Optimizing stimulus energy for cochlear implants with a machine learning model of the auditory nerve

Algorithms and the Foundations of Software technolog

Optimizing stimulus energy for cochlear implants with a machine learning model of the auditory nerve

Performing simulations with a realistic biophysical auditory nerve fiber model can be very time-consuming, due to the complex nature of the calculations involved. Here, a surrogate (approximate) model of such an auditory nerve fiber model was developed using machine learning methods, to perform simulations more efficiently. Several machine learning models were compared, of which a Convolutional Neural Network showed the best performance. In fact, the Convolutional Neural Network was able to emulate the behavior of the auditory nerve fiber model with extremely high similarity ( R 2 > 0 . 99 ), tested under a wide range of experimental conditions, whilst reducing the simulation time by five orders of magnitude. In addition, a method for randomly generating charge-balanced waveforms using hyperplane projection is introduced. In the second part of this paper, the Convolutional Neural Network surrogate model was used by an Evolutionary Algorithm to optimize the shape of the stimulus waveform in terms of energy efficiency. The resulting waveforms resemble a positive Gaussian-like peak, preceded by an elongated negative phase. When comparing the energy of the waveforms generated by the Evolutionary Algorithm with the commonly used square wave, energy decreases of 8%-45% were observed for differ-ent pulse durations. These results were validated with the original auditory nerve fiber model, which demonstrates that the proposed surrogate model can be used as its accurate and efficient replacement.(c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )</p

Mineralized and unmineralized calderas in Spain; Part I, evolution of the Los Frailes Caldera

The Cabo de Gata volcanic field of southeastern Spain contains several recently-recognized calderas. Some of the calderas are mineralized with epithermal gold, alunite, and base metal deposits, and others are barren, and yet they formed under generally similar conditions. Comparison of the magmatic, geochemical, and physical evolution of the Los Frailes, Rodalquilar, and Lomilla calderas provides insight into the processes of caldera evolution that led to precious-metal mineralization. The Los Frailes caldera formed at 14.4 Ma and is the oldest caldera. It formed in response to multiple eruptions of hornblende dacite magma. Following each eruption, the area collapsed and the caldera was invaded by the sea. Dacite domes fill the lower part of the caldera. Pyroxene andesites were erupted through the solidified core of the caldera and were probably initially responsible for magma generation. The Los Frailes caldera did not evolve to rhyolites nor was it subjected to the amount of structural development that the younger, mineralized Rodalquilar and Lomilla calderas were.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46040/1/126_2004_Article_BF00205246.pd