Related papers: Organic log-domain integrator synapse
The significant advancements in integrated photonics have enabled high-speed and energy efficient systems for various applications from data communications and high-performance computing, to medical diagnosis, sensing and ranging. However,…
Current-driven switching of nonvolatile spintronic materials and devices based on spin-orbit torques offer fast data processing speed, low power consumption, and unlimited endurance for future information processing applications. Analogous…
Inhibitory neurons play a crucial role in maintaining persistent neuronal activity. Although connected extensively through electrical synapses (gap-junctions), these neurons also exhibit interactions through chemical synapses in certain…
Monolithic three-dimensional integration of memory and logic circuits could dramatically improve performance and energy efficiency of computing systems. Some conventional and emerging memories are suitable for vertical integration,…
Among various types of neuromorphic devices towards artificial intelligence, the electrochemical synaptic transistor emerges, in which the channel conductance is modulated by the insertion of ions according to the history of gate voltage…
We propose a new digital-to-analog converter (DAC) for realizing a synapse circuit of mixed-signal spiking neural networks. We named this circuit "time-domain DAC (TDAC)". This produces weights for converting a digital input code into…
Memristors are emerging as key electronic components that retain resistance states without power. Their non-volatile nature and ability to mimic synaptic behavior make them ideal for next-generation memory technologies and neuromorphic…
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…
The ability to learn and respond to recurrent events depends on the capacity to remember transient biological signals received in the past. Moreover, it may be desirable to remember or ignore these transient signals conditioned upon other…
Future brain-machine interfaces, prosthetics, and intelligent soft robotics will require integrating artificial neuromorphic devices with biological systems. Due to their poor biocompatibility, circuit complexity, low energy efficiency, and…
We numerically demonstrate a network of coupled oscillators that can learn to solve a classification task from a set of examples -- performing both training and inference through the nonlinear evolution of the system. We accomplish this by…
Adaptive response to a varying environment is a common feature of biological organisms. Reproducing such features in electronic systems and circuits is of great importance for a variety of applications. Here, we consider memory models…
Advances in neuroscience uncover the mechanisms employed by the brain to efficiently solve complex learning tasks with very limited resources. However, the efficiency is often lost when one tries to port these findings to a silicon…
The memristive device is one of the basic elements of novel, brain-inspired, fast, and energy-efficient information processing systems in which there is no separation between memorization and information analysis functions. Since the first…
We present a mathematical analysis of a networks with Integrate-and-Fire neurons and adaptive conductances. Taking into account the realistic fact that the spike time is only known within some \textit{finite} precision, we propose a model…
A recurrent loop consisting of a single neuron is considered which is influenced by a chemical excitatory delayed synaptic feedback. We show the response of the system is dependent to the duration of the activity of the synapse which is…
Heterogeneous systems with analog CMOS circuits integrated with nanoscale memristive devices enable efficient deployment of neural networks on neuromorphic hardware. CMOS Neuron with low footprint can emulate slow temporal dynamics by…
We propose a design principle for the learning circuits of the biological brain. The principle states that almost any dendritic weights updated via heterosynaptic plasticity can implement a generalized and efficient class of gradient-based…
Characterizing the relation between weight structure and input/output statistics is fundamental for understanding the computational capabilities of neural circuits. In this work, I study the problem of storing associations between analog…
Nanoscale resistive memories are expected to fuel dense integration of electronic synapses for large-scale neuromorphic system. To realize such a brain-inspired computing chip, a compact CMOS spiking neuron that performs in-situ learning…