A carbon nanotube implementation of temporal and spatial dendritic computations

Parker, Alice C.; Joshi, Jaya; Hsu, Chih-Chieh; Singh, Nihal

doi:10.1109/mwscas.2008.4616925

Cited by 31 publications

(14 citation statements)

References 27 publications

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“…We use circuits built in Parker's BioRC group (e.g. [13], [14], [15]) because they are designed to expand for greater control and to incorporate additional mechanisms.…”

Section: Background and Our Approachmentioning

confidence: 99%

Modeling intrinsic ion-channel and synaptic variability in a cortical neuromorphic circuit

Mahvash

Parker

2011

2011 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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In this paper, the design of a neuromorphic cortical neuron with synaptic and ion-channel variability is presented and and simulated using carbon nanotube circuit elements. Since the variability could be because of noise or chaos, in this paper, both possible sources of variability are considered by embedding either Gaussian noise or a chaotic signal into the synaptic circuit and the axon circuit and observing the results. The paper also presents a chaotic signal generator using carbon nanotube transistors that could be embedded in the electronic neural circuit. The circuit uses a chaotic piecewise linear one-dimensional map implemented by switched-current circuits. The design was simulated using carbon nanotube SPICE models. Spontaneous firing of neurons due to intrinsic variability was demonstrated.

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“…We use circuits built in Parker's BioRC group (e.g. [13], [14], [15]) because they are designed to expand for greater control and to incorporate additional mechanisms.…”

Section: Background and Our Approachmentioning

confidence: 99%

Modeling intrinsic ion-channel and synaptic variability in a cortical neuromorphic circuit

Mahvash

Parker

2011

2011 IEEE Biomedical Circuits and Systems Conference (BioCAS)

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“…Our basic model for a cortical neuron, shown in Figure 1, consists of three types of sub-modules: the excitatory ( [3], [4]) and inhibitory synapses, the dendritic arbor [4] and the axon hillock. Circuit models for the dendrites and axon themselves are not shown in this basic model.…”

Section: The Carbon Nanotube Neuron Circuitmentioning

confidence: 99%

“…This synapse circuit evolved from an earlier excitatory synapse [4]. Synapse behavior is controlled by voltages on the gates of the transistors, acting as control knobs.…”

Section: A the Inhibitory Synapse Circuitmentioning

confidence: 99%

“…The adder circuit [29] has been shown previously [4]. Figure 4 shows a block diagram of the dendritic arbor portion.…”

Section: B the Dendritic Arbormentioning

confidence: 99%

“…We demonstrate the operation of this neuron with SPICE simulations. Carbon nanotube excitatory synapse circuits and their effects on dendritic computations have previously been reported [3], [4]. The focus here is on the inhibitory synapse and its effect on neural firing, along with the Support for this research has been provided by the Viterbi School of Engineering at the University of Southern California and NSF Grant 0726815 dendritic computations that can be implemented in our neuron.…”

Section: Introductionmentioning

confidence: 99%

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A carbon nanotube cortical neuron with excitatory and inhibitory dendritic computations

Joshi

Hsu

Parker

et al. 2009

2009 IEEE/NIH Life Science Systems and Applications Workshop

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A cortical neuron with carbon nanotube circuit elements that performs nonlinear dendritic computations with excitatory and inhibitory post-synaptic potentials is presented. An inhibitory synapse with controllable parameters that implement plasticity is described. The circuit design was simulated using carbon nanotube spice models, showing that the neuron fires as long as the inhibitory post-synaptic potential is weak or absent. Strong inhibitory potentials prevent the neuron from firing.

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