The main goal in realizing aVLSI (analog VLSI)\ud
systems able to mimic functionalities of biological neural networks is pointed to the reproduction of realistic synapses. Indeed,\ud
because of the relative high synapse/neuron ratio, especially in\ud
the case of extremely dense networks (i.e., reproduction of a real\ud
scenario), synapses represent a considerable limitation in terms\ud
of waste of silicon area and power consumption as well. Thanks\ud
to advancement made in the implementation of memristor, the\ud
interest in bio-inspired neural network design has been renewed.\ud
Memristors have tunable resistance which depends on its past\ud
state; this is analogous to the operating mode of biological\ud
synapses. In this paper, we present the circuit implementation of\ud
a simple memristor-based neural network. Here, we propose a\ud
driving circuit model that not requires specific shape input pulses\ud
to change the memristor conductance (i.e., synaptic strength), but\ud
it can be driven by arbitrary shaped input pulses. Moreover, this\ud
prototype circuit offers the chance of emulating the standard\ud
STDP behavior allowing “controlled” changes for the synaptic\ud
weights. Some preliminary experimental results are reported to\ud
validate the proposed driving circui
In this paper, the hardware implementation of a neuromorphic system is presented. This system is composed of a Leaky Integrate-and-Fire with Latency (LIFL) neuron and a Spike-Timing Dependent Plasticity (STDP) synapse. LIFL neuron model allows to encode more information than the common Integrate-and-Fire models, typically considered for neuromorphic implementations. In our system LIFL neuron is implemented using CMOS circuits while memristor is used for the implementation of the STDP synapse. A description of the entire circuit is provided. Finally, the capabilities of the proposed architecture have been evaluated by simulating a motif composed of three neurons and two synapses. The simulation results confirm the validity of the proposed system and its suitability for the design of more complex spiking neural networks
A zinc oxide (ZnO)-reduced graphene oxide (rGO) composite thin film memristive device is reported. Further, it has been shown that it is possible to implement Hebbian learning rules like, the spiketiming-dependent plasticity, using this device. Furthermore, a circuit on PCB is developed; this circuit can imitate the biological spike firing scheme and activate the memristor synapse. The fabricated device along with the custom made circuit can be extended for developing future neuromorphic circuit applications.
The mathematical model describes the electrical and mechanical activity of the cardiac conduction system thought set of differential equations. By changing the value of parameters included in these equations, it is possible to change the amplitude and the period of ECG waves. Although this model is a powerful tool for modeling the electrical activity of the heart, its use is often limited to those familiar with the differential equations that describe the system. The purpose of this work is to provide a system that allows generating an ECG signal using Ryzhii model without knowing the details of differential equations. First, we provide the relationships between the ECG wave features and the model parameters; then we generalize them through fitting neural networks. Finally, putting in series fitting neural network and heart model, we provide a system that allows generating a synthetic signal by setting as input only the morphological ECG feature. We computed numerical simulation in Simulink environment and implemented the fitting neural networks in Matlab. Results show that non-linear trends characterize the correlation functions between ECG morphological features and model parameters and that the fitting neural networks can generalized this trend by providing the model parameters given in input the respectively ECG feature.
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