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The Fourth International Convention on the
Mathematics Of Neuroscience and AI
28th September - 1st October, 2023.
Old Town, Rhodes.
Virtual or in-person.
Two decades into the 21st century, can we claim to be any closer to a unified model of the brain?
In this exploratory symposium, we invite submissions for short talks and posters presenting general mathematical models of brain function. We give priority to those models that can account for brain or behavioural data, or provide simulations to that effect.
Keynote Speakers
Professor Aapo Hyvärinen
University of Helsinki
Professor Janneke Jehee
Donders Institute
Professor Peter Latham
Gatsby Unit, UCL
Sessions
Chair: Dr Ruairidh M. Battleday
(Oxford)
Chair: Dr James Whittington
(University of Oxford; Stanford University)
Chair: Professor Dan V. Nicolau
(King’s College London)
Chair: Dr Ilia Sucholutsky
(Princeton University)
Keynote Talks
University of Helsinki
Painful intelligence: What AI can tell us about human suffering
This talk introduces my recent e-book with the same title, freely available at https://www.cs.helsinki.fi/u/ahyvarin/painintl/. The book uses the modern theory of artificial intelligence (AI) to understand human suffering or mental pain. Both humans and sophisticated AI agents process information about the world in order to achieve goals and obtain rewards, which is why AI can be used as a model of the human brain and mind. The book starts with the assumption that suffering is mainly caused by frustration. Frustration means the failure of an agent (whether AI or human) to achieve a goal or a reward it wanted or expected. Frustration is inevitable because of the overwhelming complexity of the world, limited computational resources, and scarcity of good data. In particular, such limitations imply that an agent acting in the real world must cope with uncontrollability, unpredictability, and uncertainty, which all lead to frustration. Such computational theory is finally used to derive various interventions or training methods that will reduce suffering in humans. The ensuing interventions are very similar to those proposed by Buddhist and Stoic philosophy, and include mindfulness meditation.
Biocomputation
The prevailing modern scientific paradigm of the brain is a computational one. But if the brain is a computer—which is an 'if'—it must have operating principles, abilities and limitations that are radically different to those of artificial computers. In this session, talks will explore diverse topics within quantitative neuroscience that consider the brain as a device for computation, broadly conceived.
Session Chair
Professor Dan V. Nicolau Jr (King’s College London)
TBA
Invited Talks
Professor Dan Nicolau Sr (McGill)
Setting the baseline of what intelligence could be: the case of space searching by populations of filamentous fungal hyphae
Professor Andrew Adamatzky and Dr. Panagiotis Mougkogiannis (University of the West of England)
Towards proteinoid neuromorphic computers
Dr Ilias Rentzeperis (Spanish National Research Council)
Modelling a continuum of simple to complex cell behavior in V1 with the INRF paradigm
Contributed Talks
Professor Marcelo Bertalmío (Spanish National Research Council)
Modeling challenging visual phenomena by taking into account dynamic dendritic nonlinearities
Dr Steeve Laquitaine (Swiss Federal Institute of Technology)
Using a large-scale biophysically detailed neocortical circuit model to map spike sorting biases
Jia Li (KU Leuven)
Self-organization of log-normally distributed connection strength
Hanna Derets (University of Waterloo)
Distance Metrics and Minimization of Epsilon Automata, with Applications to the Analysis of EEG Microstate Sequences
Neurotheory
While neuroscientists have increasingly powerful deep learning models that predict neural responses, it is not clear that these models are correspondingly increasing our understanding of what neurons are actually doing. In this session, we will take a more mechanistic approach to understanding how networks of neurons afford complex computations, both by both considering mechanistic neural model along with mathematical theories that say how neurons should behave and crucially why they behave that way.
