The Frontier

Sean Carroll introduces the connectome as the brain's wiring diagram, detailing how neurons connect, akin to an engineering schematic. Bing Brunton clarifies that a fine-grain connectome maps individual cells, while a mesoscale one maps brain areas, with no consensus on the required resolution.

Bing Brunton highlights that mapping a human connectome at the cellular level is technologically impossible currently, contrasting it with human connectomes that typically represent connections between brain areas. The human brain contains approximately 85 billion neurons.

Bing Brunton states that roughly half the cells in the brain are glia, not neurons, and are increasingly recognized as vital with their own dynamics, not just support. This represents an exciting, emerging neuroscience field.

Bing Brunton emphasizes macroscopic organisms involve complex teamwork between cells and non-cellular substances, like bone, which is a living, vascularized structure. This highlights biology's inherently messy, interconnected nature.

The connectome is more than just a neuron connectivity matrix; it includes crucial information about cell identities (e.g., dopamine, serotonin neurons) and context-dependent message reception. While detailed biophysical properties matter, not all are required for holistic animal models.

The first full connectome, mapped about 30 years ago, was of the C. elegans nematode worm, a millimeter-long organism with ~300 neurons. Bing Brunton notes that despite this map, its behavior is hard to understand due to significant non-neural chemical and mechanical communication.

Bing Brunton reports the full connectome of the Drosophila fruit fly, including brain and ventral nerve cord, was published within the last year or two. The brain has ~150,000 neurons, with an additional 22,000 in the ventral nerve cord.

Unlike C. elegans, the Drosophila connectome is potentially easier to interpret due to the fruit fly's larger size, jointed limbs, and specialized cell types, features shared with humans. Neurons can be exceptionally long, enabling rapid signal transmission from a toe to the brainstem.

Bing Brunton explains all animal locomotion, from walking to breathing, involves rhythmic movements generated by Central Pattern Generators (CPGs) within the nervous system. These circuits autonomously produce cyclic instructions, known since the 1910s, though specific cellular implementations for walking were unclear.

Bing Brunton's team, with John Tuthill, simulated a fruit fly ventral nerve cord (4,000 neurons controlling two front legs). A "pruning study" then identified a minimal circuit of just three neurons - two excitatory (E1, E2) and one inhibitory (I1) - sufficient to generate the basic walking rhythm.

Brunton highlights a successful model-driven prediction: a previously unstudied neuron from the central brain made a fly leg tap when activated by a laser. Experimental validation confirmed this prediction, demonstrating the model's predictive power beyond simply fitting existing data.

Bing Brunton's lab aims to create "digital twins" of animals: biologically interpretable simulations of the nervous system within a biomechanically realistic body and virtual environment. This allows studying complex feedback loops and predicting behaviors human intuition cannot grasp.

Brunton critiques "digital sphinx" models achieving behavioral fidelity without biological accuracy, demonstrating it by training a C. elegans connectome to control a fly body with reinforcement learning. This shows deep learning can mimic behaviors even with mismatched neural architectures, emphasizing meaningful biological interfaces.

Bing Brunton suggests understanding the nervous system's embodied nature, evolved to control a body for sensory-motor functions, is crucial for comprehending higher cognitive functions like consciousness. She notes all agreed-upon intelligent, conscious agents are embodied.

Brunton envisions using embodied animal models to understand nervous and musculoskeletal system interactions, especially for injuries like spinal cord damage. Such models could provide insights into long-term adaptations and help design better therapeutics or rehabilitation strategies.