# Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP Learning

arXiv: https://arxiv.org/abs/1611.03000 · HTML: https://mlsanity.com/paper/1611.03000/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2016.

## Built on by (most cited first, 9 of 9)

- [Hebbian Deep Learning Without Feedback](https://mlsanity.com/paper/2209.11883/) (2022)
- [A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection](https://mlsanity.com/paper/2104.10719/) (2021)
- [LaSNN: Layer-wise ANN-to-SNN Distillation for Effective and Efficient Training in Deep Spiking Neural Networks](https://mlsanity.com/paper/2304.09101/) (2023)
- [Linear Leaky-Integrate-and-Fire Neuron Model Based Spiking Neural Networks and Its Mapping Relationship to Deep Neural Networks](https://mlsanity.com/paper/2207.04889/) (2022)
- [Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks](https://mlsanity.com/paper/2102.09200/) (2021)
- [Training Deep Spiking Auto-encoders without Bursting or Dying Neurons through Regularization](https://mlsanity.com/paper/2109.11045/) (2021)
- [Spiking Two-Stream Methods with Unsupervised STDP-based Learning for Action Recognition](https://mlsanity.com/paper/2306.13783/) (2023)
- [Fractional-order spike-timing-dependent gradient descent for multi-layer spiking neural networks](https://mlsanity.com/paper/2410.15293/) (2024)
- [Computational models of object motion detectors accelerated using FPGA technology](https://mlsanity.com/paper/2310.06842/) (2023)

## Abstract (arXiv)

Hierarchical feature discovery using non-spiking convolutional neural networks (CNNs) has attracted much recent interest in machine learning and computer vision. However, it is still not well understood how to create a biologically plausible network of brain-like, spiking neurons with multi-layer, unsupervised learning. This paper explores a novel bio-inspired spiking CNN that is trained in a greedy, layer-wise fashion. The proposed network consists of a spiking convolutional-pooling layer followed by a feature discovery layer extracting independent visual features. Kernels for the convolutional layer are trained using local learning. The learning is implemented using a sparse, spiking auto-encoder representing primary visual features. The feature discovery layer extracts independent features by probabilistic, leaky integrate-and-fire (LIF) neurons that are sparsely active in response to stimuli. The layer of the probabilistic, LIF neurons implicitly provides lateral inhibitions to extract sparse and independent features. Experimental results show that the convolutional layer is stack-admissible, enabling it to support a multi-layer learning. The visual features obtained from the proposed probabilistic LIF neurons in the feature discovery layer are utilized for training a classifier. Classification results contribute to the independent and informative visual features extracted in a hierarchy of convolutional and feature discovery layers. The proposed model is evaluated on the MNIST digit dataset using clean and noisy images. The recognition performance for clean images is above 98%. The performance loss for recognizing the noisy images is in the range 0.1% to 8.5% depending on noise types and densities. This level of performance loss indicates that the network is robust to additive noise.