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Abstract Distinguishing between nectar and non-nectar odors is challenging for animals due to shared compounds and varying ratios in complex mixtures. Changes in nectar production throughout the day and over the animal’s lifetime add to the complexity. The honeybee olfactory system, containing fewer than 1000 principal neurons in the early olfactory relay, the antennal lobe (AL), must learn to associate diverse volatile blends with rewards. Previous studies identified plasticity in the AL circuits, but its role in odor learning remains poorly understood. Using a biophysical computational model, tuned by in vivo electrophysiological data, and live imaging of the honeybee’s AL, we explored the neural mechanisms of plasticity in the AL. Our findings revealed that when trained with a set of rewarded and unrewarded odors, the AL inhibitory network suppresses responses to shared chemical compounds while enhancing responses to distinct compounds. This results in improved pattern separation and a more concise neural code. Our calcium imaging data support these predictions. Analysis of a graph convolutional neural network performing an odor categorization task revealed a similar mechanism for contrast enhancement. Our study provides insights into how inhibitory plasticity in the early olfactory network reshapes the coding for efficient learning of complex odors. 

Adversarial bit-flip attack (BFA), a type of powerful adversarial weight attack demonstrated in real computer systems has shown enormous success in compromising Deep Neural Network (DNN) performance with a minimal amount of model parameter perturbation through rowhammer-based computer main memory bit-flip. For the first time in this work, we demonstrate to defeat adversarial bit-flip attacks by developing a Robust and Accurate Binary Neural Network (RA-BNN) that adopts a complete BNN (i.e., weights and activations are both in binary). Prior works have demonstrated that binary or clustered weights could intrinsically improve a network's robustness against BFA, while in this work, we further reveal that binary activation could improve such robustness even better. However, with both aggressive binary weight and activation representations, the complete BNN suffers from poor clean (i.e., no attack) inference accuracy. To counter this, we propose an efficient two-stage complete BNN growing method for constructing simultaneously robust and accurate BNN, named RA-Growth. It selectively grows the channel size of each BNN layer based on trainable channel-wise binary mask learning with a Gumbel-Sigmoid function. The wider binary network (i.e., RA-BNN) has dual benefits: it can recover clean inference accuracy and significantly higher resistance against BFA. Our evaluation of the CIFAR-10 dataset shows that the proposed RA-BNN can improve the resistance to BFA by up to 100 x. On ImageNet, with a sufficiently large (e.g., 5,000) number of bit-flips, the baseline BNN accuracy drops to 4.3 % from 51.9 %, while our RA-BNN accuracy only drops to 37.1 % from 60.9 %, making it the best defense performance. 

This work presents the first resistive random access memory (RRAM)-based compute-in-memory (CIM) macro design tailored for genome processing. We analyze and demonstrate two key types of genome processing applications using our developed CIM chip prototype: the state-of-the-art (SOTA) burrows–wheeler transform (BWT)-based DNA short- read alignment and alignment-free mRNA quantification. Our CIM macro is designed and optimized to support the major functions essential to these algorithms, e.g., parallel XNOR operations, count, addition, and parallel bit-wise and operations. The proposed CIM macro prototype is fabricated with monolithic integration of HfO2 RRAM and 65-nm CMOS, achieving 2.07 TOPS/W (tera-operations per second per watt) and 2.12 G suffixes/J (suffixes per joule) at 1.0 V, which is the most energy-efficient solution to date for genome processing. 

Redox-active colloids (RACs) represent a novel class of energy carriers that exchange electrical energy upon contact. Understanding contact-mediated electron transfer dynamics in RACs offers insights into physical contact events in colloidal suspensions and enables quantification of electrical energy transport in nonconjugated polymers. Redox-based electron transport was directly observed in monolayers of micron-sized RACs containing ethyl-viologen side groups via fluorescence microscopy through an unexpected nonlinear electrofluorochromism that is quantitatively coupled to the redox state of the colloid. Via imaging studies, using this electrofluorochromism, the apparent charge transfer diffusion coefficientD_CTof the RAC was easily determined. The visualization of energy transport within suspensions of redox-active colloids was also demonstrated. Our work elucidates fundamental mechanisms of energy transport in colloidal systems, informs the development of next-generation redox flow batteries, and may inspire new designs of smart active soft matter including conductive polymers for applications ranging from electrochemical sensors and organic electronics to colloidal robotics.