes binned spike counts across all recorded neurons
- Forecasting objective: Train on next-step spike count prediction (self-supervised)
- Linear readout: Lightweight per-session linear head on Mamba's hidden states for behavior decoding
Training Pipeline
- Bin spike trains to 50ms non-overlapping windows
- Train Mamba with teacher-forcing on next-step prediction
- After training, attach linear readout head
- Calibrate with 100-150 trials per new session
Key Technical Details
- Input: Population spike counts (Neuropixels scale, ~27k neurons across sessions)
- Output: Next-bin spike rate forecasts + behavioral state (choice, stimulus side)
- Architecture: Mamba state-space model (not Transformer)
- Training signal: Self-supervised (next-step prediction only, no behavior labels needed for the backbone)
- Calibration: Session-start calibration block with few trials
- Hardware: Workstation GPUs typical of tethered chronic Neuropixels recordings
Why It Works
Mamba's sequential processing compresses the high-dimensional neural population activity into a lower-dimensional latent state that implicitly encodes task-relevant behavioral variables. The forecasting objective forces the model to learn the generative dynamics of the neural population, which naturally captures behaviorally relevant structure — even without explicit behavioral supervision.
Activation
- spike forecasting
- behavioral decoding
- Mamba neural
- Neuropixels decoder
- closed-loop BCI
- implicit decoding
- neural population forecasting
- SSM neural time series