Session Chair
Dr James Whittington (University of Oxford; Stanford University)
A unifying framework for frontal and temporal representation of memory
Invited Talks
Dr Thomas Parr (University of Oxford)
From models to maladies
Contributed Talks
Dr Tommaso Salvatori (Verses.ai)
On the past, present, and future of predictive coding
Carol Upchurch (Louisiana State University)
Persistent Silencing of PV+ Inhibitory Interneurons Results from Proximity to a Subcritical Hopf Bifurcation
Tyler Giallanza (Princeton University)
Adapting to a changing environment with controlled retrieval of episodic memories
Declan Campbell (Princeton University)
Unraveling geometric reasoning: A neural network model of regularity biases
Probabilistic Models
Design by Amey Zhang
How should an intelligent agent behave in order to best realize their goals? What inferences or actions should they make in order to solve an important computational task? Probabilistic models aim to answer these questions at an abstract computational level, using tools from probability theory and statistical inference.
In this session we will discuss how such optimal behavior should change under different conditions of uncertainty, background knowledge, multiple agents, or constraints on resource. This can be used to understand human behavior in the real world or the lab, as well as build artificial agents that learn robust and generalizable world models from small amounts of data.
Session Chair
Dr Ruairidh Battleday (Oxford University)
Probabilistic Models of Cognition and Machine Learning: past and future directions
Invited Talks
Professor Bill Thompson (University of California, Berkeley)
Distributed Computation by Social Learning
Contributed Talks
Professor Volker Tresp (Munich Center for Machine Learning)
The Tensor Brain: A Unified Theory of Perception, Memory and Semantic Decoding
Professor Daniel Graham (HWS)
Collision Models of Brain Network Communication
Rahul Jain (Pomona College)
You Got Hexxed: Persistence during Complex Skill Learning
Representational Alignment
We may live in the same world but do we represent it in the same way? If not, then how do we still manage to effectively communicate and cooperate in a system about which we fundamentally disagree?
From Plato's Sophist to contemporary studies comparing LLMs to human brains, the study of diverging representations has fascinated researchers for millenia and continues to be an active area of research in neuroscience, cognitive science, and machine learning.
In this session, we will discuss how we can measure and manipulate the representational alignment of (both biological and artificial) intelligent entities (e.g. humans and neural networks). We will also explore the implications of representational (mis)alignment between intelligent entities on their ability to communicate, cooperate, and compete.
Session Chair
Dr Ilia Sucholutsky (Princeton University)
How and why we should study representational alignment
Invited Talks
Professor Bradley Love (UCL)
Aligning embedding spaces for model evaluation and learning
Professor Iris Groen (University of Amsterdam)
Are DNNs representationally aligned with human scene-selective cortex? Elucidating the influence of image dataset, network training and cognitive task demands
Contributed Talks
Professor Mayank Kejriwal (University of Southern California)
On using Fodor's theory of modularity for situating large language models within a larger artificial general intelligence architecture
Dr Andreea Bobu (Boston Dynamics AI Institute)
Aligning Robot and Human Representations
Schedule
Thurs 28th September 2023 (UTC+3)
10.00 Opening remarks
Dr Ruairidh M. Battleday and Professor Dan V. Nicolau Jr
10.30 Keynote: Professor Aapo Hyvärinen
