Fig 5 - uploaded by Jarrod Schiffbauer
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Electric potential with contour lines at t = 2P for λ = 80 μm, V 0 = 20, ε = 10 −2 , H ˜ = 15 μm, w ˜ = 2 μm, f = 1, M = 1, and D r 1 = D r 2 = 1 for the case with selective electrodes. The high ion concentration regions also show high conductivity (low resistance) and the low concentration regions show low conductivity (or high resistance). That is to say, the small number of equipotential lines in the high concentration region has a low magnitude electric field. The high number of equipotential lines in the low concentration reigion has a high electric field.
Source publication
COMSOL finite element modeling software is used to simulate 2D traveling-wave electrophoresis for microfluidic separations and sample concentration. A four-phase AC potential is applied to a periodic interdigitated four-electrode array to produce a longitudinal electric wave that travels through the channel. Charged particles are carried along with...
Citations
Today software systems known as neural networks are at the basis of numerous artificial intelligence applications and are successfully implemented to translate languages, classify images, recognize diseases, and form the basis of the spur in autonomous driving. However, these algorithms require a substantial amount of computer resources and energy. The brain on the other hand, operates in a highly parallel fashion, connecting neurons via synapses, rendering it compact and highly efficient in recognizing patterns, speech, and images. Neuromorphic engineering takes advantage of the efficiency of the brain by mimicking and implementing essential concepts such as neurons and synapses in hardware. In this chapter we review the development of organic neuromorphic devices. We highlight efforts to mimic essential brain functions, such as spiking phenomena, spatiotemporal processing, homeostasis, and functional connectivity and demonstrate related applications. Next, we review important metrics for implementing low-power and reliable neuromorphic computing, such as state retention and conductance tuning. Finally, we give an outlook on future directions and potential applications, with a particular focus on interfacing with biological environments.
Global oscillations in the brain synchronize neural populations and give rise to dynamic binding between different regions. This functional connectivity reconfigures as needed the architecture of the neural network, thereby transcending the limitations of its hardwired structure. Despite the fact that it underlies the versatility of biological computational systems, this concept is not captured in current neuromorphic device architectures. A functional connectivity in an array of organic neuromorphic devices connected through an electrolyte is demonstrated in this work. It is shown that the output of these devices is synchronized by a global oscillatory input despite the fact that individual inputs are stochastic and independent. This temporal coupling is induced at a specific phase of the global oscillation in a way that is reminiscent of phase locking of neurons to brain oscillations. This demonstration provides a pathway toward new neuromorphic architectural paradigms where dynamic binding transcends the limitations of structural connectivity, and could enable architectural concepts of hierarchical information flow.
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