Matthijs Pals
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View article: Jaxley: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics
Jaxley: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics Open
Biophysical neuron models provide insights into cellular mechanisms underlying neural computations. A central challenge has been to identify parameters of detailed biophysical models such that they match physiological measurements or perfo…
View article: sbi reloaded: a toolkit for simulation-based inference workflows
sbi reloaded: a toolkit for simulation-based inference workflows Open
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View article: Phase of firing does not reflect temporal order in sequence memory of humans and recurrent neural networks
Phase of firing does not reflect temporal order in sequence memory of humans and recurrent neural networks Open
The temporal order of a sequence of events has been thought to be reflected in the ordered firing of neurons at different phases of theta oscillations. Here we assess this by measuring single neuron activity (1,420 neurons) and local field…
View article: sbi reloaded: a toolkit for simulation-based inference workflows
sbi reloaded: a toolkit for simulation-based inference workflows Open
Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a significant challenge. Simulation-based inference (SBI) add…
View article: Jaxley: Differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics
Jaxley: Differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics Open
Biophysiscal neuron models provide insights into cellular mechanisms underlying neural computations. However, a central challenge has been the question of how to identify the parameters of detailed biophysical models such that they match p…
View article: Inferring stochastic low-rank recurrent neural networks from neural data
Inferring stochastic low-rank recurrent neural networks from neural data Open
A central aim in computational neuroscience is to relate the activity of large populations of neurons to an underlying dynamical system. Models of these neural dynamics should ideally be both interpretable and fit the observed data well. L…
View article: A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Science
A Practical Guide to Sample-based Statistical Distances for Evaluating Generative Models in Science Open
Generative models are invaluable in many fields of science because of their ability to capture high-dimensional and complicated distributions, such as photo-realistic images, protein structures, and connectomes. How do we evaluate the samp…
View article: Trained recurrent neural networks develop phase-locked limit cycles in a working memory task
Trained recurrent neural networks develop phase-locked limit cycles in a working memory task Open
Neural oscillations are ubiquitously observed in many brain areas. One proposed functional role of these oscillations is that they serve as an internal clock, or ‘frame of reference’. Information can be encoded by the timing of neural acti…
View article: Trained recurrent neural networks develop phase-locked limit cycles in a working memory task
Trained recurrent neural networks develop phase-locked limit cycles in a working memory task Open
Neural oscillations are ubiquitously observed in many brain areas. One proposed functional role of these oscillations is that they serve as an internal clock, or ‘frame of reference’. Information can be encoded by the timing of neural acti…
View article: Phase of firing does not reflect temporal order in sequence memory of humans and recurrent neural networks
Phase of firing does not reflect temporal order in sequence memory of humans and recurrent neural networks Open
A prominent theory proposes that the temporal order of a sequence of items held in memory is reflected in ordered firing of neurons at different phases of theta oscillations 1 . We probe this theory by directly measuring single neuron acti…
View article: Demonstrating the Viability of Mapping Deep Learning Based EEG Decoders to Spiking Networks on Low-powered Neuromorphic Chips
Demonstrating the Viability of Mapping Deep Learning Based EEG Decoders to Spiking Networks on Low-powered Neuromorphic Chips Open
Accurate and low-power decoding of brain signals such as electroencephalography (EEG) is key to constructing brain-computer interface (BCI) based wearable devices. While deep learning approaches have progressed substantially in terms of de…
View article: A functional spiking-neuron model of activity-silent working memory in humans based on calcium-mediated short-term synaptic plasticity
A functional spiking-neuron model of activity-silent working memory in humans based on calcium-mediated short-term synaptic plasticity Open
In this paper, we present a functional spiking-neuron model of human working memory (WM). This model combines neural firing for encoding of information with activity-silent maintenance. While it used to be widely assumed that information i…
View article: A functional spiking-neuron model of activity-silent working memory in humans based on calcium-mediated short-term synaptic plasticity
A functional spiking-neuron model of activity-silent working memory in humans based on calcium-mediated short-term synaptic plasticity Open
In this paper, we present a functional spiking-neuron model of human working memory (WM). This model combines neural firing for encoding of information with activity-silent maintenance. While it used to be widely assumed that information i…