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.

Learning to select actions shapes recurrent dynamics in the corticostriatal system
Christian David Márton, Simon R. Schultz, Bruno B. Averbeck
Neural Networks, 2020
Neural Networks / bioRxiv

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.

Signature patterns for top-down and bottom-up information processing via cross-frequency coupling in macaque auditory cortex
Christian David Márton, Makoto Fukushima, Corrie R. Camalier, Simon R. Schultz, Bruno B. Averbeck
eNeuro, 2019
eNeuro / bioRxiv

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.

Blog Posts
How to be less anxious amidst a changing world
Christian David Márton,
Medium, 2020

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.

Principles of computation in neural networks, real and artificial
Christian David Márton,
Medium, 2018

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.

  • FENS Dynamics of the brain: temporal aspects of computation, Denmark 2019, "Learning actions and values shapes recurrent dynamics in the corticostriatal system."
    Christian David Márton, Simon R. Schultz, Bruno B. Averbeck

  • Society for Neuroscience (SFN), San Diego 2018, "Task representation & learning in prefrontal cortex & striatum as a dynamical system."
    Christian David Márton, Simon R. Schultz, Bruno B. Averbeck

  • Bernstein Computational Neuroscience Conference, Berlin 2018, "Learning in prefrontal cortex & striatum through shaping of recurrent dynamics" Travel Grant Award, Talk in workshop on "Emergent function in non-random neural networks"
    Christian David Márton, Simon R. Schultz, Bruno B. Averbeck

  • Society for Neuroscience (SFN), Washington D.C. 2017, "High accuracy categorization of macaque identities and call types with convolutional neural networks."
    Christian David Márton, Makoto Fukushima, Simon R. Schultz, Bruno B. Averbeck

  • Society for Neuroscience (SFN), San Diego 2016, "Top-down and bottom-up control through distinct phase-amplitude couplings in the macaque auditory cortex."
    Christian David Márton, Makoto Fukushima, Simon R. Schultz, Bruno B. Averbeck

  • Brain Informatics & Health Conference (BIH), London 2015, "Markov stability partitioning shows spectrally dependent community structure amongst thalamocortical neural ensembles."
    Christian David Márton, Silvia A. Jimenez, Simon R. Schultz,

  • Organization for Computational Neuroscience (OCNS) Conference, Prague 2015, "Revealing community structure amongst thalamocortical neural ensembles through markov stability partitioning."
    Christian David Márton, Silvia A. Jimenez, Simon R. Schultz,
  • Reviewer
    eNeuro, Plos Comp Bio, Nature Machine Intelligence, Cosyne (2018, 2020)

    Le Maitre