Christian David Márton
I am VP of Technology (AI) at Vektor Medical where I work on the 🫀.
Also visiting research scientist with the Rajan lab at Harvard Medical School,
where I work at the intersection of computational neuroscience 🧠 and machine learning 🤖.
Previously, I was a research scientist at the Icahn School of Medicine at Mount Sinai,
and the National Institute of Mental Health.
I completed my Masters and PhD in Bioengineering
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 Computational 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
(Herophilus, Startupbootcamp London),
and in venture capital (Atomico, Panacea Innovation).
Email  / 
Linkedin / 
Google Scholar  / 
Git  / 
Twitter / 
Goodreads / 
Substack
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Research (Select Publications)
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.
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Integration of Cloud-enabled AI Analyses of Ventricular Tachycardia Isthmuses with Electroanatomic Mapping Systems.
CT Villongco,
CD Márton
C Schulte,
DE Krummen,
G Ho
HRX, 2024 (Top Five Abstracts)
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Validation of a Deep Learning Ventricular Tachycardia Substrate Model.
CD Márton
CT Villongco,
G Ho,
DE Krummen
HRX, 2024 (Top Five Abstracts)
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Non-invasive co-localization of ventricular tachycardia isthmuses and scar using integrated Artificial Intelligence-based CT and ECG analysis.
G Ho,
CT Villongco,
FT Han,
JC Hsu,
K Hoffmayer,
F Raissi,
GK Feld,
DE Krummen,
CD Márton
HRS, 2024
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Representations of information value in mouse orbitofrontal cortex during information seeking.
JJ Bussell,
RP Badman,
CD Márton,
ES Bromberg-Martin,
LF Abbott,
K Rajan,
R Axel
bioRxiv, 2023
Animals are motivated to acquire knowledge of their world. They seek information that does not influence reward
outcomes suggesting that information has intrinsic value. We have asked whether mice value information and whether
a representation of information value can be detected in mouse orbitofrontal cortex (OFC).
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Artificial intelligence-based wall thickness analysis to predict arrhythmogenic myocardial substrate.
CD Márton,
C Villongco,
DE Krummen,
G Ho,
HRX, 2023
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A multi-modal workflow for integrating Al-based CT scar imaging and computational ECG
mapping targets for ventricular tachycardia ablation.
C Villongco,
CD Márton,
T Moyeda,
DE Krummen,
G Ho,
HRX, 2023
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Linking task-structure and neural network dynamics.
CD Márton,
S Zhou,
K Rajan
Nature Neuroscience, 2022
The solutions found by neural networks to solve a task are often inscrutable.
We have little insight into why a particular structure emerges in a network.
By reverse engineering neural networks from dynamical principles, Dubreuil, Valente et al.
show how neural population structure enables computational flexibility.
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Reservoir-based tracking (TRAKR) for one-shot classification of neural time-series patterns.
F Afzal*,
CD Márton*,
K Rajan
bioRxiv, 2021 * Contributed equally.
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 provides the
high accuracy of ensembles or deep supervised methods while preserving the benefits of
simple distance metrics in being applicable to single examples of training data (one-shot classification).
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Efficient and robust multi-task learning with modular latent primitives.
CD Márton,
L Gagnon,
G Lajoie,
K Rajan
arXiv, 2021
Combining brain-inspired inductive biases we call functional and structural, we propose a system that learns new tasks by building on top of pre-trained
latent dynamics organised into separate recurrent modules. The resulting model, we call a Modular Latent Primitives
(MoLaP) network, allows for learning multiple tasks effectively while keeping parameter counts, and updates, low.
We also show that the skills acquired with our approach are more robust to a broad range of perturbations
compared to those acquired with other multi-task learning strategies, and that generalisation to new tasks is facilitated.
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Learning to select actions shapes recurrent dynamics in the corticostriatal system
CD Márton,
SR Schultz,
BB 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.
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Signature patterns for top-down and bottom-up information processing via
cross-frequency coupling in macaque auditory cortex
CD Márton,
M Fukushima,
CR Camalier,
SR Schultz,
BB 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.
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Blog Posts / Side projects
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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.
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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.
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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.
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Shout Out
For mind-bending language games,
my dad's imagescapes (newest & latest, in the universal language of imagery: Nachhalltige Gedichte,
MANUSKRIPT: 1),
book of tales (in German: Die Traumfrau: 16 Immagische Erzählungen / J'aime),
poems (in German, among others: Besos oder J'aime: 101 Gedichte,
AdOro - J'aime: Gedichte)
and youthful reminiscences (in Hungarian: Pitch utazásai I,
Zetelaki Halastó: Pitch utazásai II,
Tördénelem: Pitch utazásai III)
To get a flavor, see these two poem recitals: Der Boden rast &
Lass ihn träumen
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