BI 220 Michael Breakspear and Mac Shine: Dynamic Systems from Neurons to Brains

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Sep 10, 2025

Michael Breakspear is a Professor of Systems Neuroscience in Australia, and Mac Shine runs the Shine Lab at the University of Sydney. They explore the dynamic systems from neurons to entire brains. The duo discusses how scaling from single neurons to networks impacts cognitive functions. They delve into the complexities of cerebellar neurons, emphasizing innovative technologies like deep learning and optogenetics. Their conversation highlights the interplay between arousal and performance through the Yerkes-Dodson law, revealing fascinating insights into neural dynamics and collaboration in neuroscience.

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INSIGHT

Principles Versus Implementation

Systems neuroscience balances abstract principles with biological implementation details.
Implementation (e.g., neuromodulation, plasticity) can shape which theoretical principles apply in the brain.

ANECDOTE

A First Computational Conversion

Mac described his first computational collaboration with Michael using The Virtual Brain and FitzHugh–Nagumo models.
That project taught him how small parameter changes can have large system-level effects and sparked his modeling interest.

ADVICE

Model With A Clear Question

Bring a clear question into modeling and choose a model 'simple but not too simple'.
Use the right level of abstraction to make interpretable, testable predictions.

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What changes and what stays the same as you scale from single neurons up to local populations of neurons up to whole brains? How tuning parameters like the gain in some neural populations affects the dynamical and computational properties of the rest of the system.

Those are the main questions my guests today discuss. Michael Breakspear is a professor of Systems Neuroscience and runs the Systems Neuroscience Group at the University of Newcastle in Australia. Mac Shine is back, he was here a few years ago. Mac runs the Shine Lab at the University of Sidney in Australia.

Michael and Mac have been collaborating on the questions I mentioned above, using a systems approach to studying brains and cognition. The short summary of what they discovered in their first collaboration is that turning up or down the gain across broad networks of neurons in the brain affects integration - working together - and segregation - working apart. They map this gain modulation on to the ascending arousal pathway, in which the locus coeruleus projects widely throughout the brain distributing noradrenaline. At a certain sweet spot of gain, integration and segregation are balanced near a bifurcation point, near criticality, which maximizes properties that are good for cognition.

In their recent collaboration, they used a coarse graining procedure inspired by physics to study the collective dynamics of various sizes of neural populations, going from single neurons to large populations of neurons. Here they found that despite different coding properties at different scales, there are also scale-free properties that suggest neural populations of all sizes, from single neurons to brains, can do cognitive stuff useful for the organism. And they found this is a conserved property across many different species, suggesting it's a universal principle of brain dynamics in general.

So we discuss all that, but to get there we talk about what a systems approach to neuroscience is, how systems neuroscience has changed over the years, and how it has inspired the questions Michael and Mac ask.

Read the transcript.

0:00 - Intro 4:28 - Neuroscience vs neurobiology 8:01 - Systems approach 26:52 - Physics for neuroscience 33:15 - Gain and bifurcation: earliest collaboration 55:32 - Multiscale organization 1:17:54 - Roadblocks