CNS*2025 Florence

Brain-inspired computing looks to mimic how the human brain works to improve artificial intelligence (AI) systems. This area has gained a lot of interest recently because it helps us create stronger and more efficient AI models while tackling challenges faced by current artificial neural networks.

This workshop will cover a range of topics, including biological neural networks, cognitive computing, and biologically-inspired algorithms. We will discuss how learning from the brain's structure and operations can lead to new solutions for complex issues in AI, machine learning, and data processing.

The workshop will include talks from experts in the field and interactive panel discussions. Participants will have the chance to collaborate, share ideas, and connect with others who are excited about using biological principles to advance technology.

Current computational neuroscience studies are often limited to a single scale or simulator, with many still relying on standalone simulation code due to computational power and technology constraints. Simulations incorporating biophysical properties and neural morphology typically focus on single neurons or small networks, while large-scale neural network simulations often resort to point neurons as a compromise to incorporate plasticity and cell diversity. Whole-brain simulations, on the other hand, frequently sacrifice details at the individual neuron and network composition levels.
This workshop introduces recent advances leveraging the next-generation simulator Arbor, designed to overcome these challenges. Arbor enables seamless conversion from the widely used NEURON simulator, facilitates the study of functional and structural plasticity in large neural networks with detailed morphology, and supports multi-scale modeling through co-simulation, integrating microscopic and macroscopic levels of simulation.
Arbor is a library optimized for efficient, scalable neural simulations by utilizing both GPU and CPU resources. It supports the simulation of both individual neurons and large-scale networks while maintaining detailed biophysical properties and morphological complexity. The workshop will feature presentations covering key aspects:

Effortless Transition from NEURON to Arbor - Dr. Beatriz Herrera - Allen Brain Institute, USA
Introducing to the SONATA format, which simplifies the migration process and enables cross-simulator validation, ensuring a smooth transition to Arbor for researchers familiar with NEURON.

Structural Plasticity Simulations - Marvin Kaster & Prof. Felix Wolf - TU Darmstadt, Germany 
Presenting ReLEARN and Arbor’s capabilities in modeling distance-dependent structural plasticity, providing insights into structural changes.

Synaptic Plasticity -  Dr. Jannik Luboeinski - University of Göttingen, Germany
Showcasing Arbor’s capabilities in modeling calcium-based functional plasticity.

Multi-Scale Co-Simulation with TVB -  Prof. Thanos Manos - CY Cergy-Paris University, France
Demonstrating Arbor’s co-simulation with The Virtual Brain (TVB) platform, illustrating the study of epilepsy propagation as an example of multi-scale modeling.

The workshop will conclude with an interactive coding session, offering participants hands-on experience with Arbor and an opportunity to apply the presented concepts.

Note: On the 9th, the room will be Hall 1A.

Workshop website: https://kgatica.github.io/CNS2025-InfoTeory-W.io/

Methods originally developed in Information Theory have found wide applicability in computational neuroscience. Beyond these original methods there is a need to develop novel tools and approaches that are driven by problems arising in neuroscience. A number of researchers in computational/systems neuroscience and in information/communication theory are investigating problems of information representation and processing. While the goals are often the same, these researchers bring different perspectives and points of view to a common set of neuroscience problems. Often they participate in different fora and their interaction is limited. The goal of the workshop is to bring some of these researchers together to discuss challenges posed by neuroscience and to exchange ideas and present their latest work. The workshop is targeted towards computational and systems neuroscientists with interest in methods of information theory as well as information/communication theorists with interest in neuroscience.

This is the 20th iteration of this workshop at CNS --join us to celebrate!

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.

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.

Speakers

Matthias 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

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