Search Results for author: Yansong Chua

Found 11 papers, 0 papers with code

SoftHebb: Bayesian inference in unsupervised Hebbian soft winner-take-all networks

no code implementations12 Jul 2021 Timoleon Moraitis, Dmitry Toichkin, Yansong Chua, Qinghai Guo

On the other hand, Hebbian learning in winner-take-all (WTA) networks, is unsupervised, feed-forward, and biologically plausible.

Bayesian Inference

Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks

no code implementations26 Mar 2020 Malu Zhang, Jiadong Wang, Burin Amornpaisannon, Zhixuan Zhang, VPK Miriyala, Ammar Belatreche, Hong Qu, Jibin Wu, Yansong Chua, Trevor E. Carlson, Haizhou Li

In STDBP algorithm, the timing of individual spikes is used to convey information (temporal coding), and learning (back-propagation) is performed based on spike timing in an event-driven manner.

Decision Making

Transfer Learning in General Lensless Imaging through Scattering Media

no code implementations28 Dec 2019 Yukuan Yang, Lei Deng, Peng Jiao, Yansong Chua, Jing Pei, Cheng Ma, Guoqi Li

In summary, this work provides a new solution for lensless imaging through scattering media using transfer learning in DNNs.

Transfer Learning

Neural Population Coding for Effective Temporal Classification

no code implementations12 Sep 2019 Zihan Pan, Jibin Wu, Yansong Chua, Malu Zhang, Haizhou Li

We show that, with population neural codings, the encoded patterns are linearly separable using the Support Vector Machine (SVM).

Classification General Classification

An efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks

no code implementations3 Sep 2019 Zihan Pan, Yansong Chua, Jibin Wu, Malu Zhang, Haizhou Li, Eliathamby Ambikairajah

The neural encoding scheme, that we call Biologically plausible Auditory Encoding (BAE), emulates the functions of the perceptual components of the human auditory system, that include the cochlear filter bank, the inner hair cells, auditory masking effects from psychoacoustic models, and the spike neural encoding by the auditory nerve.

Speech Recognition

A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks

no code implementations2 Jul 2019 Jibin Wu, Yansong Chua, Malu Zhang, Guoqi Li, Haizhou Li, Kay Chen Tan

Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures.

Event-based vision

Hardware-friendly Neural Network Architecture for Neuromorphic Computing

no code implementations3 Apr 2019 Roshan Gopalakrishnan, Yansong Chua, Ashish Jith Sreejith Kumar

The hardware-software co-optimization of neural network architectures is becoming a major stream of research especially due to the emergence of commercial neuromorphic chips such as the IBM Truenorth and Intel Loihi.

Deep Spiking Neural Network with Spike Count based Learning Rule

no code implementations15 Feb 2019 Jibin Wu, Yansong Chua, Malu Zhang, Qu Yang, Guoqi Li, Haizhou Li

Deep spiking neural networks (SNNs) support asynchronous event-driven computation, massive parallelism and demonstrate great potential to improve the energy efficiency of its synchronous analog counterpart.

MaD: Mapping and debugging framework for implementing deep neural network onto a neuromorphic chip with crossbar array of synapses

no code implementations1 Jan 2019 Roshan Gopalakrishnan, Ashish Jith Sreejith Kumar, Yansong Chua

Neuromorphic systems or dedicated hardware for neuromorphic computing is getting popular with the advancement in research on different device materials for synapses, especially in crossbar architecture and also algorithms specific or compatible to neuromorphic hardware.

Is Neuromorphic MNIST neuromorphic? Analyzing the discriminative power of neuromorphic datasets in the time domain

no code implementations3 Jul 2018 Laxmi R. Iyer, Yansong Chua, Haizhou Li

We also use this SNN for further experiments on N-MNIST to show that rate based SNNs perform better, and precise spike timings are not important in N-MNIST.

Classifying neuromorphic data using a deep learning framework for image classification

no code implementations2 Jul 2018 Roshan Gopalakrishnan, Yansong Chua, Laxmi R. Iyer

Since then, several neuromorphic datasets as obtained by applying such sensors on image datasets (e. g. the neuromorphic CALTECH 101) have been introduced.

Classification General Classification +1

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