2023
DOI: 10.1080/14686996.2023.2183712
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Biological function simulation in neuromorphic devices: from synapse and neuron to behavior

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Cited by 23 publications
(5 citation statements)
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“…Neuromorphic devices with sensory functions have the capacity to process information from the perceived surroundings at ultralow power by emulating neural learning functionality. , On top of ionic sensitivity, materials for neuromorphic applications must, therefore, be able to satisfy some key functionalities. Synaptic plasticity is the ability of a synapse to modulate its weight, which in turn determines the efficiency with which adjacent neurons are able to propagate information among each other, represented here as the device resistance . Plasticity can be differentiated into long-term and short-term forms (LTP and STP, respectively).…”
Section: Results and Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Neuromorphic devices with sensory functions have the capacity to process information from the perceived surroundings at ultralow power by emulating neural learning functionality. , On top of ionic sensitivity, materials for neuromorphic applications must, therefore, be able to satisfy some key functionalities. Synaptic plasticity is the ability of a synapse to modulate its weight, which in turn determines the efficiency with which adjacent neurons are able to propagate information among each other, represented here as the device resistance . Plasticity can be differentiated into long-term and short-term forms (LTP and STP, respectively).…”
Section: Results and Discussionmentioning
confidence: 99%
“…Synaptic plasticity is the ability of a synapse to modulate its weight, which in turn determines the efficiency with which adjacent neurons are able to propagate information among each other, represented here as the device resistance. 73 Plasticity can be differentiated into long-term and short-term forms (LTP and STP, respectively). LTP denotes a nonvolatile state equivalent and in other words, underpins the formation of long-term memory in learning functions–hours to years.…”
Section: Results and Discussionmentioning
confidence: 99%
“…7–18 As the foundation of neuromorphic computing, artificial neurons play an essential role in implementing the computation. 19–23 An artificial neuron consists of multiple inputs and one output. When the inputs are integrated and reach a threshold, the neuron will be fired, and the output is transmitted to the next level of neurons in the form of spikes.…”
Section: Introductionmentioning
confidence: 99%
“…In addition, the increase in the synaptic weight is close to linear, which enables access to the precise states and improvement of the computational accuracy in networks. 42,43 Notably, the nonlinear depression process is mainly due to the fast backflow of N(CH 3 ) 4 + counterions driven by the ion gradients and the "internal" electrical field. Importantly, this continuous potentiation and depression process could be reversibly switched without obvious degradation of performance (Figure 3b; here we record only seven cycles).…”
mentioning
confidence: 99%