A wireless modular platform for neuro-behavioral recording and closed-loop manipulation in small animals
The Wireless, Interactive, Lightweight Datalogger (WILD) device is an ultra-lightweight multimodal neurologger for local neural data logging, behavioral sensing, onboard processing, and responsive stimulation in freely behaving animals.
Current Public Workflow
Stable public scope: 64-channel local-storage WILD workflow, WILD_console on Windows, validated release images, and post-processing through documented MATLAB and Python scripts.
Not the current public model: full-bandwidth BLE telemetry or arbitrary runtime AI-model upload.
Release Record
Use the latest GitHub release and record the exact release tag, release image filename, hardware revision, WILD_console version, SD card, and battery in every experiment note.
Run a first recording
Build hardware
PCB -> Mechanical -> Power
Analyze data
Data Format -> MATLAB -> Python -> Spike Sorting
Cite or reproduce a result
Publications -> release metadata -> reproducibility notes -> analysis script version
Embedded control at the headstage
The MCU, IMU, and board-level control electronics sit close to the animal, reducing external cabling while keeping timing-sensitive acquisition local.
Key Features
The WILD device is built around local neural logging, responsive stimulation, embedded processing, multimodal sensing, and long-duration experiments.
Local Neural Logging
High-bandwidth electrophysiology is recorded locally to microSD, while BLE supports setup, synchronization, status, and low-bandwidth preview.
Responsive Stimulation
Onboard biomarker detection and behavioral classification can trigger stimulation without requiring continuous wireless data streaming.
TinyML on Edge Devices
Curated models run on the MCU through validated release images with RAM, timing, and closed-loop latency reviewed before deployment.
Multimodal Sensing
Neural signals, IMU, auxiliary inputs, ultrasonic audio, camera, and digital events.
Long-term Recording
Local storage, low power operations, and robust export tools for naturalistic studies.
Device Specification Summary
Representative WILD device operating modes show how channel count, aggregate sample rate, sensing modules, and onboard processing affect power draw.
| Power (mW) | Channels | Aggregate samples/s | Mode | Weight (g) | Video | Audio | Motion sensor | Processing |
|---|---|---|---|---|---|---|---|---|
| 0.11 | 0 | 0 | Sleep | 1.48 | - | - | - | - |
| 24.42 | 8 | 4 x 10^4 | Ephys, IMU | 1.48 | - | - | Accelerometer, gyroscope, magnetometer | Spectral power, TinyML |
| 194.04 | 64 | 2.6 x 10^6 | Ephys, IMU | 1.48 | - | - | Accelerometer, gyroscope, magnetometer | Spectral power, TinyML |
| 208.67 | 64 | 2.92 x 10^6 | Ephys, IMU, audio | 1.48 | - | 160 kHz | Accelerometer, gyroscope, magnetometer | Spectral power, TinyML |
| 245.28 | 64 | 5.48 x 10^6 | Ephys, IMU, audio, video | 1.48 | 320 x 320 px, 16 Hz | 160 kHz | Accelerometer, gyroscope, magnetometer | Spectral power, TinyML |
Values summarize measured or configured device operating modes. Report the exact hardware revision, release image, battery, SD card, enabled modules, and whether aggregate samples/s is summed across recorded streams when comparing runtime across experiments.
System Overview
A typical session moves from the animal-mounted WILD device to local storage, synchronization, and offline analysis.
Research Highlights
Outdoor recordings
Local storage, low-power modes, and wireless control support recordings in large naturalistic environments where tethering is impractical.
Multi-animal experiments
Wired sync, BLE calibration, and clock correction workflows coordinate multiple WILD devices and external systems.
Ripple detection
DSP filters include ripple-band detection for closed-loop hippocampal experiments.
Theta phase stimulation
Hilbert and filter modes support phase-aware online control designs.
Naturalistic behavior
IMU, video, USV, and neural data support studies of social interaction, outdoor navigation, and other freely expressed behaviors.
Open hardware iteration
The repository contains WILD device hardware files, validated release assets, WILD_console installers, and analysis scripts.
Quick Start
Hardware setup
Connector, battery-polarity, microSD, probe-cabling, sensor-cabling, and release-image checks come before the first recording.
Data acquisition
WILD_console handles BLE connection, synchronization, recording configuration, closed-loop settings, and local logging startup.
Data analysis
Exported recordings are converted into Intan-style files, IMU and camera outputs, and spike-sorting inputs.
Recommended first success target: complete a short bench recording, export the folder, confirm `amplifier.dat`, `analogin.dat`, `time.dat`, `info.rhd`, and `CE_params.bin`, then open the result with the documented MATLAB or Python workflow.
Publications and Citation
Platform manuscript
The WILD platform manuscript is currently under review.
Repository and release versions
Report the hardware revision, release image, WILD_console version, and analysis scripts used for each dataset.
@article{zhao_wild_neurologger,
title = {A wireless modular platform for neuro-behavioral recording and closed-loop manipulation in small animals},
author = {Zhao, Zifang and Chang, Hongyu and Paudel, Praveen and Park, Jaehyo and Liu, Can and Aurelio, Maria Q. and Oliva, Azahara and Fernandez-Ruiz, Antonio},
year = {2026},
note = {Under review}
}
Community
Issues and Pull Requests
Use GitHub issues for reproducible bugs or documentation gaps, and pull requests for tested fixes, examples, and compatibility updates.