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 Masters and PhD in Bioengineering (Computational Neuroscience/Neurotechnology) 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 Bachelors in Neuroscience from Princeton University where I also completed the pre-medical track. During that time I worked with Uri Hasson at the Princeton Neuroscience Institute, and also spent some time at the MPI for Brain Research in Frankfurt, Germany.

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, extended and retrieved in neural networks in the brain. I am also interested in modeling network dysfunction, and restoring healthy functioning by correcting network imbalances. 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 a real-world setting biological agents do not have infinite resources to learn new things. It is thus useful to recycle previously acquired knowledge in a way that allows for faster, less resource-intensive acquisition of multiple new skills. Neural networks in the brain are likely not entirely re-trained with new tasks, but how they leverage existing computations to learn new ones is not well understood. In this work, we study this question in artificial neural networks trained on commonly used neuroscience paradigms.

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.

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 developed a a unified, computationally advantageous Granger-causal framework and used it 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.

Tired of grappling with art so abstract it makes the most obstinate Sotheby's appraiser cringe? Worry no more. A little AI (transfer learning) can help.

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”. 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.

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