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Christian K. Machens

5 accepted papers

2024

Learning interpretable control inputs and dynamics underlying animal locomotion

Thomas Soares Mullen, Marine Schimel, Guillaume Hennequin, Christian K. Machens, Michael Orger, Adrien Jouary
ICLR 2024poster

A central objective in neuroscience is to understand how the brain orchestrates movement. Recent advances in automated tracking technologies have made it possible to document behavior with unprecedented temporal resolution and scale, generating rich datasets which can be exploited to gain insights i…

Cited by 1Source ↗PDF ↗Scholar ↗
2023

Uncovering motifs of concurrent signaling across multiple neuronal populations

Evren Gokcen, Anna Ivic Jasper, Alison Xu, Adam Kohn, Christian K. Machens, Byron M. Yu
NeurIPS 2023spotlight

Modern recording techniques now allow us to record from distinct neuronal populations in different brain networks. However, especially as we consider multiple (more than two) populations, new conceptual and statistical frameworks are needed to characterize the multi-dimensional, concurrent flow of s…

Cited by 11Source ↗PDF ↗code ↗Scholar ↗
2020

Biological credit assignment through dynamic inversion of feedforward networks

Bill Podlaski, Christian K. Machens
NeurIPS 2020poster

Learning depends on changes in synaptic connections deep inside the brain. In multilayer networks, these changes are triggered by error signals fed back from the output, generally through a stepwise inversion of the feedforward processing steps. The gold standard for this process --- backpropagation…

Cited by 24Source ↗PDF ↗Scholar ↗
2020

Compact task representations as a normative model for higher-order brain activity

Severin Berger, Christian K. Machens
NeurIPS 2020poster

Higher-order brain areas such as the frontal cortices are considered essential for the flexible solution of tasks. However, the precise computational role of these areas is still debated. Indeed, even for the simplest of tasks, we cannot really explain how the measured brain activity, which evolves…

Cited by 2Source ↗PDF ↗Scholar ↗
2020

Understanding spiking networks through convex optimization

Allan Mancoo, Sander Keemink, Christian K. Machens
NeurIPS 2020poster

Neurons mainly communicate through spikes, and much effort has been spent to understand how the dynamics of spiking neural networks (SNNs) relates to their connectivity. Meanwhile, most major advances in machine learning have been made with simpler, rate-based networks, with SNNs only recently showi…

Cited by 21Source ↗PDF ↗code ↗Scholar ↗