Computational Neurophysiology
Computational Neurophysiology
From the beginning of my scientific trajectory, my work has been driven by a neurophysiological interest: understanding how channels, neurons, synapses, and circuits interact to generate coordinated activity. Starting with in vivo electrophysiology and analyses of neuronal coding, and progressively incorporating biophysical modeling, my scientific career has evolved to put mechanistic explanation at the core of my research.
As experimental techniques began to produce increasingly high-dimensional datasets, it became clear that qualitative inspection and descriptive characterization were no longer sufficient to infer the processes underlying complex activity. Engaging with this challenge led me to frame my work within computational neurophysiology, a research program that aims to answer physiological questions with formal modeling in order to understand how biological mechanisms give rise to circuit dynamics.
Computational neurophysiology is not defined by the use of computational tools alone, nor does it represent a departure from physiology. Rather, it reflects a commitment to mechanistic explanation in an era where formal approaches are necessary to address the scale and dimensionality of experimental data. My scientific interest remains rooted in neurophysiology: the questions that motivate my daily work concern the organization of circuits, the interaction of cellular and synaptic mechanisms, and the emergence of coordinated dynamics. Computational methods and biophysical models are indispensable because they allow these mechanisms to be formalized, constrained, and used to create precise predictions that inform future experiments.
Present systems neuroscience relies on characterizing activity patterns in multi-dimensional datasets to identify circuit components. Such description is essential. Yet explanatory insight requires more than cataloguing structure or observing correlations; it requires specifying how interacting mechanisms generate the observed dynamics. Computational neurophysiology provides a framework for this specification. Patterns in neural activity are analyzed to uncover reproducible structure, which is then explored through hierarchical models, from phenomenological approximations that provide computational insight to biophysically grounded implementations. Through this process, candidate mechanisms are evaluated, constrained, and refined.
At the center of this research program is the iterative interplay between data and mechanism formulation. Models are anchored in biological substrate and shaped by empirical observations; in turn, they generate predictions that can be tested experimentally. This continuous refinement, or epistemic iteration, moves beyond description toward principled understanding, revealing how circuit architecture, cellular properties, and synaptic interactions give rise to coordinated activity across scales.
More than a methodology, computational neurophysiology defines the conceptual framework I bring to research, shapes the questions I pursue, and serves as a foundation for a program that aims to bridge mechanism, dynamics, and principle across neural systems.
See my list of publications here
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