MC-QDSNN: Quantized Deep evolutionary SNN with Multi-Dendritic Compartment Neurons for Stress Detection using Physiological Signals

• URL: http://arxiv.org/abs/2410.04992v3
• Date: Fri, 25 Oct 2024 09:25:00 GMT
• Title: MC-QDSNN: Quantized Deep evolutionary SNN with Multi-Dendritic Compartment Neurons for Stress Detection using Physiological Signals
• Authors: Ajay B S, Phani Pavan K, Madhav Rao,
• Abstract summary: This work proposes Multi-Compartment Leaky (MCLeaky) neuron as a viable alternative for efficient processing of time series data. The proposed MCLeaky neuron based Spiking Neural Network model and its quantized variant were benchmarked against state-of-the-art (SOTA) Spiking LSTMs. Results show that networks with MCLeaky activation neuron managed a superior accuracy of 98.8% to detect stress.
• Score: 1.474723404975345
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Long short-term memory (LSTM) has emerged as a definitive network for analyzing and inferring time series data. LSTM has the capability to extract spectral features and a mixture of temporal features. Due to this benefit, a similar feature extraction method is explored for the spiking counterparts targeting time-series data. Though LSTMs perform well in their spiking form, they tend to be compute and power intensive. Addressing this issue, this work proposes Multi-Compartment Leaky (MCLeaky) neuron as a viable alternative for efficient processing of time series data. The MCLeaky neuron, derived from the Leaky Integrate and Fire (LIF) neuron model, contains multiple memristive synapses interlinked to form a memory component, which emulates the human brain's Hippocampus region. The proposed MCLeaky neuron based Spiking Neural Network model and its quantized variant were benchmarked against state-of-the-art (SOTA) Spiking LSTMs to perform human stress detection, by comparing compute requirements, latency and real-world performances on unseen data with models derived through Neural Architecture Search (NAS). Results show that networks with MCLeaky activation neuron managed a superior accuracy of 98.8% to detect stress based on Electrodermal Activity (EDA) signals, better than any other investigated models, while using 20% less parameters on average. MCLeaky neuron was also tested for various signals including EDA Wrist and Chest, Temperature, ECG, and combinations of them. Quantized MCLeaky model was also derived and validated to forecast their performance on hardware architectures, which resulted in 91.84% accuracy. The neurons were evaluated for multiple modalities of data towards stress detection, which resulted in energy savings of 25.12x to 39.20x and EDP gains of 52.37x to 81.9x over ANNs, while offering a best accuracy of 98.8% when compared with the rest of the SOTA implementations.

Related papers

• Scaling Wearable Foundation Models [54.93979158708164]
  We investigate the scaling properties of sensor foundation models across compute, data, and model size. Using a dataset of up to 40 million hours of in-situ heart rate, heart rate variability, electrodermal activity, accelerometer, skin temperature, and altimeter per-minute data from over 165,000 people, we create LSM. Our results establish the scaling laws of LSM for tasks such as imputation, extrapolation, both across time and sensor modalities.
  arXiv  Detail & Related papers  (2024-10-17T15:08:21Z)
• Hybridized Convolutional Neural Networks and Long Short-Term Memory for Improved Alzheimer's Disease Diagnosis from MRI Scans [2.621434923709917]
  This study aims to present a hybrid model that combines a CNN model's feature extraction capabilities with an LSTM model's detection capabilities. The training of the hybrid model involved utilizing the ADNI dataset. The model achieved a level of accuracy of 98.8%, a sensitivity rate of 100%, and a specificity rate of 76%.
  arXiv  Detail & Related papers  (2024-03-08T14:34:32Z)
• WaLiN-GUI: a graphical and auditory tool for neuron-based encoding [73.88751967207419]
  Neuromorphic computing relies on spike-based, energy-efficient communication. We develop a tool to identify suitable configurations for neuron-based encoding of sample-based data into spike trains. The WaLiN-GUI is provided open source and with documentation.
  arXiv  Detail & Related papers  (2023-10-25T20:34:08Z)
• The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks [64.08042492426992]
  We introduce the Expressive Memory (ELM) neuron model, a biologically inspired model of a cortical neuron. Our ELM neuron can accurately match the aforementioned input-output relationship with under ten thousand trainable parameters. We evaluate it on various tasks with demanding temporal structures, including the Long Range Arena (LRA) datasets.
  arXiv  Detail & Related papers  (2023-06-14T13:34:13Z)
• Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit [56.801856519460465]
  Continuous time autoregressive recurrent neural networks (CTRNNs) are a deep learning model that account for irregular observations. We demonstrate the application of these models to probabilistic forecasting of blood glucose in a critical care setting.
  arXiv  Detail & Related papers  (2023-04-14T09:39:06Z)
• Atrial Fibrillation Detection Using Weight-Pruned, Log-Quantised Convolutional Neural Networks [25.160063477248904]
  A convolutional neural network model is developed for detecting atrial fibrillation from electrocardiogram signals. The model demonstrates high performance despite being trained on limited, variable-length input data. The final model achieved a 91.1% model compression ratio while maintaining high model accuracy of 91.7% and less than 1% loss.
  arXiv  Detail & Related papers  (2022-06-14T11:47:04Z)
• Axonal Delay As a Short-Term Memory for Feed Forward Deep Spiking Neural Networks [3.985532502580783]
  Recent studies have found that the time delay of neurons plays an important role in the learning process. configuring the precise timing of the spike is a promising direction for understanding and improving the transmission process of temporal information in SNNs. In this paper, we verify the effectiveness of integrating time delay into supervised learning and propose a module that modulates the axonal delay through short-term memory.
  arXiv  Detail & Related papers  (2022-04-20T16:56:42Z)
• SPICEprop: Backpropagating Errors Through Memristive Spiking Neural Networks [2.8971214387667494]
  We present a fully memristive spiking neural network (MSNN) consisting of novel memristive neurons trained using the backpropagation through time (BPTT) learning rule. Gradient descent is applied directly to the memristive integrated-and-fire (MIF) neuron designed using analog SPICE circuit models. We achieve 97.58% accuracy on the MNIST testing dataset and 75.26% on the Fashion-MNIST testing dataset, the highest accuracies among all fully MSNNs.
  arXiv  Detail & Related papers  (2022-03-02T21:34:43Z)
• SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier [68.8204255655161]
  Implantable devices that record neural activity and detect seizures have been adopted to issue warnings or trigger neurostimulation to suppress seizures. For an implantable seizure detection system, a low power, at-the-edge, online learning algorithm can be employed to dynamically adapt to neural signal drifts. SOUL was fabricated in TSMC's 28 nm process occupying 0.1 mm2 and achieves 1.5 nJ/classification energy efficiency, which is at least 24x more efficient than state-of-the-art.
  arXiv  Detail & Related papers  (2021-10-01T23:01:20Z)
• Flexible Transmitter Network [84.90891046882213]
  Current neural networks are mostly built upon the MP model, which usually formulates the neuron as executing an activation function on the real-valued weighted aggregation of signals received from other neurons. We propose the Flexible Transmitter (FT) model, a novel bio-plausible neuron model with flexible synaptic plasticity. We present the Flexible Transmitter Network (FTNet), which is built on the most common fully-connected feed-forward architecture.
  arXiv  Detail & Related papers  (2020-04-08T06:55:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.