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!