# Neural Population Coding for Effective Temporal Classification

arXiv: https://arxiv.org/abs/1909.08018 · HTML: https://mlsanity.com/paper/1909.08018/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Sep 2019.

## Built on by (most cited first, 10 of 10)

- [Unleashing the Potential of Spiking Neural Networks by Dynamic Confidence](https://mlsanity.com/paper/2303.10276/) (2023)
- [Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning](https://mlsanity.com/paper/2111.01750/) (2021)
- [Neuromorphic Computing is Turing-Complete](https://mlsanity.com/paper/2104.13983/) (2021)
- [Fully Spiking Neural Network for Legged Robots](https://mlsanity.com/paper/2310.05022/) (2023)
- [Unsupervised Clustering of Time Series Signals using Neuromorphic Energy-Efficient Temporal Neural Networks](https://mlsanity.com/paper/2102.09200/) (2021)
- [IML-Spikeformer: Input-aware Multi-Level Spiking Transformer for Speech Processing](https://mlsanity.com/paper/2507.07396/) (2025)
- [Population-Coded Spiking Neural Networks for High-Dimensional Robotic Control](https://mlsanity.com/paper/2510.10516/) (2025)
- [A Novel Neuromorphic Processors Realization of Spiking Deep Reinforcement Learning for Portfolio Management](https://mlsanity.com/paper/2203.14159/) (2022)
- [Advantages of Neural Population Coding for Deep Learning](https://mlsanity.com/paper/2411.00393/) (2024)
- [Neuromorphic-based metaheuristics: A new generation of low power, low latency and small footprint optimization algorithms](https://mlsanity.com/paper/2505.16362/) (2025)

## Abstract (arXiv)

Neural encoding plays an important role in faithfully describing the temporally rich patterns, whose instances include human speech and environmental sounds. For tasks that involve classifying such spatio-temporal patterns with the Spiking Neural Networks (SNNs), how these patterns are encoded directly influence the difficulty of the task. In this paper, we compare several existing temporal and population coding schemes and evaluate them on both speech (TIDIGITS) and sound (RWCP) datasets. We show that, with population neural codings, the encoded patterns are linearly separable using the Support Vector Machine (SVM). We note that the population neural codings effectively project the temporal information onto the spatial domain, thus improving linear separability in the spatial dimension, achieving an accuracy of 95% and 100% for TIDIGITS and RWCP datasets classified using the SVM, respectively. This observation suggests that an effective neural coding scheme greatly simplifies the classification problem such that a simple linear classifier would suffice. The above datasets are then classified using the Tempotron, an SNN-based classifier. SNN classification results agree with the SVM findings that population neural codings help to improve classification accuracy. Hence, other than the learning algorithm, effective neural encoding is just as important as an SNN designed to recognize spatio-temporal patterns. It is an often neglected but powerful abstraction that deserves further study.