Journal ArticleDOI
Analog versus digital: extrapolating from electronics to neurobiology
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TLDR
The results suggest that it is likely that the brain computes in a hybrid fashion and that an underappreciated and important reason for the efficiency of the human brain, which consumes only 12 W, is the hybrid and distributed nature of its architecture.Abstract:
We review the pros and cons of analog and digital computation. We propose that computation that is most efficient in its use of resources is neither analog computation nor digital computation but, rather, a mixture of the two forms. For maximum efficiency, the information and information-processing resources of the hybrid form must be distributed over many wires, with an optimal signal-to-noise ratio per wire. Our results suggest that it is likely that the brain computes in a hybrid fashion and that an underappreciated and important reason for the efficiency of the human brain, which consumes only 12 W, is the hybrid and distributed nature of its architecture.read more
Citations
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Journal ArticleDOI
An Energy Budget for Signaling in the Grey Matter of the Brain
David Attwell,Simon B. Laughlin +1 more
TL;DR: The estimates of energy usage predict the use of distributed codes, with ≤15% of neurons simultaneously active, to reduce energy consumption and allow greater computing power from a fixed number of neurons.
Journal ArticleDOI
Noise in the nervous system.
TL;DR: How noise affects neuronal networks and the principles the nervous system applies to counter detrimental effects of noise are highlighted, and noise's potential benefits are discussed.
Journal ArticleDOI
Neuromorphic Silicon Neuron Circuits
Giacomo Indiveri,Bernabe Linares-Barranco,Tara Julia Hamilton,André van Schaik,Ralph Etienne-Cummings,Tobi Delbruck,Shih-Chii Liu,Piotr Dudek,Philipp Hafliger,Sylvie Renaud,Johannes Schemmel,Gert Cauwenberghs,John V. Arthur,Kai Hynna,Fopefolu Folowosele,Sylvain Saïghi,Teresa Serrano-Gotarredona,Jayawan H B Wijekoon,Yingxue Wang,Kwabena Boahen +19 more
TL;DR: The most common building blocks and techniques used to implement these circuits, and an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models.
Journal ArticleDOI
Enzyme-Free Nucleic Acid Logic Circuits
TL;DR: The design and experimental implementation of DNA-based digital logic circuits using single-stranded nucleic acids as inputs and outputs are reported, suggesting applications in biotechnology and bioengineering.
Journal ArticleDOI
The metabolic cost of neural information
TL;DR: Biophysical measurements from cells in the blowfly retina yield estimates of the energy required to generate graded (analog) electrical signals that transmit known amounts of information, which promotes the distribution of information among multiple pathways.
References
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Book
Elements of information theory
Thomas M. Cover,Joy A. Thomas +1 more
TL;DR: The author examines the role of entropy, inequality, and randomness in the design of codes and the construction of codes in the rapidly changing environment.
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The synaptic organization of the brain
TL;DR: Introduction to synaptic circuits, Gordon M.Shepherd and Christof Koch membrane properties and neurotransmitter actions, David A.Brown and Anthony M.Brown.
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Low-power CMOS digital design
TL;DR: In this paper, techniques for low power operation are presented which use the lowest possible supply voltage coupled with architectural, logic style, circuit, and technology optimizations to reduce power consumption in CMOS digital circuits while maintaining computational throughput.
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Analog VLSI and Neural Systems
TL;DR: This chapter discusses a simple circuit that can generate a sinusoidal response and calls this circuit the second-order section, which can be used to generate any response that can be represented by two poles in the complex plane, where the two poles have both real and imaginary parts.
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