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In this paper, we present an on-sensor neuromorphic vision hardware implementation of denoising spatial filter. The mean or median spatial filters with fixed window shape are known for its denoising ability, however, have the drawback of blurring the object edges. The effect of blurring increases with increase in window size. To preserve the edge information, we propose a spatial filter that uses...
Recent experimental work has demonstrated nano- textured magnetic Josephson junctions (MJJs) that exhibit tunable spiking behavior with ultra-low training energies in the attojoule range. MJJ devices integrated with standard single-flux-quantum neural systems form a new class of neuromorphic technologies that have spiking energies between attojoules and zeptojoules, operation frequencies up to 100...
Artificial neural networks and deep learning methodologies have had growing interest across industry domains, including IoT and mobile systems. However, in low-power applications, resource limitations and operating environment restrictions make implementations difficult. This survey examines efforts that target the data and compute challenges of implementing energy efficient, low cost, and accurate...
Over the last few years, research was aimed to investigate neuromorphic computing methodologies for understanding the functions and behavior of biological neurons on a real-time basis. In neuron electrical models, the principles of computational neuroscience is translated on to analog hardware and the circuits reproduces the bio-physical properties of neurons. Our aim was to implement analog neuron...
Characterizing neural responses and behavior require large scale simulation of brain circuits. Spatio-temporal information processing in large scale neural simulations often require compromises between computing resources and realistic details to be represented. In this work, we compared the implementations of point neuron models and biophysically detailed neuron models on serial and parallel hardware...
Several interconnected brain circuits such as cerebellum, cerebral cortex, thalamus and basal ganglia process motor information in many species including mammals. Interconnection between basal ganglia and cerebellum through thalamus and cortex may influence the pathways involved in basal ganglia processing. Malfunctions in the neural circuitry of basal ganglia influenced by modifications in the dopaminergic...
Neuronal models and real-time simulations of large-scale neural networks allow hypothesis testing of physiological data and for predicting neurological disorders. Simulators using web technologies serve as educational tools in addition to allowing experimentalists make predictions on experimental hypotheses. In this paper, we have developed a web-based neuron and network simulator to model spatio-temporal...
Local Field Potentials arising (LFP) from neural circuits are crucial to understand neural ensemble activity and can act as a link between molecular, cellular and circuit neuroscience. Additionally, mathematical estimations of LFPs allow the study of circuit functions and dysfunctions. In this study, we used mathematical reconstructions of LFP in rat cerebellum Crus IIa using spiking neuronal models...
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....
In this paper, we present artificial neural network (ANN) models to predict hard and soft-responses of three configurations of arbiter based physical unclonable functions (PUFs): standard, feed-forward (FF) and modified feed-forward (MFF). The models are trained using data extracted from 32-stage arbiter PUF circuits fabricated using IBM 32 nm HKMG process. The contributions of this paper are two-fold...
A spiking neuron and 3-terminal Resistive RAM (RRAM) model are proposed and simulated as a neural network. The system is analyzed as a complex network of spiking neurons connected by synapses to demonstrate a biologically-inspired associative memory. In recent years, Machine Learning and Artificial Intelligence have become popular fields due to readily available high performance computing systems...
3D integration technology offers a near term strategy for bypassing Moore's Law. Applying 3D integration to neuromorphic computing (NC) could provide a low power consumption, high-connectivity, and massively parallel processed system that can accommodate high demand computational tasks. This paper proposes a novel analog spiking nanoscale 3D NC system, wherein both neurons and synapses are stacked...
Clique-based neural networks are less complex than commonly used neural network models. They have a limited connectivity and are composed of simple functions. They are thus adapted to implement neuro-inspired computation units operating under severe energy constraints. This paper shows an ST 65-nm CMOS ASIC implementation for a 30-neuron clique-based neural network circuit. With a 1V power supply...
The Neural Engineering Framework (NEF) is a theory for mapping computations onto biologically plausible networks of spiking neurons. This theory has been applied to a number of neuromorphic chips. However, within both silicon and real biological systems, synapses exhibit higher-order dynamics and heterogeneity. To date, the NEF has not explicitly addressed how to account for either feature. Here,...
Power efficiency of the dendritic arbor has a high impact on the overall power efficiency of a neuromorphic design. We deploy a sub-threshold switched-capacitor mechanism and power gating to carry out summation of post synaptic potentials (PSPs) as well as capturing passive properties of dendrites through sampling PSPs on the capacitors and serializing them to perform summation rather than providing...
Commonly used brain-inspired models assume that neurons in the network are silent until perturbed by external stimuli. This assumption has created a family of feedforward neural models that progressively detects complex features. However, experimental observations suggest that the brain is spontaneously active where neuronal activity is driven by internal fluctuations in total synaptic input leading...
We present an array of Mihalas-Niebur neurons with dynamically reconfigurable synapses implemented in 0.5 μm CMOS technology optimized for low-power, low-mismatch, and high-density. This neural array has two modes of operation: one is each cell in the array operates as independent leaky integrate-and-fire neurons, and the second is two cells work together to model the Mihalas-Niebur neuron dynamics...
To operate under severe energy constraints, clique-based neural networks are good candidates. They benefit from a reduced exchange of information between low-complexity processing units with no performance degradation. This paper proposes a modular, flexible and scalable architecture validated by an ST 65-nm CMOS ASIC implementation for a 30-neuron clique-based neural network circuit. With 0.8V power...
In this paper, a ring-oscillator (RO) based sub-1V leaky integrate-and-fire (I&F) neuron circuit is proposed, that can support user programmable refractory period and spike-frequency adaptation. Designed in CMOS 65-nm TSMC process, the neuron can operate from 0.9 V and has the unique feature that the same circuit can be programmed to operate either at biological time-scales or at accelerated time-scales...
Here we discuss synaptic temporal integrators capable of computing plasticity functions between principal neurons which are inhibitory to yield a useful network dynamic. We model inhibitory spike-timing-dependent plasticity (iSTDP), parameterized by two thresholds of a pre- and post-synaptic leaky integrator. Because inhibitory synapses between principal neurons do not occur onto spines, but instead...
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