# Bio-Inspired Multi-Layer Spiking Neural Network Extracts Discriminative Features from Speech Signals

arXiv: https://arxiv.org/abs/1706.03170 · HTML: https://mlsanity.com/paper/1706.03170/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Jun 2017.

## Built on by (most cited first, 4 of 4)

- [Direct Learning-Based Deep Spiking Neural Networks: A Review](https://mlsanity.com/paper/2305.19725/) (2023)
- [Including STDP to eligibility propagation in multi-layer recurrent spiking neural networks](https://mlsanity.com/paper/2201.07602/) (2022)
- [IML-Spikeformer: Input-aware Multi-Level Spiking Transformer for Speech Processing](https://mlsanity.com/paper/2507.07396/) (2025)
- [HetSyn: Versatile Timescale Integration in Spiking Neural Networks via Heterogeneous Synapses](https://mlsanity.com/paper/2508.11644/) (2025)

## Abstract (arXiv)

Spiking neural networks (SNNs) enable power-efficient implementations due to their sparse, spike-based coding scheme. This paper develops a bio-inspired SNN that uses unsupervised learning to extract discriminative features from speech signals, which can subsequently be used in a classifier. The architecture consists of a spiking convolutional/pooling layer followed by a fully connected spiking layer for feature discovery. The convolutional layer of leaky, integrate-and-fire (LIF) neurons represents primary acoustic features. The fully connected layer is equipped with a probabilistic spike-timing-dependent plasticity learning rule. This layer represents the discriminative features through probabilistic, LIF neurons. To assess the discriminative power of the learned features, they are used in a hidden Markov model (HMM) for spoken digit recognition. The experimental results show performance above 96% that compares favorably with popular statistical feature extraction methods. Our results provide a novel demonstration of unsupervised feature acquisition in an SNN.