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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 <a href="https://www.systemsneurosciencegroup.com/team">Systems Neuroscience Group</a> at the University of Newcastle in Australia. Mac Shine is back, he was here a few years ago. Mac runs the <a href="https://shine-lab.org/">Shine Lab</a> 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.



<ul class="wp-block-list">
<li>Breakspear: <a href="https://www.systemsneurosciencegroup.com/team">Systems Neuroscience Group</a>.
<ul class="wp-block-list">
<li><a href="https://x.com/DrBreaky">@DrBreaky</a>.</li>
</ul>
</li>



<li>Shine: <a href="https://shine-lab.org/">Shine Lab</a>.
<ul class="wp-block-list">
<li><a href="https://x.com/jmacshine">@jmacshine</a>.</li>
</ul>
</li>



<li>Related papers
<ul class="wp-block-list">
<li><a href="https://www.ngds-ku.org/Papers/J61/Breakspear.pdf">Dynamic models of large-scale brain activity</a></li>



<li><a href="https://www.nature.com/articles/s41467-019-08999-0">Metastable brain waves</a></li>



<li><a href="https://elifesciences.org/articles/31130">The modulation of neural gain facilitates a transition between functional segregation and integration in the brain</a></li>



<li><a href="https://shine-lab.org/wp-content/uploads/2024/10/2024_cell.pdf">Multiscale Organization of Neuronal Activity Unifies Scale-Dependent Theories of Brain Function</a>.</li>



<li><a href="https://shine-lab.org/wp-content/uploads/2025/03/2025_curropin.pdf">The brain that controls itself</a>.</li>



<li><a href="https://ccs.fau.edu/hbblab/pdfs/2024_Hancock_Kelso_NRN.pdf">Metastability demystified — the foundational past, the pragmatic present and the promising future</a>.</li>



<li><a href="https://direct.mit.edu/imag/article/doi/10.1162/IMAG.a.71/131445">Generation of surrogate brain maps preserving spatial autocorrelation through random rotation of geometric eigenmodes</a>.</li>
</ul>
</li>



<li>Related episodes
<ul class="wp-block-list">
<li><a href="https://braininspired.co/podcast/212/">BI 212 John Beggs: Why Brains Seek the Edge of Chaos</a></li>



<li><a href="https://braininspired.co/podcast/216/">BI 216 Woodrow Shew and Keith Hengen: The Nature of Brain Criticality</a></li>



<li><a href="https://braininspired.co/podcast/121/">BI 121 Mac Shine: Systems Neurobiology</a></li>
</ul>
</li>
</ul>



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

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