Painful intelligence: What AI can tell us about human suffering
Session: Probabilistic Models
11.30: Dr Ruairidh Battleday (Chair; Oxford University): Probabilistic Models of Cognition and Machine Learning: past and future directions
12.00: Professor Bill Thompson (University of California, Berkeley): Distributed Computation by Social Learning
12.30: Lunch
13.30 Professor Daniel Graham (Hobart and William Smith Colleges): Collision Models of Brain Network Communication
14.00: Professor Volker Tresp (Munich Center for Machine Learning) : The Tensor Brain: A Unified Theory of Perception, Memory and Semantic Decoding
14.30: Rahul Jain (Pomona College): You Got Hexxed: Persistence during Complex Skill Learning
Session: Biocomputation
15.00 Professor Dan V. Nicolau Jr (Chair; King’s College London): Introduction
15.10: Dr Steeve Laquitaine (The Swiss Federal Institute of Technology): Using a large-scale biophysically detailed neocortical circuit model to map spike sorting biases
15.35: Jia Li (KU Leuven): Self-organization of log-normally distributed connection strength
16.00: Dr. Panagiotis Mougkogiannis (and Professor Andrew Adamatzky; University of the West of England): Towards proteinoid neuromorphic computers
16.40: Coffee Break
16.50: Professor Marcelo Bertalmío (Spanish National Research Council): Modeling challenging visual phenomena by taking into account dynamic dendritic nonlinearities
17.15 Dr Ilias Rentzeperis (Spanish National Research Council): Modelling a continuum of simple to complex cell behavior in V1 with the INRF paradigm
17.40: Hanna Derets (University of Waterloo): Distance Metrics and Minimization of Epsilon Automata, with Applications to the Analysis of EEG Microstate Sequences
18.20: Professor Dan Nicolau Sr (McGill): Setting the baseline of what intelligence could be: the case of space searching by populations of filamentous fungal hyphae
19:00 Welcome reception
Socratous Garden (https://maps.app.goo.gl/uUcS9rhFYLkMLrYG9)
Fri 29th September 2023 (UTC+3)
09:30 Take bus to Lindos
Lindos (https://goo.gl/maps/M1P6sZbR24vznK4N9)
Either: leave from bus stop at 9.30am. You can buy bus tickets with card or cash at this office.
Or, self-organize cars or taxis.
12:00-12:30 Spotlight session 1 (in person, in Lindos, MedEast)
Dr Jonathan V. Gill (NYU): The geometry and role of sequential activity in olfactory processing
Sabahaddin Taha Solakoglu (Hacettepe University): Analysis and comparison of synaptic inputs from three brain regions onto mPFC dendrites in stress resilient and stress vulnerable mice
Hang Li (LMU Munich): Do Artificial Neural Networks Understand Each Other?
Francesco Guido Rinaldi (SISSA): Intuitive Interpretation in Uncertain Environments: A Bayesian Perspective
12:30-13:00 Poster session 1 (in person, in Lindos, MedEast)
Dr Jonathan V. Gill (NYU): The geometry and role of sequential activity in olfactory processing
Sabahaddin Taha Solakoglu (Hacettepe University): Analysis and comparison of synaptic inputs from three brain regions onto mPFC dendrites in stress resilient and stress vulnerable mice
Hang Li (LMU Munich): Do Artificial Neural Networks Understand Each Other?
Francesco Guido Rinaldi (SISSA): Intuitive Interpretation in Uncertain Environments: A Bayesian Perspective
Declan Campbell (Princeton University): Unraveling geometric reasoning: A neural network model of regularity biases
13:00-14:00 Lunch (MedEast)
14:00-20:00 Conference expedition
Lindos (https://goo.gl/maps/M1P6sZbR24vznK4N9)
Last bus home to Old Town is at 21.30… but we would suggest getting an earlier one than that (they leave every half an hour).
Sat 30th September 2023 (UTC+3)
10:00 Keynote: Professor Peter Latham
What’s the question and how do we answer it?
