We are excited to announce the TSVP Thematic Program "Synergistic Neuromodulatory Interactions in Neural Networks: Computational Principles of Learning and Cognition" in February and March 2027 (tentative).
Title: Synergistic Neuromodulatory Interactions in Neural Networks: Computational Principles of Learning and Cognition
Theme of the program: This thematic program will focus on how the multiscale organization of neuromodulatory systems, and their interactions influences adaptation and learning by shaping micro- and mesoscale connectivity across invertebrate and vertebrate species. Most studies to date have focused on characterizing the functional roles of individual neuromodulators. However, ample evidence now suggests that long range axons of single neurons co-release and co-transmit more than one neuromodulator whose intricate interactions in local microcircuits and brain regions underlie complex aspects of reward-guided learning and behavior. The program will bring together a key group of experimentalists and theoreticians to explore how neuromodulatory interactions dynamically regulate local and inter-areal connectivity, synaptic plasticity, and neuronal excitability in biological neural networks across species. A primary objective of this program is to apply biological insights into complex spatio-temporal connectivity patterns to develop new theories, potentially leading to the creation of “neuromodulation-aware” artificial neural networks capable of real-time adaptation and continual learning. The program builds on the hugely successful previous thematic program on "Neuromodulation of Adaptive Learning" held in July 2024 at OIST. The program and symposium attracted more than 50 eminent researchers and participants from around the world, further reinforcing OIST as a global hub for integrating experimental and theoretical research on neuromodulatory functions in neural circuits.
The current program will focus on interactions among different neuromodulators to shed light on how neuromodulators cooperate or compete to shape higher-order cognition.
Program coordinators
Srikanth Ramaswamy (Newcastle University)
Arvind Kumar (KTH Royal Institute of Technology),
Upinder S. Bhalla (National Centre for Biological Sciences)
Vatsala Thirumalai (National Centre for Biological Sciences)
Kenji Doya (OIST)
Bernd Kuhn (OIST)
Jeff Wickens (OIST)
Tentative Schedule
Week 1: The 5-week thematic program will begin with a two-day symposium, which will include brief talks by invited speakers. Thereafter, week 1 will cover the topic “Foundations of Neuromodulation and Adaptive Learning” with the following objectives: introduce foundational concepts in neuromodulation, adaptive learning, and the computational frameworks used to study these phenomena. Activities will include 3-4 lectures and discussions, followed by a hands-on session on data analysis and computational tools relevant to modelling neuromodulation.
Week 2: will focus on “Computational Models of Neuromodulatory Interactions” with the following objectives: developing computational models for studying neuromodulatory functions, modeling decision-making and adaptive learning, modeling co-release of neuromodulators and synaptic plasticity, reward prediction errors and reinforcement learning models, and linking each of these to the proposed multiscale neuromodulation framework model. Planned activities include 3-4 lectures and discussions followed by a hands-on coding sessions on reinforcement learning models with neuromodulatory interactions, and porting these concepts into the framework model.
Week 3: will focus on “Cross-Species Comparisons and Experimental Insights” with the following objectives: analyze neuromodulatory interactions in different species and investigate how comparative models can inform adaptive learning. Activities will include 3-4 seminars & guest lectures and a data analysis workshop with datasets from multiple species to identify cross-species patterns of neuromodulatory interactions and their computational roles. Participants will consider how one can derive common and species-specific subsets of the multiscale neuromodulation model.
Week 4: will cover “Synthesis and Application to NeuroAI” with the following objectives: synthesize knowledge from weeks 1-4 and explore the application of neuromodulatory principles in adaptive AI. Activities will include 3-4 lectures on neuromodulation and NeuroAI and a collaborative workshop for integrating neuromodulatory principles and interactions into AI models. Participants will be invited to consider how the biologically motivated components of the neuromodulatory framework model might map onto AI architectures.
Week 5: will focus on outreach activities and career development avenues in neuromodulation research with the following objectives: engage with a general audience for a simplified description of neuromodulatory interactions and functions and discuss career paths in neuroscience and computational modeling. Planned activities include 2-3 outreach lectures, career development panel and a roundtable on future directions.