Publications by authors named "D M Wolpert"

Maximizing the amount of work harvested from an environment is important for a wide variety of biological and technological processes, from energy-harvesting processes such as photosynthesis to energy storage systems such as fuels and batteries. Here, we consider the maximization of free energy-and by extension, the maximum extractable work-that can be gained by a classical or quantum system that undergoes driving by its environment. We consider how the free energy gain depends on the initial state of the system while also accounting for the cost of preparing the system.

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Article Synopsis
  • The relationship between thermodynamics and computation has fascinated scientists since the 19th century, but its significance has grown due to the rising energy costs of digital devices.
  • Real-world computers, including biological ones like brains, operate under various physical constraints, which impact their thermodynamic dynamics and efficiency.
  • The emerging field of stochastic thermodynamics offers new analytical techniques to explore how these constraints affect the fundamental thermodynamic properties of systems performing computation.
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It has been argued that the historical nature of evolution makes it a highly path-dependent process. Under this view, the outcome of evolutionary dynamics could have resulted in organisms with different forms and functions. At the same time, there is ample evidence that convergence and constraints strongly limit the domain of the potential design principles that evolution can achieve.

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In many complex systems, whether biological or artificial, the thermodynamic costs of communication among their components are large. These systems also tend to split information transmitted between any two components across multiple channels. A common hypothesis is that such inverse multiplexing strategies reduce total thermodynamic costs.

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Neurobiological investigations of perceptual decision-making have furnished the first glimpse of a flexible cognitive process at the level of single neurons. Neurons in the parietal and prefrontal cortex are thought to represent the accumulation of noisy evidence, acquired over time, leading to a decision. Neural recordings averaged over many decisions have provided support for the deterministic rise in activity to a termination bound.

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