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Improved on-chip circuit densities have enabled the practical realization of increasingly demanding applications. Microelectronics design now faces a number of challenges: hardware has become more complex to describe making it a more arduous process for designs to pass verification and the proportion of a design that is covered by testing is reduced increasing the likelihood of bugs in the final hardware...
This research investigates a novel data reduction scheme using adaptive leaky refractory integrate-and-fire (ALRIF) neurons to generate pulses for an implanted neural recording system in wireless transmission applications. The wireless implanted multi-channel recording system imposes many constraints on the system but the major constraint is on low bandwidth. Other constraints including low bandwidth...
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 role in dynamic and temporal information...
We present a circuit architecture for compact analog VLSI implementation of the Izhikevich neuron model, which efficiently describes a wide variety of neuron spiking and bursting dynamics using two state variables and four adjustable parameters. Log-domain circuit design utilizing MOS transistors in subthreshold results in high energy efficiency, with less than 1 pJ of energy consumed per spike. We...
Spiking neurons and spiking neural circuits are finding uses in a multitude of tasks such as robotic locomotion control, neuroprosthetics, visual sensory processing, and audition. The desired neural output is achieved through the use of complex neuron models, or by combining multiple simple neurons into a network. In either case, a means for configuring the neuron or neural circuit is required. Manual...
This paper proposes a silicon neuron circuit which uses a slow-variable controlled leakage term to extend the repertoire of spiking patterns achievable in an integrate and fire model. The simulations reveal the potential of the circuit to provide a wide variety of neuron firing patterns observed in neocortex, including adapting and non-adapting, regular spiking, fast spiking, bursting, chattering,...
Nowadays trends in the electronic system design look beyond the conventional computing architectures towards biologically inspired solutions to find new circuit ideas to advance the computational power of the future computational structures. The present paper introduces a VHDL-AMS based spiking neuron model, which exhibits realistic behavior. This approach is suitable to integrate hybrid neuro-eletronic...
This paper presents an analogue VLSI circuit which implements three particular receptors of the chemical synapse: AMPA, GABAA and GABAB. The dynamics of postsynaptic receptor is implemented in CMOS current mode circuits. Furthermore with the implementation of chemical sensors in CMOS technology, the possibility of realising complete synapses which model the non-linear electrochemical behaviour of...
Most VLSI spiking network implementations are constructed using point neurons. However, neurons with extended dendritic structures might offer additional computational advantages. Experimental evidence suggests that dendritic compartments could be considered as independent and parallel computational units. Depending on the synaptic input patterns, the dendritic integration could be either linear or...
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...
The lateral superior olive is the first neural area to receive binaural inputs in the mammalian sound localization pathway. Selective to interaural level differences (ILD) while intensity-independent, these neurons signal when a sound arrives from within a limited range of angles. Although several different computational models have been proposed to explain ILD-selectivity, we highlight one that has...
We demonstrate neuron spiking dynamics in a small network of analog silicon neurons with dynamical conductance-based synapses. The analog VLSI chip (NeuroDyn) emulates analog continuous-time dynamics in a fully digitally programmable network of 4 biophysical neurons. Each neuron in NeuroDyn implements Hodgkin-Huxley dynamics in 4 variables, with 28 parameters defining the conductances, reversal potentials,...
A use of auditory brainstem response in auditory disorder diagnosis is investigated in this paper leading to a proposed characterization method of the human auditory system. A modified nonlinear continuous time Hopfield neural type system is able to model the system yielding the simulated response in good agreement with the measured one. Both Simulink and PSpice simulation are investigated as well...
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