Nano-scale resistive memories are expected to fuel dense integration of electronic synapses for large-scale neuromorphic system. To realize such a brain-inspired computing chip, a compact CMOS spiking neuron that performs in-situ learning and computing while driving a large number of resistive synapses is desired. This work presents a novel leaky integrate-and-fire neuron design which implements the dual-mode operation of current integration and synaptic drive, with a single opamp and enables in-situ learning with crossbar resistive synapses. The proposed design was implemented in a 0.18μm CMOS technology. Measurements show neuron's ability to drive a thousand resistive synapses, and demonstrate an in-situ associative learning. The neuron circuit occupies a small area of 0.01mm 2 and has an energy-efficiency of 9.3pJ/spike/synapse. 1

Abstract-A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural network parallelism and density. Previous hybrid analog CMOS-memristor approaches required extensive CMOS circuitry for training, and thus eliminated most of the density advantages gained by the adoption of memristor synapses. Further, they used different waveforms for pre and post-synaptic spikes that added undesirable circuit overhead. Here we describe a hardware architecture that can feature a large number of memristor synapses to learn real-world patterns. We present a versatile CMOS neuron that combines integrate-and-fire behavior, drives passive memristors and implements competitive learning in a compact circuit module, and enables in-situ plasticity in the memristor synapses. We demonstrate handwritten-digits recognition using the proposed architecture using transistor-level circuit simulations. As the described neuromorphic architecture is homogeneous, it realizes a fundamental building block for large-scale energy-efficient brain-inspired silicon chips that could lead to next-generation cognitive computing.

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The West Indian fruit fly, Anastrepha obliqua (Macquart), is one of the most important pests throughout the Americas. CLIMEX 3.0 and ArcGIS 9.3 were used to model the current and future potential geographical distribution of this pest. Under current climatic conditions, A. obliqua is predicted to be able to establish throughout much of the tropics and subtropics, including not only North and South America, where it has been reported, but also southern Asia, northeastern Australia and Sub-Saharan Africa. The main factors limiting the pest's range expansion may be cold stress. Climate change expands the potential distribution of A. obliqua poleward as cold stress boundaries recede, but the predicted distribution in northwestern Australia and northern parts of Sub-Saharan Africa will decrease because of heat stress. Considering the widely suitable range for A. obliqua globally and in China, enhanced quarantine and monitoring measures should be implemented in areas that are projected to be suitable for the establishment of the pest under current and future climatic conditions.

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