Spiking Neural Networks
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Friedemann Zenke
The position involves conducting research in computational neuroscience and bio-inspired machine intelligence, writing research articles and presenting them at international conferences, publishing in neuroscience journals and machine learning venues such as ICML, NeurIPS, ICLR, etc., and interacting and collaborating with experimental neuroscience groups or neuromorphic hardware developers nationally and internationally.
Ján Antolík
A postdoctoral position within the Computational Systems Neuroscience Group (CSNG) at Charles University, Prague, focusing on computational neuroscience and neuro-prosthetic system design. The group explores the intricacies of the visual system, sensory coding, and neuro-prosthetic solutions using computational approaches such as large-scale biologically detailed spiking network models, firing-rate models of development, and modern machine learning techniques. The team is dedicated to understanding visual perception and its restoration via neuro-prosthetic devices. Multiple project topics are available and can be adjusted to the interest and background of the applicant, including modeling electrical stimulation in a spiking model of the primary visual cortex, deep-neural networks in visual neuroscience, study of cortical dynamics in the visual cortex, and biologically detailed spiking large-scale models of early visual cortical pathway from Retina to V4.
Laurent Perrinet
This PhD subject focuses on the association between attention and spiking neural networks for defining new efficient AI models for embedded systems such as drones, robots and more generally autonomous systems. The thesis will take place between the LEAT research lab in Sophia-Antipolis and the INT institute in Marseille which both develop complementary approaches on bio-inspired AI from neuroscience to embedded systems design.
Vinita Samarasinghe
Doctoral Position in Computational Neuroscience. Are you curious about how the human brain stores memories? Have you wondered how we manage to navigate through space? Our dynamic research group uses diverse computational modeling approaches, including biological neural networks, cognitive modeling, and machine learning/artificial intelligence, to study learning and memory. Currently, we are actively seeking a talented graduate student to join our team, someone who will expand our computational modeling framework Cobel-Spike and use it to study how spiking neural networks can learn to navigate. This position is 65% at TV-L E13, starts as soon as possible, and is funded for 3 years.
Dr. Fleur Zeldenrust
For the Vidi project ‘Top-down neuromodulation and bottom-up network computation,’ we seek a postdoc to study neuromodulators in efficient spike-coding networks. Using our lab’s data on dopamine, acetylcholine, and serotonin from the mouse barrel cortex, you’ll derive models connecting single cells, networks, and behavior. The aim of this project is to explain the effects of neuromodulation on task performance in biologically realistic spiking recurrent neural networks (SRNNs). You will use the efficient spike coding framework, in which a network is not trained by a learning paradigm but deduced using mathematically rigorous rules that enforce efficient coding (i.e. maximally informative spikes). You will study how the network’s structural properties such as neural heterogeneity influence decoding performance and efficiency. You will incorporate realistic network properties of the (barrel) cortex based on our lab’s measurements and incorporate the cellular effects of dopamine, acetylcholine and serotonin we have measured over the past years into the network, to investigate their effects on representations, network activity measures such as dimensionality, and decoding performance. You will build on the single cell data, network models and analysis methods available in our group, and your results will be incorporated into our group’s further research to develop and validate efficient coding models of (somatosensory) perception. Therefore, we are looking for a team player who is willing to learn from the other group members and to share their knowledge with them.
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