A Memristive Nanoparticle/Organic Hybrid Synapstor for Neuroinspired Computing

Abstract

Grant et al, [2] Markram et al, [3] Bi, and Poo [4] observed STDP in biological synapses. The principle of STDP is to tune the response of a synapse as a function of the pre-and post-synaptic neurons spiking activity - Figure 1a. Depending on the correlation or anti-correlation of the spiking events of the pre-and postsynaptic neurons, the synapse's weight is reinforced or depressed, respectively. The so-called "STDP function" or "STDP learning window" is defined as the relationship between the change in the synaptic weight or synaptic response versus the relative timing between the pre-and postsynaptic spikes (Figure 1b). [5] The implementation of STDP with nanodevices is strongly driven by a bio-inspired approach to enable local and unsupervised learning capability in large artificial SNN in an efficient and robust way. To this end, it is envisioned to use the nanodevices as synapses and to realize the neuron functionality with complementary metal oxide semiconductor (CMOS) technology. This approach is supported by the fact that the limiting integration factor is really the synapse density, as realistic applications could require as much as 10 3 to 10 4 synapses per neuron. Snider [6] proposed an implementation of STDP with nanodevices, where the synapses are realized with a crossbar of memristors [7] and the neurons with a "time-multiplexing CMOS" circuit. Using these two elements, it should be possible to reproduce exactly the "STDP learning window" of a biological synapse (Figure 1b). Linares-Barranco et al. simulated the implementation of the STDP function with memristive nanodevices. [8,9] Using a specific shape of the spikes and the nonlinearity of the memristor, they showed that the conductivity of the memristor can be tuned depending on the precise timing between the postsynaptic and presynaptic spikes. More interestingly, they showed that the shape of the STDP learning window can be tuned by changing the shape of the spike (Figure 1c). We have to emphasize that our aim is to be inspired by the behavior of a biological synapse for neural computation applications (and not to build a model system of the synapse), thus the important point is to reproduce qualitatively the STDP behavior, even if the spike signals applied to the synapstor are not close to the real biological spike. A Memristive Nanoparticle/Organic Hybrid Synapstor for Neuroinspired ComputingA large effort is devoted to the research of new computing paradigms associated with innovative nanotechnologies that should complement and/or propose alternative solutions to the classical Von Neumann/CMOS (complementary metal oxide semiconductor) association. Among various propositions, spiking neural network (SNN) seems a valid candidate. i) In terms of functions, SNN using relative spike timing for information coding are deemed to be the most effective at taking inspiration from the brain to allow fast and efficient processing of information for complex tasks in recognition or classification. ii) In terms of technology, SNN may be able to benefit the...

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Memristive behavior in a junctionless flash memory cell

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Mimicking of pulse shape-dependent learning rules with a quantum dot memristor

“…Similar pulses were used to emulate asymmetric Hebbian learning with other memristor realizations. 26,30 Different pulse shapes are applied to investigate the emulation of input-dependent learning. Note that the pulses to mimic symmetric learning rules are symmetric in time, thus charging and discharging the QDs should not depend on the temporal order of the pulses, but on the absolute value of the time difference.…”

“…[19][20][21] For example, in hippocampal neurons, potentiation (increase) of the synaptic strength is observed when the post-follows the presynaptic pulse, while depression (decrease) occurs when the pre-follows the postsynaptic pulse (asymmetric Hebbian learning). 16 This functionality can be successfully emulated with memristors 4,[22][23][24][25][26] and, empirically, it is described with exponential functions. 14,27 Depending on the synapse type (excitatory or inhibitory), potentiation and depression can also occur for a reversed order of the pre-and postsynaptic pulses (asymmetric anti-Hebbian learning).…”