I study how neural function emerges from physical systems. At Emeron Labs , active inference and reinforcement learning drive my independent research—connecting phenomenological models with neurophysiological circuit simulations.
Emeron Labs · Emergence of inference
01 / ENGINEERED NEURAL TISSUE
PNAS · 2019
RING-SHAPED NEURAL TISSUE
From my earlier in vitro research.
Actual microscopy, PNAS 2019. Click to explore.
Emergence of inference.
My research centers on active inference and reinforcement learning : how biological neural architecture gives rise to inference, learning, and adaptive behavior—and what these mechanisms can teach us about training AI.
I combine phenomenological modeling with neurophysiological circuit simulations to develop and test hypotheses across levels of complexity. A longer-term goal is to identify principles that could improve reinforcement-learning training in commercial frontier models.
WORKING HYPOTHESES · OPEN TO DISCUSSION Discuss a hypothesis ↗I want to examine how circuit organization, recurrent dynamics, and interactions across timescales constrain learning and behavior. The central question is which architectural features provide useful mechanisms for active inference, and which effects can be captured by simpler phenomenological models.
My hypotheses distinguish expected information gain before a choice from learning realized afterward. Proposed comparisons examine an expected-free-energy selector alongside an RL-value-plus-information-gain hybrid, with explicit attention to exploration, task performance, and computational cost.
I am exploring how recurring sequences can become executable skills through discovery, training, effect modeling, and validation. A related question is whether offline consolidation and multi-timescale organization can support more reliable long-horizon behavior.
The aim is to translate candidate mechanisms into controlled computational experiments: compare them with matched RL baselines, isolate their contributions, and measure learning efficiency, transfer, robustness, and training cost. Relevance to commercial frontier models is a research objective; these are hypotheses to investigate.
The methods have changed. The underlying question remains: how does organized neural activity become meaningful function?
Building three-dimensional neural tissue and studying electrophysiology, development, and neuromuscular interactions grounded my questions in measurable biological systems.
Cerebellar simulations and neural data analysis extended that work into models of learning, forgetting, and extinction, using C++, CUDA, and Python.
Bringing biological architecture, Grossberg-inspired neuroscience, and active inference into a research program on neural function and the mechanisms that could improve reinforcement-learning training.
Earlier coauthored work: high-resolution microscopy, electrophysiology, and neuromuscular junctions that ground my interest in neural function.
Engineering three-dimensional neural tissue and examining how geometry, development, and stimulation relate to its organization and function.
Explore the microscopy ↗
Using electrophysiology and computational analysis to investigate neural firing, synchrony, plasticity, and the effects of developmental stimulation.
Explore the recordings ↗
Studying engineered neuromuscular systems to understand how cellular interactions and physical environments influence functional behavior.
Explore the junctions ↗At 21, studying entropy and protein folding led me to a question that has stayed with me: how do physical processes give rise to something as remarkable as a thought?
That curiosity took me from mechanical engineering and theoretical-physics interests at the University of Puerto Rico to a PhD in bioengineering at Illinois, where I worked with living neural systems. Computational neuroscience at UT Austin then gave me a way to investigate circuit dynamics and learning through simulation.
Today, I pursue these questions independently through Emeron Labs—emergence of inference. I am especially interested in discussing active-inference hypotheses, connecting phenomenological descriptions to neurophysiological mechanisms, and identifying experiments that can tell competing explanations apart.
I welcome thoughtful exchanges about active inference, biological neural architecture, emergent function, and better ways to train learning systems.