Neuromorphic Silicon Neurons and Large-Scale Neural Networks: Challenges and Opportunities

Poon, Chi‐Sang; Zhou, Kun

doi:10.3389/fnins.2011.00108

Cited by 182 publications

(114 citation statements)

References 31 publications

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“…The transistor count reflects the relative complexity of our biophysically grounded iono-neuromorphic model compared to phenomenological models. Additional transistors were also needed in our wide-dynamicrange subthreshold CMOS circuit designs, which effectively mitigated the effects of transistor mismatch and significantly improved the robustness of aVLSI implementation (44,53). The capacitors occupied the bulk of the chip area as some of them were relatively large (30 pF) in order to achieve a 1∶1 electronicto-biological time scale at nA level currents.…”

Section: Methods and Resultsmentioning

confidence: 99%

“…1A). A set of CMOS building block circuits biased in the subthreshold regime for robust iono-neuromorphic modeling [with wide input dynamic range to overcome device mismatch in subthreshold circuits (44)] are configured to emulate fast AMPA and slower NMDA channels, as described previously (53). The output currents are sent to a membrane node circuit that keeps the membrane potential V MEM near the resting potential V REST in the absence of stimulation (Fig.…”

Section: Methods and Resultsmentioning

confidence: 99%

“…Compared to conventional software-based computer modeling and simulation approaches, these neuromorphic electronic circuits have extremely small size (micro-to nanoscale) and low power requirements (μA to pA current per unit device with 0.5-5 V power supply) for large scale neural modeling and high speed simulation purposes. These capabil-ities are critical for many real-time, portable/implantable neural computing applications such as neuroprosthesis, brain-machine interface, neurorobotics, neuromimetic computation, machine learning, or neural-inspired adaptive control (44). However, most such neuromorphic models emulate the temporally asymmetric STDP characteristic by direct phenomenological curve fitting (45) instead of biophysical modeling (26).…”

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confidence: 99%

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A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity

Rachmuth

Shouval²,

Bear

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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165

148

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Current advances in neuromorphic engineering have made it possible to emulate complex neuronal ion channel and intracellular ionic dynamics in real time using highly compact and powerefficient complementary metal-oxide-semiconductor (CMOS) analog very-large-scale-integrated circuit technology. Recently, there has been growing interest in the neuromorphic emulation of the spike-timing-dependent plasticity (STDP) Hebbian learning rule by phenomenological modeling using CMOS, memristor or other analog devices. Here, we propose a CMOS circuit implementation of a biophysically grounded neuromorphic (iono-neuromorphic) model of synaptic plasticity that is capable of capturing both the spike rate-dependent plasticity (SRDP, of the Bienenstock-Cooper-Munro or BCM type) and STDP rules. The iono-neuromorphic model reproduces bidirectional synaptic changes with NMDA receptor-dependent and intracellular calcium-mediated long-term potentiation or long-term depression assuming retrograde endocannabinoid signaling as a second coincidence detector. Changes in excitatory or inhibitory synaptic weights are registered and stored in a nonvolatile and compact digital format analogous to the discrete insertion and removal of AMPA or GABA receptor channels. The versatile Hebbian synapse device is applicable to a variety of neuroprosthesis, brain-machine interface, neurorobotics, neuromimetic computation, machine learning, and neural-inspired adaptive control problems.iono-neuromorphic modeling | rate-based synaptic plasticity | silicon neuron | subthreshold microelectronics | VLSI circuit L earning and memory are emergent animal behaviors governed by activity-dependent neuronal adaptation rules in response to changing environments. A putative neuronal mechanism of learning and memory is Hebbian synaptic plasticity (1)-the adaptive modification of excitatory synaptic strength following paired activation of the pre-and postsynaptic neurons. Two classic paradigms for the induction of Hebbian synaptic plasticity in the mammalian hippocampus and neocortex are rate-based plasticity (2-4) [herein referred to as spike-rate-dependent plasticity (SRDP)] and spike-timing-dependent plasticity (STDP) (5-7). The SRDP induction protocols control presynaptic firing rate in order to vary the sign and magnitude of synaptic plasticity (8): a high-frequency (20-100 Hz) train of presynaptic pulses results in long-term potentiation (LTP) of the synaptic strength, whereas a low-frequency (1-5 Hz) train results in long-term depression (LTD). These protocols are consistent with the theoretical learning rule (BCM rule) proposed by Bienenstock, Cooper, and Munro (9), in which the sign and magnitude of synaptic plasticity are controlled solely by postsynaptic activity as determined by presynaptic firing rate: low postsynaptic activity weakens synaptic efficacy and high postsynaptic activity strengthens it. By contrast, the STDP induction protocol stipulates that precise timing of preand postsynaptic activities determines the direction and strength of synaptic pl...

