NEATLABs Behavioral and Neural Research Systems

The System Behind the Science

At NEATLABs, my work was not only "analysis after the experiment." I helped build and operate the behavioral and computational systems that made the experiments possible: custom rodent paradigms, Raspberry Pi-based behavioral boxes, MATLAB/Simulink control logic, event extraction, local field potential processing, and publication-supporting analyses.

The research questions were about decision-making, action suppression, impulsivity, reward value, traumatic brain injury, and cortico-striatal neural dynamics. The software work translated those questions into runnable experiments and analyzable datasets.

Behavioral Paradigms

Each core paradigm has its own dedicated brief with a study-design visualization, the specific rat behavior it elucidated, and the methodology of the paper it anchored:

• Go/No-Go & Go/Wait — Action, Inhibition, Impulsivity — acting vs withholding vs waiting; multisite LFP + human EEG map action and theta-inhibition networks (eNeuro 2021; rodent default-mode suppression 2021).
• Delay Discounting — Reward Value & Subjective Value — smaller-sooner vs larger-later choices; reward-locked cortico-striatal beta tracks magnitude, delay, and modeled subjective value (Cognitive, Affective, & Behavioral Neuroscience 2024).
• Probabilistic Reversal Learning — Reward Certainty — uncertain, reversing contingencies; beta and high-gamma track reward probability, and optogenetic beta stimulation causally perturbs behavior (Journal of Neuroscience 2025).

Across all three, the systems challenge I owned was the same: turn cues, waits, premature responses, omissions, choices, and rewards into reliable, millisecond-aligned computational events, because the neural analysis is only as trustworthy as the behavioral timestamps beneath it.

Experimental Conditions

The research scope included comparisons across animal and treatment conditions, including healthy controls and TBI-related experiments, along with pharmacological conditions such as saline, methylphenidate, and ketamine where relevant to the lab's study designs.

The key point for this deployed systems page is scope: the systems had to support structured comparisons across subjects, tasks, trial types, and experimental conditions, while keeping neural and behavioral timing aligned.

Behavioral Boxes and Raspberry Pi Control

I worked around the behavioral box infrastructure used for translational rodent testing. This included Raspberry Pi-based operant box systems, MATLAB/Simulink control workflows, and synchronization with electrophysiology-oriented experiments.

This is part of the same engineering lineage as the Journal of Neuroscience Methods paper on an open-source Raspberry Pi-based operant box for translational behavioral testing in rodents. The system-level work mattered because reliable neuroscience depends on reliable event generation, logging, timing, and recovery from ordinary experimental edge cases.

Neural Data Analysis Infrastructure

On the analysis side, I built and maintained workflows for large-scale LFP and behavior analysis:

• Event-aligned neural signal extraction
• ERP and ERSP-style analyses
• Time-frequency processing
• Cross-correlation and network analyses
• Granger-causality-style analysis exploration
• Spike sorting algorithm testing, including Kilosort and MountainSort contexts
• Large multi-terabyte LFP datasets
• MATLAB, Jupyter, EEGLAB-style, and parallel computing workflows

I also explored dynamic time warping and tensor component analysis for massive LFP datasets. That work was aimed at finding precise temporal neural patterns across trials, conditions, and animals.

Publication Anchors

These systems connect to several public NEATLABs papers and preprints:

• Mapping Large-Scale Networks Associated with Action, Behavioral Inhibition and Impulsivity
• Open-source Raspberry Pi-based operant box for translational behavioral testing in rodents
• Electrophysiological correlates of rodent default-mode network suppression
• Cortico-striatal beta oscillations as a reward-related signal
• Beta and high gamma oscillations in the cortico-striatal network reflect reward certainty

Why This Is Software Work

The software was not decorative. It was the bridge between a rat in a box, a neural signal on disk, an event timestamp, a pharmacological condition, and a scientific figure.

What this work shows:

• I can reason across hardware, behavior, signal processing, and biology
• I can build systems where experimental design and data structure stay connected
• I am comfortable with messy, high-dimensional research data
• I enjoy making tools that help scientists ask more precise questions

This is the root of my current interest in neuroscience infrastructure: systems that preserve enough context for data to remain scientifically meaningful.