Session: Neurotheory
11:10 - 11:40: Carol Upchurch (Louisiana State University): Persistent Silencing of PV+ Inhibitory Interneurons Results from Proximity to a Subcritical Hopf Bifurcation
11:40 - 12:10: Tyler Giallanza (Princeton University): Adapting to a changing environment with controlled retrieval of episodic memories
12:10 - 12:20: Break
12:20 - 13:00: Dr James Whittington (Chair; University of Oxford; Stanford University): A unifying framework for frontal and temporal representation of memory
13:00: Lunch
14:00 - 14:50: Dr Thomas Parr (University of Oxford): From models to maladies
14:50 - 15:00: Break
15:00 - 15:30: Dr Tommaso Salvatori (Verses.ai): On the past, present, and future of predictive coding
15:30 - 15:45: Spotlight 1: Shivang Rawat (NYU): Coherence influences the dimensionality of communication subspaces
15:45 - 16:00: Spotlight 2: Declan Campbell (Princeton University): Unraveling geometric reasoning: A neural network model of regularity biases
16:30-17:00 Spotlight session 2 (virtual)
Dr Michael Popov (OMCAN network; University of Oxford): Round Numbers and Representational Alignment. Fundamentalness of Ramanujan’s theorems
Dr Charles Cohen (Fidelis.ai): Identifying the active properties of layer 5 myelinated axons with automated and robust optimization of action potential propagation
Dr Aslan Satary Dizaji (AutocurriculaLab & Neuro-Inspired Vision): Dimensionality of Intermediate Representations of Deep Neural Networks with Biological Constraints
Dr Anita Keshmirian (Munich LMU): Deciphering Causal Reasoning: Human vs. Language Models
Arvind Saraf (Attention Tag): Simulating the (Equinamous) Subconscious Mind
Michael Yifan Li (Stanford): Learning to Learn Functions
Shirin Vafaei (Osaka University): Brain-grounding of word embeddings for improving brain decoding of visual stimuli
17:00-18:00 Poster session 2 (virtual)
Dr Charles Cohen (Fidelis.ai): Identifying the active properties of layer 5 myelinated axons with automated and robust optimization of action potential propagation
Dr Aslan Satary Dizaji (AutocurriculaLab & Neuro-Inspired Vision): Dimensionality of Intermediate Representations of Deep Neural Networks with Biological Constraints
Dr Sunder Bukya (University of Hyderabad ): Gender Disparities in Spatial Cognition: The Influence of Stereopsis and Mental Rotation
Arvind Saraf (Attention Tag): Simulating the (Equinamous) Subconscious Mind
Jay Verma (University of Delhi): Computational Modeling of Hyperpolarizing Astrocytic Influence on Cortical Up-Down State Transitions
Shivang Rawat (NYU): Coherence influences the dimensionality of communication subspaces
Michael Yifan Li (Stanford): Learning to Learn Functions
Shirin Vafaei (Osaka University): Brain-grounding of word embeddings for improving brain decoding of visual stimuli
Asit Pal (NYU): Feedback-Dependent Communication Subspace in a Multistage Recurrent Circuit Model Implementing Normalization
Simon Frieder (Oxford): (Non-)Convergence Results for Predictive Coding Networks
20:00 Conference dinner
Pizanias (Rhodes Old Town)
Sun 1st Oct 2023 (UTC+3)
10:00 Keynote: Professor Janneke Jehee
Probabilistic representations in the human visual cortex
Session: Representational alignment
11.15: Dr Ilia Sucholutsky (Chair; Princeton University): How and why we should study representational alignment
11.45: Professor Bradley Love (UCL): Aligning embedding spaces for model evaluation and learning
12.15: Professor Iris Groen (University of Amsterdam): Are DNNs representationally aligned with human scene-selective cortex? Elucidating the influence of image dataset, network training and cognitive task demands
13:00: Lunch
14:00: Professor Mayank Kejriwal (USC): On using Fodor's theory of modularity for situating large language models within a larger artificial general intelligence architecture
14.30: Dr Andreea Bobu (Boston Dynamics AI Institute): Aligning Robot and Human Representations
15:00 Final Conference Expedition and Closing Remarks
Speak to organizers to arrange transportation
Topics
Including but not limited to:
computational neuroscience
reinforcement learning
cognition/protocognition
neural circuits and ANNs
neural complexity
brain-machine interfaces
biocomputation
mathematical approaches to consciousness
Symposium Chairs
Professor Dan V. Nicolau Jr
King’s College London; and Nuffield Department of Medicine, University of Oxford
Dr Ruairidh Battleday
Center for Brain Science, Harvard University
Sponsors
Many thanks to our generous sponsors from Google Deepmind (www.deepmind.com) and the Diverse Intelligences grant (www.disi.org).
Google DeepMind
Templeton World Charity Foundation
Diverse Intelligences Institute