Fleur Zeldenrust

Fleur Zeldenrust
Radboud University | RU · Donders Institute for Brain, Cognition, and Behaviour

PhD in Computational Neuroscience

About

49
Publications
5,728
Reads
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236
Citations
Introduction
Fleur Zeldenrust currently works at the Donders Institute for Brain, Cognition, and Behaviour, Radboud University. Fleur does research in Computational Neuroscience. Her most recent publication is 'Spike and Burst Coding in Thalamocortical Relay Cells.'
Additional affiliations
July 2016 - present
Radboud University
Position
  • Professor (Assistant)
Description
  • I am a computational neuroscientist, trained in physics and neuroscience. I work on models of sensorimotor computation: interactions between sensory perception and motor control form the foundation of how we perceive the world and respond to it.
June 2015 - June 2015
Okinawa Institute of Science and Technology
Position
  • Tutor
Description
  • https://groups.oist.jp/ocnc/oist-computational-neuroscience-course-ocnc2015
October 2014 - July 2016
University of Amsterdam
Position
  • Lecturer
Description
  • Co-developed courses in Computational Neuroscience, coordinated a course in signal analysis, lectured in courses in Neuroscience (from perception to cognition; learning and memory), supervised MSc and BSc students.
Education
September 2004 - September 2006
University of Amsterdam
Field of study
  • Neurobiology / Physics
September 2001 - September 2004
University of Amsterdam
Field of study
  • Physics
September 2000 - September 2001
University of Amsterdam
Field of study
  • Physics / Multidisciplinary ('bèta-gamma')

