Why do we need a brain?
Drawing inspiration from animal behavior and biological structure
Chapter 2 of Principles of Neural Design (2017) by Peter Sterling and Simon Laughlin opens with the question: why do we need a brain?
Introductory neuroscience courses generally start at the lowest level, with neurons, synapses, and action potentials. It’s like teaching someone how a car works by starting with spark plugs rather than how the engine makes the car move, or why you need a transmission. Instead of starting with the nuts and bolts, we begin this course as Sterling and Laughlin begin their book: by motivating the investigation into core principles that tell us what the brain is for. And if these principles are truly fundamental, they should hold true for all types of brains.
What we identify here as the brain’s purpose, especially because we are seeking principles, should apply not only to humans but as well to the nematode worm, C. elegans, and to flies. The deep purpose of the nematode’s brain of 302 neurons, the fruit fly’s brain of 105 neurons, and our own brain of 10^11 neurons (Azevedo et al., 2009) must be the same.
Much of the current AI industry likes to impose specific definitions of intelligence, like image classification and next-token prediction1. In our approach, we counter with these questions: Is that what the brain is designed to do? What problems is biology solving? Why did the brain develop anatomically distinct structures; why did we evolve ears and eyes?
Speaking of eyes, the optical properties of animal eyes are beautiful examples of evolution based on animal capabilities and environments2. An improvement to the pit eye (a), the pinhole eye (b) found in the Nautilus is still quite limited due to low retinal illuminance and low resolution. As a solution, various types of lenses (c-h) allow wider apertures and refraction. Some animals developed reflective structure to capture more light (i). And these are just single-chamber eyes! That nature has discovered mechanisms that parallel the optics in our engineered systems is incredible.
To examine behavior, we turn to simpler brains like those of C. elegans, fruit flies, and rats, but what we’ve learned is anything but simple. Some of the big breakthroughs in neuroscience relate to navigation in animals, such as the fruit fly ellipsoid body encoding head direction, and place and grid cells in rat hippocampus (we will go deeper into these topics later in the course). One of Bruno’s favorite examples is jumping spiders3. They have remarkable eyes that swivel back and forth to scan the environment. They stalk their prey, navigating in complex ways in 3D space. Jumping spider psychophysics (yes4) show that they perform object recognition. Supposedly, if you catch one and put it in front of a white computer screen and move your cursor around, it will try to pounce on it like a tiny cat.
Clearly, there is complex computation going on inside a jumping spider. Forget mammalian brains; we don’t even know how these simpler brains work! Until we do, it’s hard to make the case that we have made significant progress on discovering core principles. Surely we cannot discount what underlies these behaviors and structures in our studies of neuroscience and intelligence.
As interesting as animal behavior and biological structure are, this course is about computation, not evolutionary biology. Part of the reason the systems above are so compelling is that we can use mathematical language to describe them. This course will draw on elements of signal processing, statistics, calculus, linear algebra, attractor networks, and manifold representations, just to name a few. While mapping to math is useful, we should, above all, be open to embracing the complexity of biology. Bruno points out that maybe in studying these principles, we will realize we lack the appropriate language, and even develop new mathematical tools.
We’ve talked about animal behavior, but action is largely driven by sensory input. And it is not enough to just sense: signals need to be encoded in ways that are useful for action. For our first technical topic, the next lecture will dive into sensory coding by looking at the computations involved in phototransduction.
These definitions of intelligence often just happen to match the capabilities of current state-of-the-art models on arbitrary benchmarks that drive products and profits. What a coincidence!
The Evolution of Eyes, Land & Fernald 1992; Animal Eyes, Land & Nilsson 2012.
To the point where we have considered making Jumping Spider Fan Club t-shirts for the lab.
Interesting read! Thanks!
I like Humberto Maturana and Francisco Varela’s view: brains (and, more generally, nervous systems) have evolved as devices whose job it is to maintain certain internal relations between the organism's sensors and effectors.