2,659 research outputs found
Spiking neural network model of reinforcement learning in the honeybee implemented on the GPU
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Enhancement of synchronization in a hybrid neural circuit by spike timing dependent plasticity
Synchronization of neural activity is fundamental for many functions of the brain. We demonstrate that spike-timing dependent plasticity (STDP) enhances synchronization (entrainment) in a hybrid circuit composed of a spike generator, a dynamic clamp emulating an excitatory plastic synapse, and a chemically isolated neuron from the Aplysia abdominal ganglion. Fixed-phase entrainment of the Aplysia neuron to the spike generator is possible for a much wider range of frequency ratios and is more precise and more robust with the plastic synapse than with a nonplastic synapse of comparable strength. Further analysis in a computational model of HodgkinHuxley-type neurons reveals the mechanism behind this significant enhancement in synchronization. The experimentally observed STDP plasticity curve appears to be designed to adjust synaptic strength to a value suitable for stable entrainment of the postsynaptic neuron. One functional role of STDP might therefore be to facilitate synchronization or entrainment of nonidentical neurons
(Quantum) Space-Time as a Statistical Geometry of Lumps in Random Networks
In the following we undertake to describe how macroscopic space-time (or
rather, a microscopic protoform of it) is supposed to emerge as a
superstructure of a web of lumps in a stochastic discrete network structure. As
in preceding work (mentioned below), our analysis is based on the working
philosophy that both physics and the corresponding mathematics have to be
genuinely discrete on the primordial (Planck scale) level. This strategy is
concretely implemented in the form of \tit{cellular networks} and \tit{random
graphs}. One of our main themes is the development of the concept of
\tit{physical (proto)points} or \tit{lumps} as densely entangled subcomplexes
of the network and their respective web, establishing something like
\tit{(proto)causality}. It may perhaps be said that certain parts of our
programme are realisations of some early ideas of Menger and more recent ones
sketched by Smolin a couple of years ago. We briefly indicate how this
\tit{two-story-concept} of \tit{quantum} space-time can be used to encode the
(at least in our view) existing non-local aspects of quantum theory without
violating macroscopic space-time causality.Comment: 35 pages, Latex, under consideration by CQ
Evaluating megaprojects: from the âiron triangleâ to network mapping
Evaluation literature has paid relatively little attention to the specific needs of evaluating large, complex industrial and infrastructure projects, often called âmegaprojectsâ. The abundant megaproject governance literature, in turn, has largely focused on the so-called âmegaproject pathologiesâ, i.e. the chronic budget overruns, and failure of such projects to keep to timetables and deliver the expected social and economic benefits. This article draws on these two strands of literature, identifies shortcomings, and suggests potential pathways towards an improved evaluation of megaprojects. To counterbalance the current overemphasis on relatively narrowly defined accountability as the main function of megaproject evaluation, and the narrow definition of project success in megaproject evaluation, the article argues that conceptualizing megaprojects as dynamic and evolving networks would provide a useful basis for the design of an evaluation approach better able to promote learning and to address the socio economic aspects of megaprojects. A modified version of ânetwork mappingâ is suggested as a possible framework for megaproject evaluation, with the exploration of the multiple accountability relationships as a central evaluation task, designed to reconcile learning and accountability as the central evaluation functions. The article highlights the role of evaluation as an âemergentâ property of spontaneous megaproject âgoverningâ, and explores the challenges that this poses to the role of the evaluator
StdpC: a modern dynamic clamp
With the advancement of computer technology many novel uses of dynamic clamp have become possible. We have added new features to our dynamic clamp software StdpC (âSpike timing-dependent plasticity Clampâ) allowing such new applications while conserving the ease of use and installation of the popular earlier Dynclamp 2/4 package. Here, we introduce the new features of a waveform generator, freely programmable HodgkinâHuxley conductances, learning synapses, graphic data displays, and a powerful scripting mechanism and discuss examples of experiments using these features. In the first example we built and âvoltage clampedâ a conductance based model cell from a passive resistorâcapacitor (RC) circuit using the dynamic clamp software to generate the voltage-dependent currents. In the second example we coupled our new spike generator through a burst detection/burst generation mechanism in a phase-dependent way to a neuron in a central pattern generator and dissected the subtle interaction between neurons, which seems to implement an information transfer through intraburst spike patterns. In the third example, making use of the new plasticity mechanism for simulated synapses, we analyzed the effect of spike timing-dependent plasticity (STDP) on synchronization revealing considerable enhancement of the entrainment of a post-synaptic neuron by a periodic spike train. These examples illustrate that with modern dynamic clamp software like StdpC, the dynamic clamp has developed beyond the mere introduction of artificial synapses or ionic conductances into neurons to a universal research tool, which might well become a standard instrument of modern electrophysiology
SpineML and Brian 2.0 interfaces for using GPU enhanced Neuronal Networks (GeNN)
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Forecasting in the light of Big Data
Predicting the future state of a system has always been a natural motivation
for science and practical applications. Such a topic, beyond its obvious
technical and societal relevance, is also interesting from a conceptual point
of view. This owes to the fact that forecasting lends itself to two equally
radical, yet opposite methodologies. A reductionist one, based on the first
principles, and the naive inductivist one, based only on data. This latter view
has recently gained some attention in response to the availability of
unprecedented amounts of data and increasingly sophisticated algorithmic
analytic techniques. The purpose of this note is to assess critically the role
of big data in reshaping the key aspects of forecasting and in particular the
claim that bigger data leads to better predictions. Drawing on the
representative example of weather forecasts we argue that this is not generally
the case. We conclude by suggesting that a clever and context-dependent
compromise between modelling and quantitative analysis stands out as the best
forecasting strategy, as anticipated nearly a century ago by Richardson and von
Neumann
Insect-inspired visual navigation on-board an autonomous robot: real-world routes encoded in a single layer network
Insect-Inspired models of visual navigation, that operate by scanning for familiar views of the world, have been shown to be capable of robust route navigation in simulation. These familiarity-based navigation algorithms operate by training an artificial neural network (ANN) with views from a training route, so that it can then output a familiarity score for any new view. In this paper we show that such an algorithm â with all computation performed on a small low-power robot â is capable of delivering reliable direction information along real-world outdoor routes, even when scenes contain few local landmarks and have high-levels of noise (from variable lighting and terrain). Indeed, routes can be precisely recapitulated and we show that the required computation and storage does not increase with the number of training views. Thus the ANN provides a compact representation of the knowledge needed to traverse a route. In fact, rather than losing information, there are instances where the use of an ANN ameliorates the problems of sub optimal paths caused by tortuous training routes. Our results suggest the feasibility of familiarity-based navigation for long-range autonomous visual homing
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