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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Research

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.


TRAKR - A reservoir-based tool for fast and accurate classification of time-series patterns
Furqan Afzal*, CD Márton*, Kanaka Rajan
bioRxiv, 2021 * Contributed equally.

Neuroscience has seen a dramatic increase in the types of recording modalities and complexity of neural time-series data collected from them. The brain is a highly recurrent system producing rich, complex dynamics that result in different behaviors. Correctly distinguishing such nonlinear neural time series in real-time, especially those with non-obvious links to behavior, could be useful for a wide variety of applications. These include detecting anomalous clinical events such as seizures in epilepsy, and identifying optimal control spaces for brain machine interfaces. It remains challenging to correctly distinguish nonlinear time-series patterns because of the high intrinsic dimensionality of such data. We introduce a reservoir-based tool, state tracker (TRAKR), which offers the high accuracy of ensembles or deep supervised methods while preserving the computational benefits of simple distance metrics.

Efficient and robust multi-task learning in the brain with modular task primitives.
CD Márton, Guillaume Lajoie, Kanaka Rajan
arXiv, 2021

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 actions shapes recurrent dynamics in the corticostriatal system
CD Márton, Simon R. Schultz, Bruno B. Averbeck
Neural Networks, 2020 / 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.

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

Blog Posts / Side projects
Predict prices of Gerhard Richter paintings
Colab, 2021

Tired of grappling with art so abstract it makes the most obstinate Sotheby's appraiser cringe? Worry no more.

How to be less anxious amidst a changing world
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”. 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
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.

Conferences
  • 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), Neuromatch Academy (2021)

    Le Maitre