Abstract—A subthreshold MOS integrated circuit (IC) is de-signed and fabricated for implementing a competitive neural network of the Lotka–Volterra (LV) type which is derived from conventional membrane dynamics of neurons and is used for the selection of external inputs. The steady-state solutions to the LV equation can be classified into three types, each of which represents qualitatively different selection behavior. Among the solutions, the winners-share-all (WSA) solution in which a certain number of neurons remain activated in steady states is particu-larly useful owing to robustness in the selection of inputs from a noisy environment. The measured results of the fabricated LV IC’s agree well with the theoretical prediction as long as ...
A transistor neural network based on MOS characteristics has been developed in order to implement el...
Artificial Neural Networks are powerful computational tools with many diverse applications in a vari...
There are several possible hardware implementations of neural networks based either on digital, anal...
An analog MOS circuit is proposed for implementing a Lotka–Volterra (LV) competitive neural network ...
Abstract. We describe an aVLSI network consisting of a group of excitatory neurons and a global inhi...
Abstract. We describe an aVLSI network consisting of a group of excitatory neurons and a global inhi...
Möller R, Maris M, Tomes J, Mojaev A. A strong winner-take-all neural network in analogue hardware. ...
Abstract—This paper studies a general class of dynamical neural networks with lateral inhibition, ex...
We have designed, fabricated, and tested a series of compact CMOS integrated circuits that realize t...
MasterThis paper presents a neuromorphic IC based-on winner-take-all (WTA) network. To reduce the ef...
Abstract. Winner-take-all (WTA) circuits are commonly used in a wide variety of applications. One of...
As the integrated circuit (IC) technology advances into smaller nanometre feature sizes, a fixed-err...
We have designed, fabricated, and tested a series of compact CMOS integrated circuits that realize t...
As the integrated circuit (IC) technology advances into smaller nanometre feature sizes, a fixed-err...
A neural network is designed using a multiple-input transconductance amplifier (MITA) and digital mu...
A transistor neural network based on MOS characteristics has been developed in order to implement el...
Artificial Neural Networks are powerful computational tools with many diverse applications in a vari...
There are several possible hardware implementations of neural networks based either on digital, anal...
An analog MOS circuit is proposed for implementing a Lotka–Volterra (LV) competitive neural network ...
Abstract. We describe an aVLSI network consisting of a group of excitatory neurons and a global inhi...
Abstract. We describe an aVLSI network consisting of a group of excitatory neurons and a global inhi...
Möller R, Maris M, Tomes J, Mojaev A. A strong winner-take-all neural network in analogue hardware. ...
Abstract—This paper studies a general class of dynamical neural networks with lateral inhibition, ex...
We have designed, fabricated, and tested a series of compact CMOS integrated circuits that realize t...
MasterThis paper presents a neuromorphic IC based-on winner-take-all (WTA) network. To reduce the ef...
Abstract. Winner-take-all (WTA) circuits are commonly used in a wide variety of applications. One of...
As the integrated circuit (IC) technology advances into smaller nanometre feature sizes, a fixed-err...
We have designed, fabricated, and tested a series of compact CMOS integrated circuits that realize t...
As the integrated circuit (IC) technology advances into smaller nanometre feature sizes, a fixed-err...
A neural network is designed using a multiple-input transconductance amplifier (MITA) and digital mu...
A transistor neural network based on MOS characteristics has been developed in order to implement el...
Artificial Neural Networks are powerful computational tools with many diverse applications in a vari...
There are several possible hardware implementations of neural networks based either on digital, anal...