Related papers: Memristive integrative sensors for neuronal activi…
The influence of the epileptiform neuronal activity on the response of a CMOS-integrated ZrO2-based memristive crossbar and its conductivity was studied. Epileptiform neuronal activity was obtained in vitro in the hippocampus slices of…
Memristive neuromorphic systems are designed to emulate human perception and cognition, where the memristor states represent essential historical information to perform both low-level and high-level tasks. However, current systems face…
Rapid progress of artificial neural network applications in recent years has led to the issue of an unprecedented energy consumption. It can be solved by the implementation of energy efficient hardware based on non-von-Neumann…
We introduce a novel electro-optomechanic neural sensor for realizing ultra-compact neural recording probes that can detect and relay electrophysiology signals from within neural tissue. This technology addresses outstanding challenges…
Sensory processing with neuromorphic systems is typically done by using either event-based sensors or translating input signals to spikes before presenting them to the neuromorphic processor. Here, we offer an alternative approach: direct…
Memristors have uses as artificial synapses and perform well in this role in simulations with artificial spiking neurons. Our experiments show that memristor networks natively spike and can exhibit emergent oscillations and bursting spikes.…
Memristive devices hold promise to improve the scale and efficiency of machine learning and neuromorphic hardware, thanks to their compact size, low power consumption, and the ability to perform matrix multiplications in constant time.…
Memristors have demonstrated immense potential as building blocks in future adaptive neuromorphic architectures. Recently, there has been focus on emulating specific synaptic functions of the mammalian nervous system by either tailoring the…
Self-organizing memristive networks are physical circuits that dynamically reconfigure their circuitry in response to external input signals. Their adaptive behavior arises from intrinsic neuro-synaptic dynamics combined with a…
Optical memristors represent a monumental leap in the fusion of photonics and electronics, heralding a new era of applications from neuromorphic computing to artificial intelligence. However, current technologies are hindered by complex…
A complete data acquisition and signal output control system for synchronous stimuli generation, geared towards in vivo neuroscience experiments, was developed using the Terasic DE2i-150 board. All emotions and thoughts are an emergent…
Spike detection plays a central role in neural data processing and brain-machine interfaces (BMIs). A challenge for future-generation implantable BMIs is to build a spike detector that features both low hardware cost and high performance.…
Here we present our Python toolbox "MR. Estimator" to reliably estimate the intrinsic timescale from electrophysiologal recordings of heavily subsampled systems. Originally intended for the analysis of time series from neuronal spiking…
Memristors have been widely studied as artificial synapses in neuromorphic circuits, due to their functional similarity with biological synapses, low operating power, and high integration density. In this work, a memristive synapse,…
To obtain precisely controllable, robust as well as reproduceable memristor for efficient neuromorphic computing still very challenging. Molecular tailoring aims at obtaining the much more flexibly tuning plasticity has recently generated…
Controllable quantized conductance states of TiN/Ti/HfO$_x$/TiN memristors are realized with great precision through a pulse-mode reset procedure, assisted with analytical differentiation of the condition of the set procedure, which…
Interface-type resistive switching (RS) devices with lower operation current and more reliable switching repeatability exhibits great potential in the applications for data storage devices and ultra-low-energy computing. However, the…
Memristive associative learning has gained significant attention for its ability to mimic fundamental biological learning mechanisms while maintaining system simplicity. In this work, we introduce a high-order memristive associative…
Hardware neural networks could perform certain computational tasks orders of magnitude more energy-efficiently than conventional computers. Artificial neurons are a key component of these networks and are currently implemented with…
Conventional neuro-computing architectures and artificial neural networks have often been developed with no or loose connections to neuroscience. As a consequence, they have largely ignored key features of biological neural processing…