We are looking for a postdoctoral researcher to study the effects of neuromodulators in biologically realistic networks and learning tasks in the Vidi project 'Top-down neuromodulation and bottom-up network computation, a computational study'. You will use cellular and behavioural data gathered by our department over the previous five years on dopamine, acetylcholine and serotonin in mouse barrel cortex, to bridge the gap between single cell, network and behavioural effects. 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 biologically realistic learning frameworks, such as force learning, to study how network structure influences task performance. You will use existing open source data to train a SRNN on a pole detection task (for rodents using their whiskers) and incorporate realistic network properties of the (barrel) cortex based on our lab's measurements. Next, you will incorporate the cellular effects of dopamine, acetylcholine and serotonin that we have measured into the network, and investigate their effects on task performance. In particular, you will research the effects of biologically realistic network properties (balance between excitation and inhibition and the resulting chaotic activity, non-linear neuronal input-output relations, patterns in connectivity, Dale's law) and incorporate known neuron and network effects. 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. We are therefore looking for a team player who can collaborate well with the other group members, and is willing to both learn from them and share their knowledge.

We are looking for a postdoctoral researcher to work on the Vidi project 'Top-down neuromodulation and bottom-up network computation, a computational study' and study the effects of neuromodulators in balanced networks. You will use cellular and behavioural data on the effects of dopamine, acetylcholine and serotonin in mouse barrel cortex gathered by our department over the past five years, to bridge the gap between single cell, network and behavioural effects. You will use the balanced network framework to study network activity under neuromodulation. In order to do this, you will develop a balanced network description of the barrel cortex, with realistic barrel cortex properties (see https://doi.org/10.1007/s12021-022-09576-5). Next, you will incorporate the cellular effects of dopamine, acetylcholine and serotonin that we have measured over the previous years (see https://doi.org/10.1093/gigascience/giy147 and https://doi.org/10.1101/2022.01.12.476007) into the network, and investigate their effects on overall network activity and behaviour. More particularly, through simulations and analytical derivations, you will research the effects of neuromodulators on the stability of the balanced state, synchrony, regularity and chaos. You will build on the single cell data, models and analysis methods available in our group, and your results will be incorporated into our group's further research to develop and validate machine learning and efficient coding models of (somatosensory) perception. We are therefore looking for a team player who can work well with our other group members and is willing to both learn from them and share their knowledge.

Postdoctoral scientist positions are available at the Nathan Kline Institute (NKI) for Psychiatric Research to work on computational neuroscience research funded by recently awarded NIH and DoD grants. Our NIH-funded projects investigate the brain's dynamic circuit motifs underlying internal vs. external-oriented processes in the auditory and interconnected areas, using circuit modeling of the thalamocortical system. In this project, the postdoc will build data-driven biophysical models constrained by data collected from electrophysiology labs at NKI and Columbia & The Feinstein Institutes for Medical Research, and then use the models to predict optimal neuromodulation strategies for inducing/suppressing circuit patterns, testable in vivo. Our DoD project involves developing computational models of the hippocampal and entorhinal cortex circuitry used in spatial navigation, higher level decision making circuits, and integrating the models with agents learning to solve navigation tasks using neurobiologically-inspired learning rules. This project includes mathematicians and robotics researchers at UTK and CMU.

The Wiring, Neuromodeling and Brain Lab at IT:U Interdisciplinary Transformation University Austria is looking for two PhD students to work on neuromodulation-aware artificial intelligence. We are interested in (1) the role of individual neuromodulators (e.g., dopamine, serotonin, and acetylcholine) in initiating and implementing diverse biological and cognitive functions, (2) how competition and cooperation among neuromodulators enrich single neuromodulator computations, and (3) how multi-neuromodulator dynamics can be translated into learning rules for more flexible, robust, and adaptive learning in artificial neural networks (ANNs). Start date: Jan-Mar 2025. Apply by Nov 30, 2024: https://it-u.at/en/research/research-groups/computational-neuroscience/ For more information, please visit: https://majorjiemei.wixsite.com/wnblab If you have any questions, please contact Dr. Jie Mei (jie.mei@it-u.at).

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.