Advanced Memory and Neural Computing

Measuring the dynamics of neural processing across time scales requires following the spiking of thousands of individual neurons over milliseconds and months. To address this need, we introduce the Neuropixels 2.0 probe together with newly designed analysis algorithms. The probe has more than 5000 sites and is miniatu…

• ÇAĞATAY AYDIN

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Dense crossbar arrays of non-volatile memory (NVM) devices represent one possible path for implementing massively-parallel and highly energy-efficient neuromorphic computing systems. We first review recent advances in the application of NVM devices to three computing paradigms: spiking neural networks (SNNs), deep neu…

Using two phase-change memory devices per synapse, a three-layer perceptron network with 164 885 synapses is trained on a subset (5000 examples) of the MNIST database of handwritten digits using a backpropagation variant suitable for nonvolatile memory (NVM) + selector crossbar arrays, obtaining a training (generaliza…

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,…

Feeling temperature and touch The range of receptors in our skin make it possible to sense when we are touching an object and also gives us a general sense of the temperature of that object. Achieving this in an artificial skin-like material has been a challenge because most of the approaches for sensing touch are the…

• LEVENT BEKER

Event-based cameras are a new passive sensing modality with a number of benefits over traditional cameras, including extremely low latency, asynchronous data acquisition, high dynamic range, and very low power consumption. There has been a lot of recent interest and development in applying algorithms to use the events…

• TOLGA ÖZASLAN

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• TALİP SERKAN KASIRGA

Abstract In-memory computing using resistive memory devices is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. However, due to device variability and noise, the network needs to be trained in a specific way so that transferring the digitally trained weights to the ana…

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• MURAT KARAKUŞ

In this paper, the author derives several sufficient conditions for the asymptotic stability of delayed neural networks. These conditions ensure the asymptotic stability of the equilibrium point of a delayed neural network independently of the delay parameter. The results obtained impose constraint conditions on the i…

• SABRİ ARIK

Memristive technology has been rapidly emerging as a potential alternative to traditional CMOS technology, which is facing fundamental limitations in its development. Since oxide-based resistive switches were demonstrated as memristors in 2008, memristive devices have garnered significant attention due to their biomim…