The honey bee swarm carried out its best-of-N nest site computations by operating more than a dozen different information processing loops in parallel and by recruiting more resources to provide greater precision in l...
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The honey bee swarm carried out its best-of-N nest site computations by operating more than a dozen different information processing loops in parallel and by recruiting more resources to provide greater precision in loops evaluating the better quality sites. The positive feedback effects of recurrent recruitment by means of waggle dance signalling amplified the utilisation of the swarm's energy, memory and carrier resources. The relatively strong negative feedback effects of various attenuation mechanisms tended to reduce resource use and therefore counter-balanced amplification for long enough for a meaningful nest site survey and quorum decision to be made. Some information processing mechanisms such as exploration tendency, waggle dance signalling, site non-specific attenuation, noise reduction, independent site evaluation, energy efficient coding, mixed precision processing, self-organising computation and quorum decision making were found to profoundly influence the efficiency of resource use. Significant insights were also gained into extended cognition, dark data processing, information quality and resource leakage. Finally, the energy cost of acquiring and processing sensory information was estimated.
Sensory neurons code information about stimuli in their sequence of action potentials (spikes). Intuitively, the spikes should represent stimuli with high fidelity. However, generating and propagating spikes is a meta...
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Sensory neurons code information about stimuli in their sequence of action potentials (spikes). Intuitively, the spikes should represent stimuli with high fidelity. However, generating and propagating spikes is a metabolically expensive process. It is therefore likely that neural codes have been selected to balance energy expenditure against encoding error. Our recently proposed optimal, energy-constrained neural coder (Jones et al. Frontiers in Computational Neuroscience, 9, 61 2015) postulates that neurons time spikes to minimize the trade-off between stimulus reconstruction error and expended energy by adjusting the spike threshold using a simple dynamic threshold. Here, we show that this proposed coding scheme is related to existing coding schemes, such as rate and temporal codes. We derive an instantaneous rate coder and show that the spike-rate depends on the signal and its derivative. In the limit of high spike rates the spike train maximizes fidelity given an energy constraint (average spike-rate), and the predicted interspike intervals are identical to those generated by our existing optimal coding neuron. The instantaneous rate coder is shown to closely match the spike-rates recorded from P-type primary afferents in weakly electric fish. In particular, the coder is a predictor of the peristimulus time histogram (PSTH). When tested against in vitro cortical pyramidal neuron recordings, the instantaneous spike-rate approximates DC step inputs, matching both the average spike-rate and the time-to-first-spike (a simple temporal code). Overall, the instantaneous rate coder relates optimal, energy-constrained encoding to the concepts of rate-coding and temporal-coding, suggesting a possible unifying principle of neural encoding of sensory signals.
We devise new coding methods to minimize Phase Change Memory write energy. Our method minimizes the energy required for memory rewrites by utilizing the differences between PCM read, set, and reset energies. We develo...
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ISBN:
(纸本)9781450311991
We devise new coding methods to minimize Phase Change Memory write energy. Our method minimizes the energy required for memory rewrites by utilizing the differences between PCM read, set, and reset energies. We develop an integer linear programming method and employ dynamic programming to produce codes for uniformly distributed data. We also introduce data-aware coding schemes to efficiently address the energy minimization problem for stochastic data. Our evaluations show that the proposed methods result in up to 32% and 44% reduction in memory energy consumption for uniform and stochastic data respectively.
Suprathreshold Stochastic Resonance (SSR) is a recently discovered form of stochastic resonance that occurs in populations of neuron-like devices. A key feature of SSR is that all devices in the population possess ide...
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ISBN:
(纸本)0819453935
Suprathreshold Stochastic Resonance (SSR) is a recently discovered form of stochastic resonance that occurs in populations of neuron-like devices. A key feature of SSR is that all devices in the population possess identical threshold nonlinearities. It has previously been shown that information transmission through such a system is optimized by nonzero internal noise. It is also clear that it is desirable for the brain to transfer information in an energyefficient manner. In this paper we discuss the energyefficient maximization of information transmission for the case of variable thresholds and constraints imposed on the energy available to the system, as well as minimization of energy for the case of a fixed information rate. We aim to demonstrate that under certain conditions, the SSR configuration of all devices having identical thresholds is optimal. The novel feature of this work is that optimization is performed by finding the optimal threshold settings for the population of devices, which is equivalent to solving a noisy optimal quantization problem.
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