19,147 research outputs found
Optimal control of transitions between nonequilibrium steady states
Biological systems fundamentally exist out of equilibrium in order to
preserve organized structures and processes. Many changing cellular conditions
can be represented as transitions between nonequilibrium steady states, and
organisms have an interest in optimizing such transitions. Using the
Hatano-Sasa Y-value, we extend a recently developed geometrical framework for
determining optimal protocols so that it can be applied to systems driven from
nonequilibrium steady states. We calculate and numerically verify optimal
protocols for a colloidal particle dragged through solution by a translating
optical trap with two controllable parameters. We offer experimental
predictions, specifically that optimal protocols are significantly less costly
than naive ones. Optimal protocols similar to these may ultimately point to
design principles for biological energy transduction systems and guide the
design of artificial molecular machines.Comment: Accepted for publication at PLoS ON
A bibliography /with abstracts/ on gas-lubricated bearings Interim report
Gas lubricated bearings - annotated bibliograph
Integer Echo State Networks: Hyperdimensional Reservoir Computing
We propose an approximation of Echo State Networks (ESN) that can be
efficiently implemented on digital hardware based on the mathematics of
hyperdimensional computing. The reservoir of the proposed Integer Echo State
Network (intESN) is a vector containing only n-bits integers (where n<8 is
normally sufficient for a satisfactory performance). The recurrent matrix
multiplication is replaced with an efficient cyclic shift operation. The intESN
architecture is verified with typical tasks in reservoir computing: memorizing
of a sequence of inputs; classifying time-series; learning dynamic processes.
Such an architecture results in dramatic improvements in memory footprint and
computational efficiency, with minimal performance loss.Comment: 10 pages, 10 figures, 1 tabl
Transient Information Flow in a Network of Excitatory and Inhibitory Model Neurons: Role of Noise and Signal Autocorrelation
We investigate the performance of sparsely-connected networks of
integrate-and-fire neurons for ultra-short term information processing. We
exploit the fact that the population activity of networks with balanced
excitation and inhibition can switch from an oscillatory firing regime to a
state of asynchronous irregular firing or quiescence depending on the rate of
external background spikes.
We find that in terms of information buffering the network performs best for
a moderate, non-zero, amount of noise. Analogous to the phenomenon of
stochastic resonance the performance decreases for higher and lower noise
levels. The optimal amount of noise corresponds to the transition zone between
a quiescent state and a regime of stochastic dynamics. This provides a
potential explanation on the role of non-oscillatory population activity in a
simplified model of cortical micro-circuits.Comment: 27 pages, 7 figures, to appear in J. Physiology (Paris) Vol. 9
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