Aging Increases Susceptibility to Develop Cardiac Hypertrophy following High Sugar Consumption.

Aging and poor diet are independent risk factors for heart disease, but the impact of high-sucrose (HS) consumption in the aging heart is understudied. Aging leads to impairments in mitochondrial function that result in muscle dysfunction (e.g., cardiac remodeling and sarcopenia). We tested whether HS diet (60%kcal sucrose) would accelerate muscle dysfunction in 24-month-old male CB6F1 mice. By week 1 on HS diet, mice developed significant cardiac hypertrophy compared to age-matched chow-fed controls. The increased weight of the heart persisted throughout the 4-week treatment, while body weight and strength declined more rapidly than controls. We then tested whether HS diet could worsen cardiac dysfunction in old mice and if the mitochondrial-targeted drug, elamipretide (ELAM), could prevent the diet-induced effect. Old and young mice were treated with either ELAM or saline as a control for 2 weeks, and provided with HS diet or chow on the last week. As demonstrated in the previous experiment, old mice had age-related cardiac hypertrophy that worsened after one week on HS and was prevented by ELAM treatment, while the HS diet had no detectable effect on hypertrophy in the young mice. As expected, mitochondrial respiration and reactive oxygen species (ROS) production were altered by age, but were not significantly affected by HS diet or ELAM. Our findings highlight the vulnerability of the aged heart to HS diet that can be prevented by systemic targeting of the mitochondria with ELAM.

A Soft-Pruning Method Applied During Training of Spiking Neural Networks for In-memory Computing Applications.

Inspired from the computational efficiency of the biological brain, spiking neural networks (SNNs) emulate biological neural networks, neural codes, dynamics, and circuitry. SNNs show great potential for the implementation of unsupervised learning using in-memory computing. Here, we report an algorithmic optimization that improves energy efficiency of online learning with SNNs on emerging non-volatile memory (eNVM) devices. We develop a pruning method for SNNs by exploiting the output firing characteristics of neurons. Our pruning method can be applied during network training, which is different from previous approaches in the literature that employ pruning on already-trained networks. This approach prevents unnecessary updates of network parameters during training. This algorithmic optimization can complement the energy efficiency of eNVM technology, which offers a unique in-memory computing platform for the parallelization of neural network operations. Our SNN maintains ~90% classification accuracy on the MNIST dataset with up to ~75% pruning, significantly reducing the number of weight updates. The SNN and pruning scheme developed in this work can pave the way toward applications of eNVM based neuro-inspired systems for energy efficient online learning in low power applications.

Neuroinspired unsupervised learning and pruning with subquantum CBRAM arrays.

Resistive RAM crossbar arrays offer an attractive solution to minimize off-chip data transfer and parallelize on-chip computations for neural networks. Here, we report a hardware/software co-design approach based on low energy subquantum conductive bridging RAM (CBRAM®) devices and a network pruning technique to reduce network level energy consumption. First, we demonstrate low energy subquantum CBRAM devices exhibiting gradual switching characteristics important for implementing weight updates in hardware during unsupervised learning. Then we develop a network pruning algorithm that can be employed during training, different from previous network pruning approaches applied for inference only. Using a 512 kbit subquantum CBRAM array, we experimentally demonstrate high recognition accuracy on the MNIST dataset for digital implementation of unsupervised learning. Our hardware/software co-design approach can pave the way towards resistive memory based neuro-inspired systems that can autonomously learn and process information in power-limited settings.