Active Sensing
active sensing
Michael Schmuker
We have two Research Fellow positions available, funded through the NSF/MRC NeuroNex grant “Odor to Action”, an international consortium of 16 world-leading research groups from the US, Canada, and the UK. The consortium will apply their joint expertise in computational and experimental neuroscience, behavioural studies, fluid dynamics, and fragrance chemistry to unlock how brains use natural stimuli to generate adaptive natural behaviours. Apply to join an interdisciplinary team of computational neuroscientists, AI researchers and computer scientists to investigate the neuro-computational principles of the sense of smell and translate them into approaches for machine and robot olfaction. Our team will investigate real-time machine olfaction using biologically realistic spiking networks, on neuromorphic hardware, embodied in robots, performing olfactory navigation in real time. We will also use machine learning to analyse experimental data provided by collaborators and to gain a deeper insight into fragrance space. For more information and to apply, please head to https://www.jobs.herts.ac.uk , search for job vacancy 029755. Informal inquiries via email are welcome!
Prof. Angela Yu
Prof. Angela Yu recently moved from UCSD to TU Darmstadt as the Alexander von Humboldt AI Professor, and has a number of PhD and postdoc positions available in her growing “Computational Modeling of Intelligent Systems” research group. Applications are solicited from highly motivated and qualified candidates, who are interested in interdisciplinary research at the intersection of natural and artificial intelligence. Prof. Yu’s group uses mathematically rigorous and algorithmically diverse tools to understand the nature of representation and computations that give rise to intelligent behavior. There is a fair amount of flexibility in the actual choice of project, as long as the project excites both the candidate and Prof. Yu. For example, Prof. Yu is currently interested in investigating scientific questions such as: How is socio-emotional intelligence similar or different from cognitive intelligence? Is there a fundamental tradeoff, given the prevalence of autism among scientists and engineers? How can AI be taught socio-emotional intelligence? How are artificial intelligence (e.g. as demonstrated by large language models) and natural intelligence (e.g. as measured by IQ tests) similar or different in their underlying representation or computations? What roles do intrinsic motivations such as curiosity and computational efficiency play in intelligent systems? How can insights about artificial intelligence improve the understanding and augmentation of human intelligence? Are capacity limitations with respect to attention and working memory a feature or a bug in the brain? How can AI system be enhanced by attention or WM? More broadly, Prof. Yu’s group employs and develops diverse machine learning and mathematical tools, e.g. Bayesian statistical modeling, control theory, reinforcement learning, artificial NN, and information theory, to explain various aspects of cognition important for intelligence: perception, attention, decision-making, learning, cognitive control, active sensing, economic behavior, and social interactions. Participants who have experience with two or more of the technical areas, and/or one or more of the application areas, are highly encouraged to apply. As part of the Centre for Cognitive Science at TU Darmstadt, the Hessian AI Center, as well as the Computer Science Department, Prof. Yu’s group members are encouraged and expected to collaborate extensively with preeminent researchers in cognitive science and AI, both nearby and internationally. All positions will be based at TU Darmstadt, Germany. Starting dates for the positions are flexible. Salaries are commensurate with experience and expertise, and highly competitive with respect to U.S. and European standards. The working language in the group and within the larger academic community is English. Fluency in German is not required; the university provides free German lessons for interested scientific staff.
Prof. Angela Yu
Multiple PhD and postdoctoral positions are immediately available in Prof. Angela Yu's research group at TU Darmstadt. The group investigates the intersection of natural and artificial intelligence using mathematically rigorous approaches to understand the representations and computations underlying intelligent behavior. The research particularly addresses challenges of inferential uncertainty and opportunities of volitional control. The group employs diverse methodological tools including Bayesian statistical modeling, control theory, reinforcement learning, and information theory to develop theoretical frameworks explaining key aspects of cognition: perception, attention, decision-making, learning, cognitive control, active sensing, economic behavior, and social interactions.
Active vision in Drosophila
NMC4 Short Talk: Brain-inspired spiking neural network controller for a neurorobotic whisker system
It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model to study active sensing and sensorimotor integration through feedback loops. In this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modelling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virtual mouse robot, exploiting the Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by brain-inspired controllers. Eventually, the peripheral whisker system was properly connected to an adaptive cerebellar network controller. The whole system was able to drive active whisking with learning capability, matching neural correlates of behaviour experimentally recorded in mice.
Clinical, Cognitive and Neuroscience Insights into Multisensory Processes
Time is of the essence: active sensing in natural vision reveals novel mechanisms of perception
n natural vision, active vision refers to the changes in visual input resulting from self-initiated eye movements. In this talk, I will present studies that show that the stimulus-related activity during active vision differs substantially from that occurring during classical flashed-stimuli paradigms. Our results uncover novel and efficient mechanisms that improve visual perception. In a general way, the nervous system appears to engage in sensory modulation mechanisms, precisely timed to self-initiated stimulus changes, thus coordinating neural activity across different cortical areas and serving as a general mechanism for the global coordination of visual perception.
A computational framework for decoding active sensing
COSYNE 2025
Deep reinforcement learning trains agents to track odor plumes with active sensing
COSYNE 2025
A fish-in-the-loop system to study the underlying mechanisms of active sensing
FENS Forum 2024