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A CMOS synapse design is presented which can perform tunable asymmetric spike timing-dependent learning in asynchronous spiking neural networks. The overall design consists of three primary subcircuit blocks, and the operation of each is described. Pair-based Spike Timing-Dependent Plasticity (STDP) of the entire synapse is then demonstrated through simulation using the Cadence Virtuoso platform....
We present the design and measurement of a continuous-time, accelerated, reconfigurable Leaky Integrate and Fire (LIF) neuron model emulated in 65-nm CMOS technology. The neuron circuit is designed as a sub-circuit of our highly integrated neuromorphic prototype chip, the “HICANN-DLS”. The design is geared towards testability and debug features, as well as area and power efficiency. Each neuron in...
We present the design and measurement of a continuous-time, accelerated, reconfigurable Leaky Integrate and Fire (LIF) neuron model emulated in 65-nm CMOS technology. The neuron circuit is designed as a sub-circuit of our highly integrated neuromorphic prototype chip, the “HICANN-DLS”. The design is geared towards testability and debug features, as well as area and power efficiency. Each neuron in...
Neural encoder is one of the key components in neuromorphic computing systems, whereby sensory information is transformed into spike coded trains. The design of temporal encoder has attracted a widespread attention in the field of neuromorphic computing in the past few years. The information in the temporal encoding scheme with inter-spike intervals can arise from correlations between spike times,...
In this paper, we describe the implementation of simple Boolean logic circuits using a multi-gate floating-body MOSFET, which was originally developed for neuromorphic circuits such as integrate-and-fire neuron circuits. By changing the channel doping level alone, the same circuit with a multi-gate floating-body MOSFET can represent different logic functions such as NAND or NOR. In addition, the circuit...
Biologically-realistic synapses serve the role of computation, modification and communication, which makes it an important pillar of spike-based neuron model. Various circuit implementations of different types of synapse exist depending on different functions and applications. This paper simulates Biomimetic Real-Time Cortex Project design of synapse using SPICE code showing theoretical analysis using...
In this paper, we report a simulation based study of large-scale image learning and recognition using neural network consisting of active pixel sensor (APS), LIF neurons, and memristive devices as synapses in crossbar array. Our studies indicate that images can be efficiently encoded into spiking-patterns using the proposed model which can be used to train the memristive devices based on spike-timing-dependent-plasticity...
This paper presents a CMOS neuron circuit design based on the Hindermarsh-Rose (HR) neuron model. In order to be fabricated in a 0.18μm CMOS technology with 1.8V compatible transistors, both time and amplitude scaling of HR neuron model is adopted. This on chip solution also minimizes the power consumption and circuit size, which is ideal for motion control unit of the proposed bio-mimetic micro-robot...
There are a number of spiking and bursting neuron models with varying levels of complexity, ranging from the simple integrate-and-fire model to the more complex Hodgkin–Huxley model. The simpler models tend to be easily implemented in silicon but yet not biologically plausible. Conversely, the more complex models tend to occupy a large area although they are more biologically plausible. In this paper,...
Most neural networks have a basic competitive learning rule on top of a more involved processing algorithm. This work highlights three basic learning rules - winner-take-all (WTA), spike timing dependent plasticity (STDP), and inhibition of return (IOR). It also gives a design example implementing WTA combined with STDP in a position detector. A CMOS and an MMOST (Memristor-MOS Technology) design...
Modeling neural tissue is an important tool to investigate biological neural networks. Until recently, most of this modeling has been done using numerical methods. In the European research project "FACETS" this computational approach is complemented by different kinds of neuromorphic systems. A special emphasis lies in the usability of these systems for neuroscience. To accomplish this goal...
The Mihalas-Niebur neural model is a generalized model of the leaky integrate-and-fire neuron with adaptive threshold. It has been shown in simulation to be capable of producing most of the known spiking and bursting patterns of cortical neurons. We present results from the first circuit implementation of the model with six spiking patterns observed in biological regular-spiking, fast-spiking and...
Silicon neuron circuits emulate the electrophysiological behavior of real neurons. Many circuits can be integrated on a single very large scale integration (VLSI) device, and form large networks of spiking neurons. Connectivity among neurons can be achieved by using time multiplexing and fast asynchronous digital circuits. As the basic characteristics of the silicon neurons are determined at design...
A CMOS circuit is proposed that emulates FitzHugh-Nagumo's differential equations using OTAs, diode connected MOSFETs and capacitors. These equations model the fundamental behavior of biological neuron cells. Fitz-Hugh-Nagumo's model is characterized by two threshold values. If the input to the neuron is between the two thresholds the output yields a sequence of firing pulses, if the input is outside...
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