CNS*2025 Florence

One of the goals of the NIH BRAIN (Brain Research Through Advancing Innovative Neurotechnologies) Initiative is to develop theories, models, and methods to understand brain functions and their causal links to behaviors. The modern advances in neuroscience and AI have generated growing interests in NeuroAI, as witnessed by the feedback from the recent NIH BRAIN NeuroAI workshop (Nov. 12-13, 2024). Briefly, NeuroAI is aimed to, first, use AI to understand and improve the brain and behaviors, and second, to develop brain-inspired AI systems for robust, faster and more efficient operations and performances. Motivated by the new wave and developments in NeuroAI, this workshop invites leading experts and new investigators from various research backgrounds to discuss many emerging research topics. The goal of this full-day workshop, in the name of BRAIN 2.0 (BRidging AI and Neuroscience), is to focus on building the bridge between AI and neuroscience, to discuss new research directions and outstanding questions, and to foster team collaborations and open science. Research topics of interest include but not limited to neural transformers and foundation models, new neural network architectures, distributional or meta reinforcement learning, structural reasoning and inference, large language models (LLMs), and digital twins brain. The format of the workshop will consist of both overview-like and research-oriented lecture presentations as well as panel discussions.

This workshop will explore how brain digital twins are revolutionizing research into pathological brain conditions and transforming the landscape of precision medicine. Participants will learn how these models work and how they integrate data and tools from different fields, such as molecular neuroscience, network theory and dynamical systems. We will discuss how digital twins can help identify early biomarkers able to characterize pathological states and predict disease progression. Another key topic will be the use of digital twins as in silico environments for testing potential treatments before applying them in clinical scenarios.
Through real-world examples and interactive sessions, we will tackle some of the challenges that come with this innovative approach, such as achieving anatomical precision, handling large datasets, and ensuring ethical use in patient care. The focus will remain on making these cutting-edge tools accessible and impactful, not just for researchers but also for clinicians aiming to deliver more effective, tailored care to their patients.

Full workshop program

Schedule
9:00-9:30 Fleur Zeldenrust
Heterogeneity, non-linearity and dimensionality: how neuron and network properties shape computation
9:30-10:00 Vassilis Cutsuridis
Synapse strengthening in bistratified cells leads to super memory retrieval in the hippocampus
10:00-10:30 Spyridon Chavlis
Dendrites as nature's blueprint for a more efficient AI
10:30-11:00 Coffee Break
11:00-11:30 Andreas Tolias
Foundation models and digital twins of the brain (online)
11:30-12:00 Robert Legenstein
Spatio-Temporal Processing with Dynamics-enhanced Spiking Neural Networks
12:00-12:30 Max Garagnani
Concept superposition and learning in standard and brain-constrained deep neural networks
12:30-14:00 Lunch
14:00-14:30 Martin Trefzer
Motifs, Modules, and Mutations: Building Brain-like Networks
14:30-15:00 Julian Göltz
From biology to silicon substrates: neural computation with physics
15:00-15:30 Maxim Bazhenov
Do Neural Networks Dream of Electric Sheep?
15:30-16:00  Coffee Break
16:00-16:30 Dhireesha Kudithipudi
Temporal Chunking Enhances Recognition of Implicit Sequential Patterns
16:30-17:00 Thomas Nowotny
Auto-adjoint method for gradient descent in spiking neural networks
17:00-18:00 Questions and Debate
18:00 End of Workshop

Full workshop program

Please visit the dedicated website for full details: https://nicolomeneghetti.github.io/ECP_CNS2025_Wshop/

Simulating large-scale neural activity is essential for understanding brain dynamics and linking in silico models to experimentally measurable signals like LFP, EEG, and MEG. These simulations, ranging from detailed biophysical models to simplified proxies, bridge microscale neural dynamics with meso- and macro-scale recordings, offering powerful tools to interpret data, refine analyses, and explore brain function. Recent advances have demonstrated the clinical and theoretical value of such models, shedding light on oscillations, excitation-inhibition balance, and biomarkers of neurological disorders like epilepsy, Alzheimer's, and Parkinson’s disease. This workshop will cover the latest methodologies, hybrid modeling approaches, and applications of brain signal simulations.By gathering experts across disciplines, it aims to foster collaboration and advance our understanding of brain function and dysfunction.


