Neuromorphic computing with multi-memristive synapses.

Neuromorphic computing has emerged as a promising avenue towards building the next generation of intelligent computing systems. It has been proposed that memristive devices, which exhibit history-dependent conductivity modulation, could efficiently represent the synaptic weights in artificial neural networks. However, precise modulation of the device conductance over a wide dynamic range, necessary to maintain high network accuracy, is proving to be challenging. To address this, we present a multi-memristive synaptic architecture with an efficient global counter-based arbitration scheme. We focus on phase change memory devices, develop a comprehensive model and demonstrate via simulations the effectiveness of the concept for both spiking and non-spiking neural networks. Moreover, we present experimental results involving over a million phase change memory devices for unsupervised learning of temporal correlations using a spiking neural network. The work presents a significant step towards the realization of large-scale and energy-efficient neuromorphic computing systems.

Temporal correlation detection using computational phase-change memory.

Conventional computers based on the von Neumann architecture perform computation by repeatedly transferring data between their physically separated processing and memory units. As computation becomes increasingly data centric and the scalability limits in terms of performance and power are being reached, alternative computing paradigms with collocated computation and storage are actively being sought. A fascinating such approach is that of computational memory where the physics of nanoscale memory devices are used to perform certain computational tasks within the memory unit in a non-von Neumann manner. We present an experimental demonstration using one million phase change memory devices organized to perform a high-level computational primitive by exploiting the crystallization dynamics. Its result is imprinted in the conductance states of the memory devices. The results of using such a computational memory for processing real-world data sets show that this co-existence of computation and storage at the nanometer scale could enable ultra-dense, low-power, and massively-parallel computing systems.

Peripheral administration of poly I:C leads to increased hippocampal amyloid-beta and cognitive deficits in a non-transgenic mouse.

Alzheimer's disease (AD) is a progressive disorder characterized by neuronal and behavioral deterioration. Two hallmark pathologies of AD are amyloid-beta (Aß) plaques and neurofibrillary tangles, and the presence of such pathology can limit cell-to-cell communication, leading to cognitive deficits, and neuronal cell death. Although Aß plaques were originally thought to cause the cognitive deficits, more simple forms of Aß, such as monomers, dimers, tetramers and oligomers, have also been shown to be neurotoxic. Moreover, chronic inflammation has also been implicated in the onset and progression of these AD-related pathologies. The current study was designed to further our understanding of peripheral inflammation-induced AD-like pathology, by administering polyinosinic:polycytidylic acid (poly I:C), a viral mimetic. Mice were administered intraperitoneal injections of poly I:C or saline once daily for 7 consecutive days. Hippocampal tissue from animals receiving poly I:C contained significantly higher levels of the Aß1₋42 peptide. Even after ensuring that potential sickness behavior could not confound cognitive testing, we found that animals administered poly I:C displayed significant cognitive deficits in the hippocampus-dependent contextual fear conditioning paradigm. These results confirm our hypothesis that peripheral inflammation can lead to increased levels of hippocampal-Aß and associated cognitive deficits.