We report a generic smartphone app for quantitative annotation of complex images. The app 1 is simple enough to be used by children, and annotation tasks are distributed across app 2 users, contributing to efficient annotation. We demonstrate its flexibility and speed by 3 annotating >30,000 images, including features of rice root growth and structure, stem cell 4 aggregate morphology, and complex worm (C. elegans) postures, for which we show that the 5 speed of annotation is >130-fold faster than state-of-the-art techniques with similar accuracy. 6 7 8 9
The behaviour of biological neural networks is simulated using two different models of the elements of the network. For the first model, individual neurons represent the simplest conventional model of a biological neuron, while in the second model they are sufficiently sophisticated (without being much more complex) to be capable of generating deterministically chaotic outputs. The behaviour of networks using this second model is much more realistic, as is confirmed by comparison with actual electroencephalograms (EEGs) recorded under clinical conditions. NEURONAL MODELSModels of the Structure of Biological Neurons A biological neuron can be modelled as a simple threshold element [ 2 ] , which produces an output when its activity (membrane potential) exceeds a threshold value, say T ( fig. 1). The outputs z; of other neurons are multiplied by weights w;, corresponding to the amount of neurotransmitter released at the synapses. The activity of the neuron is a spatic-temporal summation of the weighted inputs, with the temporal integration being achieved by assuming the activity decays exponentially. A periodic input to a neuronal model of this form generates a periodic sequence of weighted inputs which, depending upon their amplitude and frequency, produce either a null or a periodic output. n
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