Research
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)
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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.
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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.
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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.
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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.
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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,
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eNeuro, Plos Comp Bio, Nature Machine Intelligence, Cosyne (2018, 2020)
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