09:15 – 09:50
Dominik Peter Koller, Berlin Institute of Health (BIH) at Charité – Universitätsmedizin Berlin, Berlin, Germany
Title: "How structural connectivity directs cortical traveling waves and shapes frequency gradients"

09:50 – 10:25
Gaute T. Einevoll, Department of Physics, University of Oslo, Oslo, Norway
Title: "Modeling electric brain signals and stimulation"

10:30 – 11:00
Coffee Break

11:00 – 11:35
Johanna Senk, Institute for Advanced Simulation (IAS-6), Jülich Research Centre, Jülich, Germany
Title: "Large-scale modeling of mesoscopic networks at single-neuron resolution"

11:35 – 12:10
Pablo Martínez Cañada, Research Centre for Information and Communications Technologies (CITIC), University of Granada, Granada
Title: "Inverse Modelling of Field Potentials from Simulations of Spiking Network Models: Applications in Neuroscience Research and Clinical Settings"

12:10 – 12:40
Nicolò Meneghetti, The Biorobotics Institute, Sant’Anna School of Advanced Studies, Pisa, Italy
Title: "From microcircuits to mesoscopic signals: a kernel approach to efficient and interpretable LFP estimation"

12:45 - 14.00
Lunch Break

14:15 – 14:50
Emily Patricia Stephen, Department of Math and Statistics, Boston University, Boston, MA, United States of America
Title: "Connecting biophysical models to empirical power spectra using Filtered Point Processes"

14:50 - 15:25
Madeleine Lowery, School of Electrical and Electronic Engineering, University College Dublin, Dublin, Ireland
Title: "Modelling Neural Activity During Adaptive Deep Brain Stimulation for Parkinson’s Disease"

15:30 - 16:00
Coffee Break 

16:00 – 16:35
Meysam Hashemi, Aix Marseille University INSERM, INS, Institute for Systems Neuroscience, Marseille, France
Title: "Principles and Operation of Virtual Brain Twins"

16:35 - 17:10
Katharina Duecker, Brown University and University of Birmingham,  USA/UK
Title: "The Human Neocortical Neurosolver as an interactive modeling tool to study the multi-scale mechanisms of human EEG/MEG signals"

Aims and topic
Predictive processes are ubiquitous in the brain and thought to be critical for adaptive behaviours, such as rapid learning and generalisation of tasks and rules. Early works such as the computational vision model proposed by Rao and Ballard (1999) have inspired over two decades of theoretical, computational, and experimental research about predictive neural processing. Stemming from these early works, ongoing investigations provide a rich ecosystem of theory, experiments and computational models that expand beyond the notion of predictive coding. Further, thanks to rapidly developing neural recording technologies, large datasets at multiple scales of granularity and resolution are becoming increasingly available. New computational models enable us to gain a mechanistic understanding of how neural circuits learn to implement and deploy predictive computations. Yet, a full understanding of the underlying computational logic remains fleeting because different aspects are often studied in separate research programs (e.g., layer circuits vs whole-brain neuroimaging), with little cross-pollination. This symposium will look at predictive processing in the context of modern computational neuroscience. Speakers will discuss new theories extrapolating low-dimensional population activity, recent work exploring efficient coding in artificial neural networks and rats' visual cortex, coding hierarchies of prediction errors across brain areas, and computational modelling of behaviour and neural data across species (humans, monkeys, rodents), focusing on high-level, flexible behaviours (hierarchical reasoning, context changes, conceptual knowledge). The topic addressed in this symposium is central to multiple streams of research in computational neuroscience, e.g., perception, decision-making, motor control, and social behaviour. Our aspiration is to stimulate interaction among researchers working in different disciplines and highlight the open questions that will shape future research.

SpeakersMatthias Tsai --Bern University, Switzerland
Rohan Rao --Newcastle University / Oxford University, UK
Erin Rich --New York University, USA
Silvia Maggi --University of Nottingham, UK
Armin Lak --Oxford University, UK
Abhishek Banerjee --Oxford University / Queen Mary University of London, UK
Aurelio Cortese --ATR Institute International, Japan

Schedule
9.00 - 9.05: opening remarks
9.05 - 9.45: Erin Rich,
9.45 - 10.15: Rohan Rao,
10.15 - 10.30: coffee break
10.30 - 11.00: Matthias Tsai
11.00 - 11.45: Aurelio Cortese
11.45 - 12.00: discussion
12.00 - 14.00: Lunch
14.00 - 14.45: Abhishek Banerjee,
14.45 - 15.30: Silvia Maggi
15.30 - 15.45: coffee break
15.45 - 16.30: Armin Lak,
16.30 - 17.00: discussion
17.00 - 17.05: closing remarks