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The computational function of neural networks is thought to depend primarily on the learning/plasticity function carried out at the synapse. Neuromorphic circuit realizations have taken this into account by implementing a variety of synaptical processing functions, with most recent synapse circuits replicating some form of Spike Time Dependent Plasticity(More)
The information processing of neural networks depends heavily on the learning/plasticity function carried out at the individual synapses. Traditionally, neuromorphic ICs have integrated forms of Spike-Time-Dependent-Plasticity (STDP), a subset of the rich repertoire of biological plasticity. However, STDP is challenged by rate-dependent learning as well as(More)
Neuromorphic realizations of the short-term dynamics at a synapse often use simplistic circuit models. In this paper, we present a more biologically realistic VLSI implementation of these mechanisms. Our circuit approach is analytically derived from a model of neurotransmitter release, so that it can be directly related to simulation results and biological(More)
Neuromorphic circuits try to replicate aspects of the information processing in neural tissue. Historically, this has often meant some kind of long-term learning function which slowly adjusts the weight of a synapse to achieve a certain target network function. Recently, short-term dynamics at the synapse have also gained significant attention due to their(More)
A switched-capacitor (SC) neuromorphic system for closed-loop neural coupling in 28 nm CMOS is presented, occupying 600 um by 600 um. It offers 128 input channels (i.e., presynaptic terminals), 8192 synapses and 64 output channels (i.e., neurons). Biologically realistic neuron and synapse dynamics are achieved via a faithful translation of the behavioural(More)
Efficient Analog-Digital Converters (ADC) are one of the mainstays of mixed-signal integrated circuit design. Besides the conventional ADCs used in mainstream ICs, there have been various attempts in the past to utilize neuromorphic networks to accomplish an efficient crossing between analog and digital domains, i.e., to build neurally inspired ADCs.(More)
Synaptic dynamics, such as long- and short-term plasticity, play an important role in the complexity and biological realism achievable when running neural networks on a neuromorphic IC. For example, they endow the IC with an ability to adapt and learn from its environment. In order to achieve the millisecond to second time constants required for these(More)
For neuromorphic ICs, the implemented synaptic dynamics play an important role in the complexity achievable when running networks on the overall IC. One of these ingredients for realistic dynamics are conductance-based synapses, which in contrast to current-based synapses let a neuron adapt in various ways to its input characteristics. Another ingredient is(More)
Computational tasks such as object and pattern recognition rely on deterministic learning in the brain carried out mostly at the synapses, which link the brain’s neurons and shape the overall computational function of a group of neurons.1 Modelers build mathematical abstractions of synaptic learning by approximating the behavior of biological synapses as(More)