Probabilistic Models

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.

• Dr Ruairidh M. Battleday (Oxford)

• Professor Aapo Hyvärinen (University of Helsinki): Painful Intelligence: What AI Can Tell Us About Human Suffering

• Dr Ruairidh Battleday (Oxford University): Probabilistic Models of Cognition and Machine Learning: Past and Future Directions
• Professor Bill Thompson (University of California, Berkeley): Distributed Computation by Social Learning

• 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

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.

• Dr James Whittington (University of Oxford; Stanford University)

• Professor Peter Latham (Gatsby Unit, UCL): What's the Question and How Do We Answer It?

• Dr James Whittington (University of Oxford; Stanford University): A unifying framework for frontal and temporal representation of memory
• Dr Thomas Parr (University of Oxford): From models to maladies

• 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

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.

• Professor Dan V. Nicolau Jr (King's College London)

• 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

• 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

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.

• Dr Ilia Sucholutsky (Princeton University)

• Professor Janneke Jehee (Donders Institute): Probabilistic Representations In The Human Visual Cortex

• Dr Ilia Sucholutsky (Princeton University): How and Why We Should Study Representational Alignment
• 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

• 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