Publications

Publications (49)
Article
Full-text available
With its six layers and ~ 12,000 neurons, a cortical column is a complex network whose function is plausibly greater than the sum of its constituents’. Functional characterization of its network components will require going beyond the brute-force modulation of the neural activity of a small group of neurons. Here we introduce an open-source, biolo...
Preprint
Optical (fluorescence) imaging of ionic dynamics has revolutionized neuroscience as it allows the study of neural activity across spatially identified populations. Quantification of fluorescence signals is commonly performed using ratiometric measures, like the ΔF/F. Although these measures are robust and easy to implement, they do not take advanta...
Preprint
Full-text available
Background: The recent release of two large intracellular electrophysiological databases now allows high-dimensional systematic analysis of mechanisms of information processing in the neocortex. Here, to complement these efforts, we introduce a freely and publicly available database that provides a comparative insight into the role of various neuro...
Article
Full-text available
Cortical networks show a large heterogeneity of neuronal properties. However, traditional coding models have focused on homogeneous populations of excitatory and inhibitory neurons. Here, we analytically derive a class of recurrent networks of spiking neurons that close to optimally track a continuously varying input online, based on two assumption...
Preprint
Full-text available
Transformation of postsynaptic potentials (PSPs) into action potentials (APs) is the rate-limiting step of communication in neural networks. The efficiency of this intracellular information transfer also powerfully shapes stimulus representations in sensory cortices. Using whole-cell recordings and information-theoretic measures, we show herein tha...
Preprint
Full-text available
Sensory neurons reconstruct the world from action potentials (spikes) impinging on them. Recent work argues that the formation of sensory representations are cell-type specific, as excitatory and inhibitory neurons use complementary information available in spike trains to represent sensory stimuli. Here, by measuring the mutual information between...
Preprint
Full-text available
With its six layers and ~12000 neurons, a cortical column is a complex network whose function is plausibly greater than the sum of its constituents’. Functional characterization of its network components will require going beyond the brute-force modulation of the neural activity of a small group of neurons. Here we introduce an open-source, biologi...
Preprint
Full-text available
Cortical networks show a large heterogeneity of neuronal properties. However, traditional coding models have focused on homogeneous populations of excitatory and inhibitory neurons. Here, we analytically derive a class of recurrent networks of spiking neurons that close to optimally track a continuously varying input online, based on two assumption...
Article
Full-text available
Background Neurons in the supragranular layers of the somatosensory cortex integrate sensory (bottom-up) and cognitive/perceptual (top-down) information as they orchestrate communication across cortical columns. It has been inferred, based on intracellular recordings from juvenile animals, that supragranular neurons are electrically mature by the f...
Preprint
Full-text available
Background: Neurons in the supragranular layers of the somatosensory cortex integrate sensory (bottom-up) and cognitive/perceptual (top-down) information as they orchestrate communication across cortical columns. It has been inferred, based on intracellular recordings from juvenile animals, that supragranular neurons are electrically mature by the...
Article
Full-text available
What any sensory neuron knows about the world is one of the cardinal questions in Neuroscience. Information from the sensory periphery travels across synaptically coupled neurons as each neuron encodes information by varying the rate and timing of its action potentials (spikes). Spatiotemporally correlated changes in this spiking regimen across neu...
Article
Full-text available
Neuronal action potentials or spikes provide a long-range, noise-resistant means of communication between neurons. As point processes single spikes contain little information in themselves, i.e., outside the context of spikes from other neurons. Moreover, they may fail to cross a synapse. A burst, which consists of a short, high frequency train of...
Article
Full-text available
Mammalian thalamocortical relay (TCR) neurons switch their firing activity between a tonic spiking and a bursting regime. In a combined experimental and computational study, we investigated the features in the input signal that single spikes and bursts in the output spike train represent and how this code is influenced by the membrane voltage state...
Data
ETAs of two different cells. ETAs for two cells (red and black) from S1 Fig, and for a Poisson event-train with the same number of events as the red trace. Even though the reliability between the cells of which we show the ETAs is quite low, the ETAs are still very comparable. (TIF)
Data
Eigenvalues of the covariance analysis (Fig 8). (TIF)
Data
Reliability of event times between cells. Since all analyses in this paper critically depend on spike timing, the differences in spike timing show directly the consistency of our results on a population level. In supplementary figure 1 we compare the reliability of spike timing between recordings within the same cell (blue traces), between recordin...
Data
Bias of information calculation. The same analysis as in Fig 5 repeated 50 times, but using Poisson event-trains with the same number of events as in Fig 5. Error-bars denote standard deviations. (TIF)
Article
Full-text available
Understanding the relation between (sensory) stimuli and the activity of neurons (i.e. `the neural code') lies at heart of understanding the computational properties of the brain. However, quantifying the information between a stimulus and a spike train has proven to be challenging. We propose a new (in vitro) method to measure how much information...
Article
Full-text available
Background Measures of spike train synchrony are widely used in both experimental and computational neuroscience. Time-scale independent and parameter-free measures, such as the ISI-distance, the SPIKE-distance and SPIKE-synchronization, are preferable to time scale parametric measures, since by adapting to the local firing rate they take into acco...
Article
Full-text available
http://www.scholarpedia.org/article/Spike_frequency_adaptation
Article
Full-text available
Neurons in the sensory systems extract information about the outside world from a constant stream of noisy sensory inputs. Previously, we have shown that the dynamics of spiking sensory neurons can be interpreted as a form of Bayesian inference in time [1], where spikes are only fired if they provide new information that cannot be predicted from pa...
Article
The reliability and precision of the timing of spikes in a spike train is an important aspect of neuronal coding. We investigated reliability in thalamocortical relay (TCR) cells in the acute slice and also in a Morris-Lecar model with several extensions. A frozen Gaussian noise current, superimposed on a DC current, was injected into the TCR cell...
Conference Paper
Full-text available
The Generalized Linear Model (GLM) is a powerful tool in assessing neural spike responses ([1]; for an overview, see [2]). The model assumes that the output of a neuron is an inhomogeneous Poisson process, of which the instantaneous rate is given by a thresholded sum of the linearly filtered input and output. It can incorporate effectively both the...
Article
Pyramidal cells perform computations on their inputs within the context of the local network. The present computational study investigates the consequences of feed-forward inhibition for the firing rate and reliability of a typical hippocampal pyramidal neuron that can respond with single spikes as well as bursts. A simple generic inhibitory intern...
Thesis
Full-text available
De hersenen verwerken voortdurend informatie uit hun omgeving. Fleur Zeldenrust onderzocht op het niveau van hersencellen (neuronen) hoe deze informatieverwerking plaatsvindt, ofwel wat de ‘neurale code’ is. Dit onderzocht ze met zowel experimentele waarnemingen als theoretische modellen. Zeldenrust richtte zich op twee hersengebieden: de hippocamp...
Conference Paper
Neurons in the sensory systems extract information about the outside world from a constant stream of noisy sensory input. Previously, we have shown that the dynamics of spiking sensory neurons can be interpreted as a form of Bayesian inference in time [1], where spikes are only fired if they provide new information that cannot be predicted from the...
Article
Full-text available
The thalamus modulates the information flow to and from the cortex. The basal ganglia provide the thalamus with inhibitory input, whereas the cortex sends excitatory connections to the thalamus. Thalamocortical relay (TCR) neurons can fire both single spikes and bursts: the latter ones after a low threshold T-type calcium current is activated from...
Conference Paper
Pyramidal cells perform computations on excitatory inputs in a local network that provides various forms of inhibition. A previous study [1] investigated two functionally separated inhibitory feedback loops: 1) one with slow synaptic kinetics (20 ms) projecting to the distal dendrite of the pyramidal cell (e. g. O-LM interneurons) 2) one with fast...
Conference Paper
The thalamus controls the information that flows to and from the cortex under inhibitory modulation of the basal ganglia. Thalamocortical relay neurons (TCRs) can fire single spikes as well as bursts: bursts are fired after activation of the low threshold T-type calcium current from a sufficiently deep level of hyperpolarization. Open question are:...
Article
Pyramidal cells in the hippocampus are part of a small neuronal network that performs computations on external input. The network consists of principal cells and various forms of feedback inhibition. Experimental evidence indicates at least two functionally distinct inhibitory feedback loops in the CA3 area of the hippocampus: (1) a loop in which O...
Article
Full-text available
Meeting abstracts - A single PDF containing all abstracts in this
Conference Paper
In an analytical study we investigated the effect of homeostatic scaling of excitability (HSE) in a neuron in which synaptic efficacy depends on the timing of pre- and postsynaptic spikes. This type of learning is known as spike timingdependent plasticity (STDP). Although the latter form of use-dependent learning is experimentally confirmed it is n...

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Projects

Projects (4)
Project
Understand how inhibition influences information transfer
Project
What information do single neurons transfer? How can this be modulated?
Project
Why do some neurons burst?