Search Results for author:
Found 16 papers, 4 papers with code
Spiking-LEAF: A Learnable Auditory front-end for Spiking Neural Networks
Brain-inspired spiking neural networks (SNNs) have demonstrated great potential for temporal signal processing.
Unleashing the Potential of Spiking Neural Networks for Sequential Modeling with Contextual Embedding
The human brain exhibits remarkable abilities in integrating temporally distant sensory inputs for decision-making.
TC-LIF: A Two-Compartment Spiking Neuron Model for Long-term Sequential Modelling
The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays.
Long Short-term Memory with Two-Compartment Spiking Neuron
The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays.
A Hybrid Neural Coding Approach for Pattern Recognition with Spiking Neural Networks
The biological neural systems evolved to adapt to ecological environment for efficiency and effectiveness, wherein neurons with heterogeneous structures and rich dynamics are optimized to accomplish complex cognitive tasks.
Training Spiking Neural Networks with Local Tandem Learning
The LTL rule follows the teacher-student learning approach by mimicking the intermediate feature representations of a pre-trained ANN.
Target Speaker Verification with Selective Auditory Attention for Single and Multi-talker Speech
Inspired by the study on target speaker extraction, e. g., SpEx, we propose a unified speaker verification framework for both single- and multi-talker speech, that is able to pay selective auditory attention to the target speaker.
Multi-Tones' Phase Coding (MTPC) of Interaural Time Difference by Spiking Neural Network
Inspired by the mammal's auditory localization pathway, in this paper we propose a pure spiking neural network (SNN) based computational model for precise sound localization in the noisy real-world environment, and implement this algorithm in a real-time robotic system with a microphone array.
Progressive Tandem Learning for Pattern Recognition with Deep Spiking Neural Networks
In this paper, we propose a novel ANN-to-SNN conversion and layer-wise learning framework for rapid and efficient pattern recognition, which is referred to as progressive tandem learning of deep SNNs.
You Only Spike Once: Improving Energy-Efficient Neuromorphic Inference to ANN-Level Accuracy
Rate-encoded SNNs could be seen as inefficient as an encoding scheme because it involves the transmission of a large number of spikes.
Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks
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.
Deep Spiking Neural Networks for Large Vocabulary Automatic Speech Recognition
The brain-inspired spiking neural networks (SNN) closely mimic the biological neural networks and can operate on low-power neuromorphic hardware with spike-based computation.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Neural Population Coding for Effective Temporal Classification
We show that, with population neural codings, the encoded patterns are linearly separable using the Support Vector Machine (SVM).
An efficient and perceptually motivated auditory neural encoding and decoding algorithm for spiking neural networks
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.
A Tandem Learning Rule for Effective Training and Rapid Inference of Deep Spiking Neural Networks
Spiking neural networks (SNNs) represent the most prominent biologically inspired computing model for neuromorphic computing (NC) architectures.
Deep Spiking Neural Network with Spike Count based Learning Rule
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.
