Neural Complexity & States of Consciousness
Investigating how statistical and information-theoretic measures (SC, LZc, ApEn, etc.) capture dynamics of consciousness across sleep, wakefulness, and psychedelic states.
Complexity Science, Consciousness and Mathematical Theory of Learning.
I apply methods from complexity science and information theory to investigate the neural basis of consciousness. Beyond this, I study the mathematical theory of learning in neural networks and cognitive systems, and how these ideas can be implemented in neurorobotics. My research is inherently interdisciplinary, integrating neuroscience, mathematics, and artificial intelligence.
Research Highlights
Metaheuristic Method for Solving Systems of Equations
Developed a novel metaheuristic approach for solving nonlinear systems of equations, bridging ideas from neural computation and evolutionary search.
Synaptic Homeostasis in Robotics
Applied Continuous-Time Recurrent Neural Networks (CTRNNs) with homeostatic plasticity for adaptive robotic control, enabling stable navigation under changing environments.
Experience
Selected research and industry roles. See CV for full details.
Conferences & Talks
Awards & Scholarships
Extracurricular Activities
Skills
Academic Journey
MSc — University of Sussex — 2023–2024
B.Eng — Ahmadu Bello University — 2009–2014
Short Curriculum Vitae
Contact
I’m open to collaboration and talks. For the quickest response, email me; otherwise, connect on LinkedIn.