Index
Concepts
The ideas both fields lay claim to. Each one is shown from both sides — the bar underneath says how the topics split.
- Energy efficiency 10 topics Work done per joule spent. The brain runs on roughly 20 watts, which is the benchmark that makes most computing hardware look profligate — and a hard ceiling for anything implanted.
- Impedance 9 topics What a circuit presents to a signal at a given frequency — the quantity that decides whether an electrode can hear a neuron, or a coil can reach one.
- Noise 8 topics The floor set by physics beneath every measurement and every signal — the thing instrument designers fight, and that nervous systems seem in places to exploit.
- Population coding 7 topics Information carried across many noisy units rather than any single reliable one — which turns a wiring problem into a measurement problem, and sets the specification for most neural instruments.
- Feedback 6 topics Routing a system's output back to its input so it corrects itself — invented for amplifiers in 1927, and since found running almost everywhere in the nervous system.
- Adaptive control 4 topics Adjusting a controller's own parameters as the plant changes underneath it — necessary when the thing being controlled is alive, and therefore never twice the same.
- Sparse coding 4 topics Representing information with few active elements at a time, rather than many slightly-active ones — cheap in energy and bandwidth, and apparently the strategy sensory systems settled on.
- Hierarchy 2 topics Building complicated selectivity out of layers of simple selectivity — how the visual system turns spots of light into objects, and why where a signal enters a structure matters.
- Learning rules 2 topics How a system changes its own parameters from experience — the question neuroscience asks about synapses and control engineering asks about adaptive controllers.