Name: Exploring Neuromodulators and How They Might Impact AI
Start: 2022-04-13T10:00:00-07:00
End: 2022-04-13T12:00:00-07:00

Neuromodulatory systems such as serotonin and dopamine seem to play an important role in our ability to effortlessly adapt to and learn in unpredictable, ever-changing environments. These systems can dynamically change how neurons learn and respond to stimuli based on context. Our brain has a diverse set of neuromodulatory systems, operating at multiple time scales and interacting in complex ways.

Can neuromodulatory principles enable better and more flexible learning in deep neural networks?

In this Brains@Bay meetup, our speakers will review what is known about neuromodulatory mechanisms and explore how they may provide useful insights into creating more efficient and truly intelligent machines.

Speaker Lineup:
➤ Srikanth Ramaswamy (Newcastle University)
➤ Jie Mei (The Brain and Mind Institute)
➤ Thomas Miconi (ML Collective)

The talks will be followed by a discussion panel and Q&A.

**We look forward to seeing you there!**

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➤ **Srikanth Ramaswamy and Jie Mei, What can deep neural networks learn from neuromodulatory systems?**

Abstract: Neuromodulators are signalling chemicals in the brain, which control the emergence of adaptive learning and behaviour. Neuromodulators including dopamine, acetylcholine, serotonin and noradrenaline operate on a spectrum of spatio-temporal scales in tandem and opposition to reconfigure functions of biological neural networks and to regulate global cognition and state transition. Although neuromodulators are important in shaping cognition, their phenomenology is yet to be fully realized in deep neural networks (DNNs). In this talk, we will first give an overview of the biological organizing principles of neuromodulators in adaptive cognition and highlight the competition and cooperation across neuromodulators. We will then discuss ongoing research on bio-inspired mechanisms of neuromodulatory function in DNNs, and propose a computational framework to incorporate their diverse functional settings and inspire new architectures of “neuromodulation-aware” DNNs.

➤ **Thomas Miconi, How to evolve your own lab rat: Learning to acquire novel cognitive tasks with evolution, plasticity and meta-meta-learning**

Abstract: A hallmark of intelligence is the ability to learn new flexible, cognitive behaviors - that is, behaviors that require discovering,
storing and exploiting novel information for each new instance of the
task. In meta-learning, agents are trained with external algorithms to
learn one specific cognitive task. However, animals are able to pick
up such cognitive tasks automatically, as a result of their evolved
neural architecture and synaptic plasticity mechanisms, including
neuromodulation. Here we evolve neural networks, endowed with plastic
connections and reward-based neuromodulation, over a sizable set of
simple meta-learning tasks based on a framework from computational
neuroscience. The resulting evolved networks can automatically acquire
a novel simple cognitive task, never seen during evolution, through
the spontaneous operation of their evolved neural organization and
plasticity system. We suggest that attending to the multiplicity of
loops involved in natural learning may provide useful insight into the
emergence of intelligent behavior.

Charmaine L.

Subutai Ahmad

Christy M.

Brains@Bay

Technology

Artificial Intelligence

Neuroscience

Machine Learning

Machine Intelligence

Artificial Intelligence Machine Learning Robotics

Deep Learning

Deep Reinforcement Learning

Computational Neuroscience

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Exploring Neuromodulators and How They Might Impact AI

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