Neuro-AI-talks 2023 – (NEAT)

Welcome to NEAT – Neuro-AI-Talks in Osnabrück – an event for EU-based research groups working at the intersection of neuroscience and artificial intelligence. The focus of NEAT is to foster connections and discussions among attendees, sharing new ideas, projects, and directions, as well as exploring potential collaborations. The event is aiming for a rather small group of attendees (approximately 70-80 people, invite only) to encourage an open and relaxed exchange. Each research group is welcome to join with 3-4 members and their PI. We look forward to welcoming you to Osnabrück in September.

Questions? Feel free to contact Katja Ruge at katja.ruge(at)uni-osnabrueck.de.

Registration deadline: 31 March 2023
Hotel reservation deadline: 31 July 2023
Welcome reception: 24 September 2023
Main event: 25 September 2023

NEAT will take place in the “Bohnenkamphaus”, a conference venue at the heart of the botanical gardens of the University of Osnabrück.

We were able to negotiate special prices for the NEAT participants in three hotels in Osnabrück. Rooms are reserved at this rate until 31st of July 2023. Please book via email or via phone, booking codeword for both hotels is “neat 2023”.

Vienna House Remarque Walking distance to venue: 15 minutes Single room: 109 Eur/night per room Double room: 109 Eur/night per room Booking codeword: “neat 2023” Web: Remarque Hotel

Walhalla Hotel Walking distance to venue: 18 minutes Single room: 94 Eur/night Double room: 104 Eur/night Booking codeword: “neat 2023” Web: Hotel Walhalla

The joint workshop dinner will take place at the Portobar in Osnabrück – reachable on foot from the event and hotel.
Address: Weidenstraße 2, 49080 Osnabrück

Web: https://portobar-restaurant.de/restaurant/
Due to the generous donations by our sponsors, the workshop dinner will be free of charge (registration required).

Keynote 1: Prof. Matthew Larkum Decoding the cortex: deep pyramidal insights into computation

Keynote 2: Prof. Mariya Toneva “Language modeling beyond language modeling.”

Sunday September 24, 2023
17:00 – 19:00	Welcome reception
19:00 – 22:00	Dinner matching Similar to CCN mind-matching, we plan to match scientists according to their research interests. Mind-matched groups will have joint dinner at local restaurants.
Monday September 25, 2023
08:30 – 09:00	Registration
09:00 – 09:15	Welcome
09:15 – 10:15	Keynote 1 – Matthew Larkum
	Coffee Break
10:30 – 11:15	Science Shuffle
11:15 – 12:45	Poster Session 1
	Lunch
13:00 – 14:30	Poster Session 2
14:30 – 15:30	Debate Pods
	Coffee Break
16:00 – 17:00	Keynote 2 – Mariya Toneva
17:00 – 17:30	Closing Remarks
19:00	Joint Dinner

Katja Seeliger	Investigating the sensitivity of higher order visual areas with brain-optimization of common convolutional neural network architectures
Sushrut Thorat	Characterising representation dynamics in recurrent neural networks for object recognition
Jessica Thompson	Numerical reasoning with dual-stream neural networks.
Alessandro T Gifford	A large and rich EEG dataset for modeling human visual object recognition.
Ayu M I Gusti Bagus	High-Level Visual Cortex Representations Linearly Generalize Like Humans, Unlike current ANNs
Adrien Doerig	Visuo-semantic transformation in the human brain and DNNs
Kai Sandbrink	“How is control sensed and integrated into decision-making?”
Johannes Singer	Revealing the locus and content of behaviorally relevant information about real-world scenes in human visual cortex.
Laura Hansel	MorphOcc: Implicit Model for Representing Neuronal Morphologies
Ahmed ElGazzar	Modelling neural dynamics with neural differential equations
Agnessa Karapetian	Empirically identifying and computationally modelling the brain-behaviour relationship for human scene categorization.
Siddharth Chaturvedi	Embodied Intelligence in Simple Dynamical Systems
Sari Sadiya	Relating Artificial and Cognitive Representations
Giacomo Aldegheri	Computational models of relational processing in human scene-selective cortex
Micha Heilbron	Higher-level spatial prediction during natural scene perception in mouse visual cortex
Maartje Koot	The Role of Predictive Dynamics in ANN Image Classification
Joachim Bellet	Dynamic selectivity of visual features in macaque monkey prefrontal cortex: A comparative analysis with deep neural networks
Noor Seijdel	Network depth improves scene segmentation: a critical test with computer generated images.
Michaela Vystrcilova	Benchmarking system identification models of the retina.
David Richter	What did you expect? Prediction error tuning in sensory cortex
Farbod Nosrat Nezami	Time scale-plasticity learning rule for dendritic neuron model to achieve online time-invariant sequence processing
Gabriele Merlin	Language models and brain alignment: beyond word-level semantics and prediction – Gabriele Merlin and Mariya Toneva
Elaheh Akbarifathkouhi	Using CNNs to understand why we have an other-race effect
Victoria Bosch	End-to-end topographic networks as models of cortical map formation and human visual behaviour: moving beyond convolutions
Timo van Kerkoerle	Temporal dynamics of feature selectivity in neuronal populations in macaque monkey prefrontal cortex
Philip Sulewski	The Active Visual Semantics Dataset: Understanding visual intelligence in action.
Cliona O’Doherty	Time as a teacher – infant AI & fMRI
Justus Hübotter	Spiking neural networks for robot control
Sebastian Musslick	Augmenting EEG with Generative Adversarial Networks Enhances Brain Decoding Across Classifiers and Sample Sizes
David-Elias Künstle	Psychophysical scaling with ordinal embedding methods
Peter König	Improved spatial knowledge acquisition through sensory augmentation Improved spatial knowledge acquisition through sensory augmentation.
Brett David Roads	Enriching ImageNet with Human Similarity Judgments and Psychological Embeddings.
Johannes Mehrer	Topographic ANNs predict neural and behavioral responses to causal perturbations
Shreya Kapoor	Perception of Mooney faces: Extreme Generalization through Inverse Rendering?
Daniel Anthes	Diagnosing Catastrophe: Large Parts of Accuracy Loss in Continual Learning can be Accounted for by Readout Misalignment.
Clemens G Bartnik	Human perception of navigational affordances in real-world environments
Katharina Dobs	Using DNNs to understand why face perception works the way it does
Dota Tianai Dong	How are Language and Vision Dynamically Integrated in the Brain During Naturalistic Movie Viewing
Lea-Maria Schmitt	What recurrent dynamics underlie perceptual inference?
Maria Eckstein	Predictive and Interpretable: Combining Artificial Neural Networks and Classic Cognitive Models to Understand Human Learning and Decision Making
Pavithra Elumalai	Models for area V4 in free viewing macaques
Amber Brands	Spatiotemporal adaptation through divisive normalization improves deep neural network recognition of objects in noise.
Davide Cortinovis	The role of action-related properties in shaping the object space in the biological and artificial brain.
Gabriele Merlin	Language models and brain alignment: beyond word-level semantics and prediction
Niklas Müller	Investigating the Impact of High-Quality Natural Image Data for Training DCNNs;
Bernhard Egger	ReWaRD: Retinal Waves for Pre-Training Artificial Neural Networks Mimicking Real Prenatal Development