Abstract
Liquid state machine (LSM), a bio-inspired computing model consisting of the input sparsely connected to a randomly interlinked reservoir (or liquid) of spiking neurons followed by a readout layer, finds utility in a range of applications varying from robot control and sequence generation to action, speech, and image recognition. LSMs stand out among other Recurrent Neural Network (RNN) architectures due to their simplistic structure and lower training complexity. Plethora of recent efforts have been focused toward mimicking certain characteristics of biological systems to enhance the performance of modern artificial neural networks. It has been shown that biological neurons are more likely to be connected to other neurons in the close proximity, and tend to be disconnected as the neurons are spatially far apart. Inspired by this, we propose a group of locally connected neuron reservoirs, or an ensemble of liquids approach, for LSMs. We analyze how the segmentation of a single large liquid to create an ensemble of multiple smaller liquids affects the latency and accuracy of an LSM. In our analysis, we quantify the ability of the proposed ensemble approach to provide an improved representation of the input using the Separation Property (SP) and Approximation Property (AP). Our results illustrate that the ensemble approach enhances class discrimination (quantified as the ratio between the SP and AP), leading to better accuracy in speech and image recognition tasks, when compared to a single large liquid. Furthermore, we obtain performance benefits in terms of improved inference time and reduced memory requirements, due to lowered number of connections and the freedom to parallelize the liquid evaluation process.
Highlights
Over the past few decades, artificial neural algorithms have developed to an extent that they can perform more human-like functions
We have presented an ensemble approach for Liquid State Machines (LSMs) that enhances separation and approximation properties, leading to accuracy improvements
The separation property in LSMs measures the dispersion between projected liquid states from different classes, whereas the approximation property indicates the concentration of the liquid states that belong to the same class
Summary
Over the past few decades, artificial neural algorithms have developed to an extent that they can perform more human-like functions. The massive RNNs of today, can describe images in natural language (Xie, 2017), produce handwriting (Graves, 2013), and even make phone calls to book appointments (Yaniv and Yossi, 2018). Such fascinating, human-like capabilities are obtained at the cost of increased structural and training complexity, and significant power consumption, storage requirements, and delay. In this work we focus on a particular type of spiking RNN; the Liquid State Machine (LSM) (Maass et al, 2002). LSMs have been used for a variety of applications including robot control (Urbain et al, 2017), sequence generation (Panda and Roy, 2017), decoding actual brain activity (Nikolicet al., 2009), action recognition (Panda and Srinivasa, 2018), speech recognition (Maass et al, 2002; Verstraeten et al, 2005; Goodman and Ventura, 2006; Zhang et al, 2015; Wu et al, 2018; Zhang and Li, 2019), and image recognition (Grzyb et al, 2009; Wang and Li, 2016; Srinivasan et al, 2018; Zhang and Li, 2019)
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