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Section: Methods and Resultsmentioning

confidence: 99%

Section: Methods and Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

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A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity

Rachmuth

Shouval²,

Bear

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

Self Cite

165

148

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“…Biologically realistic supercomputer simulations of the brain can only simulate a small fraction of the brain cells in a small mammal at significantly reduced speed [123,124]. The massive parallelism enabled by a scalable, biologically realistic hardware implementation of the many thousands of neurons involved in the visual system can provide more quick and efficient simulations [124][125][126] which may give further insight into the visual system, while also offering potential for image processing applications.…”

Section: B the Visual Cortexmentioning

confidence: 99%

Superconducting Optoelectronic Circuits for Neuromorphic Computing

et al. 2017

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Neural networks have proven effective for solving many difficult computational problems. Implementing complex neural networks in software is very computationally expensive. To explore the limits of information processing, it will be necessary to implement new hardware platforms with large numbers of neurons, each with a large number of connections to other neurons. Here we propose a hybrid semiconductor-superconductor hardware platform for the implementation of neural networks and large-scale neuromorphic computing. The platform combines semiconducting few-photon light-emitting diodes with superconducting-nanowire single-photon detectors to behave as spiking neurons. These processing units are connected via a network of optical waveguides, and variable weights of connection can be implemented using several approaches. The use of light as a signaling mechanism overcomes fanout and parasitic constraints on electrical signals while simultaneously introducing physical degrees of freedom which can be employed for computation. The use of supercurrents achieves the low power density necessary to scale to systems with enormous entropy. The proposed processing units can operate at speeds of at least 20 MHz with fully asynchronous activity, light-speed-limited latency, and power densities on the order of 1 mW/cm 2 for neurons with 700 connections operating at full speed at 2 K. The processing units achieve an energy efficiency of ≈ 20 aJ per synapse event. By leveraging multilayer photonics with deposited waveguides and superconductors with feature sizes > 100 nm, this approach could scale to systems with massive interconnectivity and complexity for advanced computing as well as explorations of information processing capacity in systems with an enormous number of information-bearing microstates.

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“…Было разработано множе-ство различных схемотехнических реализаций нейронов, отличающихся по степени детализации и биологической правдоподобности [1, [7][8][9][10], по количеству воспроизводимых динамических режимов [11][12][13][14]. Также исследовалась и коллективная динамика аппаратных нейронных моде-лей [15][16][17][18][19][20][21].…”

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Аппаратная Реализация Нейроподобного Генератора С Импульсной И Пачечной Динамикой На Основе Системы Фазовой Синхронизации

Мищенко¹,

Большаков²,

Матросов³

2017

Письма В Журнал Технической Физики

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Предложена аппаратная модель нейрона на основе системы фазовой синхронизации с полосовым фильтром в цепи управления. Описаны основные конструктивные элементы системы. Экспериментально продемонстрировано существование различных динамических режимов, качественно похожих на импульсную и пачечную динамику нейронов. DOI: 10.21883/PJTF.2017.13.44806.16737

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Neuromorphic Silicon Neurons and Large-Scale Neural Networks: Challenges and Opportunities

Cited by 182 publications

References 31 publications

A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity

A biophysically-based neuromorphic model of spike rate- and timing-dependent plasticity

Superconducting Optoelectronic Circuits for Neuromorphic Computing

Аппаратная Реализация Нейроподобного Генератора С Импульсной И Пачечной Динамикой На Основе Системы Фазовой Синхронизации

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