Investigate nervous systems
Research Scientist · Practitioner & Engineer · Educator
Understanding neural systems. Engineering artificial intelligence.
I investigate how neural systems generate perception, movement, and adaptive behavior and translate those principles into computational models, artificial intelligence, and scientific software.
About
From neural dynamics to intelligent systems.
My research seeks to uncover the computational principles that emerge from nervous systems and translate those principles into intelligent systems. By integrating experimental neuroscience, computational neuroscience, and NeuroAI, I investigate how neural systems process information, adapt, and produce complex behavior, not simply to understand those systems, but to discover the principles that can inspire new computational architectures.
My research follows a simple progression: observe, model, extract, and engineer. I investigate neural systems through experimental and computational neuroscience, developing models that explain how nervous systems compute across multiple levels of biological organization before extracting computational principles that extend beyond biology. Those principles are translated into NeuroAI, where neuroscience and artificial intelligence continuously inform one another. Across academic, government, and entrepreneurial settings, I apply this reciprocal exchange to develop computational models, algorithms, intelligent systems, and scientific software that bridge scientific discovery with engineering innovation.
Explain neural computation
Develop reciprocal scientific insight
Translate discovery into technology
Research focuses
One continuum, from biological mechanism to engineered intelligence.
Three complementary research objectives spanning biological discovery, computational modeling, and intelligent systems engineering.
01
Experimental Neuroscience
Understanding Neural Systems
Investigating the biological mechanisms that give rise to neural computation, adaptive behavior, and nervous system function. This work spans neural dynamics, motor control, sensory neuroscience, and the experimental investigation of biological neural systems.
02
Computational Neuroscience
Explaining Neural Computation
Developing computational models that explain how neural systems organize, compute, and adapt across multiple levels of biological organization—from individual neurons and neural circuits to connectomics, large-scale brain networks, and neural simulations.
03
NeuroAI & Artificial Intelligence
Engineering Intelligent Systems
Translating computational principles discovered in nervous systems into NeuroAI architectures while developing intelligent systems, artificial intelligence technologies, and software for scientific, clinical, and organizational applications. Through NeuroAI, neuroscience provides principles and methodologies for both engineering and investigating artificial neural systems, while AI and computational methods become powerful tools for advancing our understanding of biological neural systems.
Active Research & Development
Ongoing research translating scientific discovery into computational technologies.
Neural Dynamics & Motor Control
Investigating how biophysical mechanisms - including intracellular calcium dynamics, neuronal currents, and neuromodulation - generate, coordinate, and maintain neuronal and network dynamics to produce robust stereotypical motor pattern output. Research seeks to uncover the mechanistic principles that enable rhythmic neural computation through experimental and computational investigations of oscillatory neurons and central pattern-generating networks.
Connectomics & Computational Brain Modeling
Developing computational methods for identifying task-specific functional brain subnetworks, constructing macroscopic neural models, and simulating network activity under environmentally-induced physiological perturbations. Research spans connectomics, network analysis, neural modeling, and large-scale simulation to investigate brain organization, complex neural dynamics, and human performance.
Sensory-System-Inspired NeuroAI
Developing neurobiologically inspired artificial intelligence systems by translating computational principles from biological sensory systems into novel AI architectures. Research spans retina-inspired computer vision, cochlea-inspired signal processing, multimodal AI, and biologically inspired neural architectures to investigate how biological information processing can improve artificial intelligence.
Agentic AI & Intelligent Software Systems
Engineering intelligent software systems and large-language-model-backed applications that combine specialist agents and collaborative multi-agent systems to address complex organizational and scientific problems. Research and development span agent orchestration, domain-specific reasoning, workflow automation, organizational knowledge, and shared software infrastructure designed to automate operational processes and support specialized technical tasks.
Healthcare AI & Clinical Technologies
Developing intelligent computational technologies that leverage artificial intelligence to augment clinical practice, improve diagnostic decision making, and address limitations in clinical data. Research spans multimodal synthetic data generation, clinician-guided generative AI, automated diagnostic inference, and human–AI collaboration to develop clinically grounded technologies that improve healthcare delivery while providing new insight into physiological systems.
Proposed Research Agenda
Artificial Systems Neuroscience
Extending neuroscience beyond biology.
Artificial Systems Neuroscience is a proposed scientific discipline that extends neuroscience from the study of biological nervous systems to the systematic investigation of modern, large artificial neural networks (e.g., deep neural networks, transformers, and future neural architectures). Rather than treating neuroscience as a source of biologically inspired model architectures and learning algorithms, it reinterprets neuroscience as a general scientific methodology for understanding the development, organization, and computation of complex adaptive neural systems, regardless of substrate.
