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2020
In the previous chapter, we saw how a perceptron operates and how a simple learning algorithm can be implemented. However, the perceptron has some serious limitations, which will motivate us to formulate a more robust artificial neuron, called the sigmoid neuron.
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In the previous chapter, we saw how a perceptron operates and how a simple learning algorithm can be implemented. However, the perceptron has some serious limitations, which will motivate us to formulate a more robust artificial neuron, called the sigmoid neuron.
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An Adiabatic Regenerative Capacitive Artificial Neuron
2021 IEEE International Symposium on Circuits and Systems (ISCAS), 2021In recent years, RRAM technology has been actively developed as a means of reducing power dissipation and area in a host of circuits, most notably artificial neuron synapses. However, further reduction in energy consumption may be possible by transitioning to capacitive synapses and combining them with adiabatic technique.
Sachin Maheshwari +3 more
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Artificial Neuron Hardware IP Verification
2020 IEEE 29th Asian Test Symposium (ATS), 2020Implementing artificial neural network (ANN) on hardware, e.g. as hard IP in SoC or soft IP in FPGA, for acceleration is one of the common methods to obtain high performance. Being the heart of ANN, the performance of artificial neuron directly determines the performance of the entire system. Artificial neuron’s algorithm comprises of two portions, sum-
Teo Sje Yin, Soon Ee Ong
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Magnetoresistive Structures for Artificial Neurons
Automation and Remote Control, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Dynamics of stochastic artificial neurons
Neurocomputing, 2001zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Artificial vision through neuronal stimulation
Neuroscience Letters, 2012The term visual prosthesis refers to any device capable of eliciting visual percepts in an individual through electrical stimulation of any part of the visual system.Blindness can be due to eye pathology or due to damage of the lateral geniculate or visual cortex.
Rodrigo A Brant, Fernandes +3 more
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Artificial Electrical Morris–Lecar Neuron
IEEE Transactions on Neural Networks and Learning Systems, 2015In this paper, an experimental electronic neuron based on a complete Morris-Lecar model is presented, which is able to become an experimental unit tool to study collective association of coupled neurons. The circuit design is given according to the ionic currents of this model.
Rachid Behdad +4 more
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An Artificial Synapse for Interfacing to Biological Neurons
2006 IEEE International Symposium on Circuits and Systems, 2006This work details CMOS, bio-inspired circuits that are used to link an artificial neuron and a living neuron and between two living neurons. We read an intracellular signal from a living neuron, use an integrate-and-fire neuron as a simple processing element to detect the spikes, and an artificial synapse to send outputs to a living neuron.
Christal Gordon +4 more
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Artificial neuron models for hydrological modeling
2007 International Joint Conference on Neural Networks, 2007Artificial neural networks (ANNs) have been successfully employed for hydrological modeling in the last two decades or so. Most ANN hydrologic models use the McCulloch and Pitts' artificial neuron (MPAN) as the building block of the ANN models. This paper presents the results of a study employing an artificial neuron called Generalized Neuron (GN ...
Seema Narain, Ashu Jain
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Square Wave Artificial Neuron (SWAN)
2014 Fourth International Conference on Digital Information and Communication Technology and its Applications (DICTAP), 2014We developed a square wave based artificial neuron to take advantage of inexpensive and readily available Field Programmable Gate Array technologies. While conventional neurons require computationally intensive floating point arithmetic to determine the output, our artificial neuron converts inputs into square waves and the time that these waves ...
David Ellis, Kosuke Imamura
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