Christian David Márton

I am a research scientist at Mount Sinai Friedman Brain Institute, where I work at the intersection of computational neuroscience and machine learning.

Previously, I completed my PhD in Bioengineering (Computational Neuroscience) across Imperial College London and NIMH/NIH , where I was advised by Simon Schultz & Bruno B. Averbeck and funded by the Wellcome Trust. I received my Masters in Bioengineering from Imperial College London , and my Bachelors in Neuroscience from Princeton University where I also completed the pre-medical track.

I also enjoy thinking about deep tech ventures in biology and healthcare. During my PhD, I have also spent time working with (bio)tech startups (e.g. System1Bio, Startupbootcamp London), in venture capital (Atomico), and with a biotech incubator out of Oxford University (Panacea Innovation).

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I am passionate about computational neuroscience and machine learning, and computational biology more broadly. I am interested in how information is stored, retrieved, updated, and extended in neural networks in the brain. In my work I use computational modeling together with tools from across machine learning, information engineering, signal processing and statistics. I enjoy working across disciplines.

“In the final analysis, a drawing simply is no longer a drawing, no matter how self-sufficient its execution may be. It is a symbol, and the more profoundly the imaginary lines of projection meet higher dimensions, the better.” (Paul Klee)

Learning to select appropriate actions based on their values is fundamental to adaptive behavior. This form of learning is supported by fronto-striatal systems. The computational mechanisms that shape the neurophysiological responses, however, are not clear. To examine this, we developed a recurrent neural network (RNN) model of the dlPFC-dSTR circuit and trained it on an oculomotor sequence learning task. After training, this system was able to autonomously represent and update action values and select actions, thus being able to closely approximate the representational structure in corticostriatal recordings. Altogether, this study advances our understanding of how neural circuit dynamics are involved in neural computation, revealing how dynamics in the corticostriatal system support task learning.

The brain consists of highly interconnected cortical areas, yet the patterns in directional cortical communication are not fully understood, in particular with regards to interactions between different signal components across frequencies. We employed a a unified, computationally advantageous Granger-causal framework to examine bi-directional cross-frequency interactions across four sectors of the auditory cortical hierarchy in macaques. Our findings extend the view of cross-frequency interactions in auditory cortex, suggesting they also play a prominent role in top-down processing. Our findings also suggest information need not be propagated along separate channels up and down the cortical hierarchy, with important implications for theories of information processing in the brain such as predictive coding.

The world keeps turning, the clock never stops, and I just want to do the most optimal thing. So the faster I figure out myself, the sooner I can get started to do what matters. We often hear sentences like “Be the best you can be”, “Know thyself”, “Travelling makes you grow”, “Stay on your path”, or “Be more conscious of yourself”. They are supposed to help us figure out what matters, but oftentimes they only make us more anxious: like what am I to do with myself amidst a world that never stays the same? As soon as I figured out one thing, it seems like the chessboard has shifted and I have to start anew. This article will try to attack platitudes head-on and provide some soothing answers, like a pill popped quickly, but less addictive and hopefully more everlasting.

Can we discern fundamental computational principles by which neural networks operate in the brain? By connecting individual brushstrokes into meaningful wholes, this article will strive to generate insight into how things might fit together, across disciplinary boundaries.

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