Present question
If a trained transformer were discovered in nature rather than engineered, how would neuroscience investigate it?
Answer
By treating it as a nervous system — mapping its organization, localizing function, perturbing its computation, and investigating its development, dynamics, and pathology through systematic experimentation.
Rather than treating neuroscience as a source of AI inspiration, Artificial Systems Neuroscience seeks to treat neuroscience as an AI investigatory tool, establishing an experimentally grounded, substrate-independent science of neural systems. One concerned with the organization, computation, dynamics, development, pathology, and intervention of artificial neural systems.
Selected publications
Representative contributions.
Selected contributions representing current work in healthcare AI, neural dynamics, connectomics, and computational neuroscience.
View complete record in CVFrom Cochlear Function to Diagnostic Decision: A Cascaded Neural Architecture for Automated Neonatal Hearing Assessment
Bauer, Jeng & Ellison · Awaiting submission
IKCa Trajectory Reveals Extremely Slow (Seconds-Long) Intracellular Calcium Removal Dynamics in a Lobster Pyloric Neuron
Ellison, Thuma & Hooper · Manuscript in progress
An Algorithm for Identifying Task-Specific Brain Subnetworks Using the Visuomotor System as an Example
Ellison, Matar, Gokoglu, Prabhu & Hooper · bioRxiv
DOI ↗Computational Neuroscientific Approaches to Investigate Dynamic Properties of Macro and Micro Motor Systems
Doctoral Dissertation · Ohio University
OhioLINK ↗Macroscopic Model of Hypogravity-Induced Primate Brain Activity via Identification and Analysis of a Neurovisuomotor Performance Pathway
Ellison, Matar, Gokoglu & Prabhu · Research Square
DOI ↗Computational technologies
Research expressed as models, algorithms, software, and systems.
View complete technology record in CV
Neural model
Awaiting submission
Copyright pending
Auditory Diagnostic Inference
A physiologically organized deep neural architecture that models sequential auditory processing to infer neonatal hearing diagnoses from cochlear function through automated diagnostic decision-making.
Network algorithm
Ongoing Research
Double Anchor Network Selection (DANS)
A connectomics algorithm that identifies task-specific brain subnetworks by reconstructing directed information flow between predefined input and output brain regions.
Scientific software
Released (2024)
expoFitter
Scientific software for nonlinear model fitting, model parameter estimation, and visualization of neuroelectrophysiological current data.
Agentic AI
Active development
Osmo
An executive orchestration agent for AI specialist delegation, coordination, and result synthesis within a hierarchical multi-agent system.
NeuroAI system
IP in deliberation
Artificial Auditory System
A neurocognitive AI system and method for mixed-sound source separation and speaker diarization.
Generative framework
Awaiting submission
Copyright pending
Clinician Guided Gen. Framework (CGGF)
A clinically grounded generative AI framework that integrates advanced prompt engineering with physiological and clinician-based validation to generate synthetic clinical data.
Teaching & pedagogy
Education from tissue to silicon.
I develop and deliver university instruction, technical training, workshops, bootcamps, and seminars spanning programming, data science, machine learning, artificial intelligence, organizational AI, and neuroscience. Educational experience includes undergraduate and graduate instruction together with advanced technical education for governmental, defense, healthcare, corporate, and international organizations.
View complete teaching record in CVSelected courses
BIOS 3455 — Human Physiology Laboratory
InstitutionOhio University
LevelUndergraduate
BIOS 4135/5135 — Human Neuroscience Laboratory (Brain Dissection Lab)
InstitutionOhio University
LevelUndergraduate / Graduate
BIOS 6820 — From Biophysical Neurons to Artificial Neural Networks
InstitutionOhio University
LevelGraduate Seminar
RDS GenAI 105 — Neural Networks and Deep Learning
Education ProviderData Society
Client OrganizationIQVIA
AI 210 — Transformers and Large Language Models
Education ProviderData Society
Client OrganizationIQVIA
GAIT 201 — Designing and Implementing AI Agents
Education ProviderData Society
Client OrganizationIQVIA
Selected testimonials
Participant feedback
“Your expertise and dedication made it an incredible learning experience. Your passion for the topic was evident and inspiring.”
“You've been an amazing presenter. I learned far more than I was expecting from this class.”
“GenAI seemed like a huge can of worms, but this really helped encourage me to try out some ways to incorporate this into my work.”
Representative organizations supported...
AI Facilitation & Research
data.ai is an AI facilitation and research company helping organizations develop AI/ML capabilities by leveraging internal data. Through AI education, collaborative research, and applied technology development, data.ai helps organizations discover, develop, deploy, and integrate intelligent solutions.
Visit data.aiContact
Let's investigate something difficult.
I welcome conversations related to any aspect of my work.