Principles of Neural Design
Peter Sterling and Simon Laughlin
Abstract
The human brain is far smarter than a supercomputer but requires 100,000-fold less energy and space. Such efficient information processing is governed by ten principles of design. These apply to the whole brain across the full range of spatial and temporal scales, and to the brains of all species. The principles are: compute with chemistry; compute directly with analog primitives; combine analog and pulsatile processing; code sparsely; send only what information is needed for a particular task; transmit information at the lowest acceptable rate; minimize wire; make neural components irreducibl ... More
The human brain is far smarter than a supercomputer but requires 100,000-fold less energy and space. Such efficient information processing is governed by ten principles of design. These apply to the whole brain across the full range of spatial and temporal scales, and to the brains of all species. The principles are: compute with chemistry; compute directly with analog primitives; combine analog and pulsatile processing; code sparsely; send only what information is needed for a particular task; transmit information at the lowest acceptable rate; minimize wire; make neural components irreducibly small; complicate; adapt and match, learn and forget. This approach does not explain the “hows” of brain design but does explain many of the “whys”. For example, it explains why certain signals are sent via hormones and others via nerves; why neural wires are mostly thin with only a few thick; why synapses differ in size, number and reliability according to the circuit that they serve; why every neuron type has a characteristic shape; why the cerebral cortex is parceled into different areas and different layers; why learning couples to forgetting. “Whys” explained on nearly every page. Given the explanatory power of ten principles, we should search for more.
Keywords:
Reverse engineering,
Brain’s core tasks,
Efficiency in space and energy,
Information processing,
Chemical processing,
Electrical processing,
Neuron,
Neural circuit,
Learning as design,
Design of learning
Bibliographic Information
| Print publication date: 2015 |
Print ISBN-13: 9780262028707 |
| Published to MIT Press Scholarship Online: September 2016 |
DOI:10.7551/mitpress/9780262028707.001.0001 |
