Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at Elsevier Neural Network

Liquid State Machine with Dendritically Enhanced Readout for Low-power, Neuromorphic VLSI Implementations

In this paper, we describe a new neuro-inspired, hardware-friendly readout stage for the liquid state machine (LSM), a popular model for reservoir computing. Compared to the parallel perceptron architecture trained by the p-delta algorithm, which is the state of the art in terms of performance of readout stages, our readout architecture and learning algorithm can attain better performance with significantly less synaptic resources making it attractive for VLSI implementation. Inspired by the nonlinear properties of dendrites in biological neurons, our readout stage incorporates neurons having multiple dendrites with a lumped nonlinearity. The number of synaptic connections on each branch is significantly lower than the total number of connections from the liquid neurons and the learning algorithm tries to find the best 'combination' of input connections on each branch to reduce the error. Hence, the learning involves network rewiring (NRW) of the readout network similar to structural plasticity observed in its biological counterparts. We show that compared to a single perceptron using analog weights, this architecture for the readout can attain, even by using the same number of binary valued synapses, up to 3.3 times less error for a two-class spike train classification problem and 2.4 times less error for an input rate approximation task. Even with 60 times larger synapses, a group of 60 parallel perceptrons cannot attain the performance of the proposed dendritically enhanced readout. An additional advantage of this method for hardware implementations is that the 'choice' of connectivity can be easily implemented exploiting address event representation (AER) protocols commonly used in current neuromorphic systems where the connection matrix is stored in memory. Also, due to the use of binary synapses, our proposed method is more robust against statistical variations.Comment: 14 pages, 19 figures, Journa

Metal Oxide Memristors with Internal Dynamics for Neuromorphic Applications

Metal oxide memristors, a two-terminal nanoscale semiconductor device whose resistance/conductance can be regulated according to the history of applied stimulations, are initially proposed as a promising candidate for the next generation non-volatile memory. Bearing the similarity to the weight change of synapses in human brain, they are recently being intensively investigated as a critical component in neural network for neuromorphic applications. The resistive switching mechanism is attributed to the redistribution of oxygen vacancies under electric field and spontaneous diffusion. Based on this understanding, 2nd order switching dynamics is discovered and thoroughly investigated for the first time in both WOx memristor and Ta2O5-TaOx memristor and more comprehensive resistive switching models are proposed to quantitively capture the internal ionic dynamics. The dynamics is utilized to implement important synaptic functions including paired pulse facilitation, spike-timing dependent plasticity, experience dependent plasticity, in single cell and in a bio-realistic fashion. WOx memristor crossbar network is used to implement several important neuromorphic applications including: 1) sparse coding, as the network can easily conduct matrix operation, especially dot product and the resistance of each cell at the crosspoint can be regulated to store information needed for computation, 2) temporal information processing through memristor-based liquid state machine, as WOx memristor has the ability to process temporal information due to its short-term memory which is caused by its spontaneous decay characteristics. Improvement of both single cell performance towards better synaptic behaviors and memristor crossbar network performance for large scale applications are achieved by the optimization of fabrication methods.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137133/1/chdu_1.pd

Design of Robust Memristor-Based Neuromorphic Circuits and Systems with Online Learning

Computing systems that are capable of performing human-like cognitive tasks have been an area of active research in the recent past. However, due to the bottleneck faced by the traditionally adopted von Neumann computing architecture, bio-inspired neural network style computing paradigm has seen a spike in research interest. Physical implementations of this paradigm of computing are known as neuromorphic systems. In the recent years, in the domain of neuromorphic systems, memristor based neuromorphic systems have gained increased attention from the research community due to the advantages offered by memristors such as their nanoscale size, nonvolatile nature and power efficient programming capability. However, these devices also suffer from a variety of non-ideal behaviors such as switching speed and threshold asymmetry, limited resolution and endurance that can have a detrimental impact on the operation of the systems employing these devices. This work aims to develop device-aware circuits that are robust in the face of such non-ideal properties. A bi-memristor synapse is first presented whose spike-timing-dependent plasticity (STDP) behavior can be precisely controlled on-chip and hence is shown to be robust. Later, a mixed-mode neuron is introduced that is amenable for use in conjunction with a range of memristors without needing to custom design it. These circuits are then used together to construct a memristive crossbar based system with supervised STDP learning to perform a pattern recognition application. The learning in the crossbar system is shown to be robust to the device-level issues owing to the robustness of the proposed circuits. Lastly, the proposed circuits are applied to build a liquid state machine based reservoir computing system. The reservoir used here is a spiking recurrent neural network generated using an evolutionary optimization algorithm and the readout layer is built with the crossbar system presented earlier, with STDP based online learning. A generalized framework for the hardware implementation of this system is proposed and it is shown that this liquid state machine is robust against device-level switching issues that would have otherwise impacted learning in the readout layer. Thereby, it is demonstrated that the proposed circuits along with their learning techniques can be used to build robust memristor-based neuromorphic systems with online learning

Transient Information Flow in a Network of Excitatory and Inhibitory Model Neurons: Role of Noise and Signal Autocorrelation

We investigate the performance of sparsely-connected networks of integrate-and-fire neurons for ultra-short term information processing. We exploit the fact that the population activity of networks with balanced excitation and inhibition can switch from an oscillatory firing regime to a state of asynchronous irregular firing or quiescence depending on the rate of external background spikes. We find that in terms of information buffering the network performs best for a moderate, non-zero, amount of noise. Analogous to the phenomenon of stochastic resonance the performance decreases for higher and lower noise levels. The optimal amount of noise corresponds to the transition zone between a quiescent state and a regime of stochastic dynamics. This provides a potential explanation on the role of non-oscillatory population activity in a simplified model of cortical micro-circuits.Comment: 27 pages, 7 figures, to appear in J. Physiology (Paris) Vol. 9

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective

On metrics of density and power efficiency, neuromorphic technologies have the potential to surpass mainstream computing technologies in tasks where real-time functionality, adaptability, and autonomy are essential. While algorithmic advances in neuromorphic computing are proceeding successfully, the potential of memristors to improve neuromorphic computing have not yet born fruit, primarily because they are often used as a drop-in replacement to conventional memory. However, interdisciplinary approaches anchored in machine learning theory suggest that multifactor plasticity rules matching neural and synaptic dynamics to the device capabilities can take better advantage of memristor dynamics and its stochasticity. Furthermore, such plasticity rules generally show much higher performance than that of classical Spike Time Dependent Plasticity (STDP) rules. This chapter reviews the recent development in learning with spiking neural network models and their possible implementation with memristor-based hardware

A Digital Neuromorphic Architecture Efficiently Facilitating Complex Synaptic Response Functions Applied to Liquid State Machines

Information in neural networks is represented as weighted connections, or synapses, between neurons. This poses a problem as the primary computational bottleneck for neural networks is the vector-matrix multiply when inputs are multiplied by the neural network weights. Conventional processing architectures are not well suited for simulating neural networks, often requiring large amounts of energy and time. Additionally, synapses in biological neural networks are not binary connections, but exhibit a nonlinear response function as neurotransmitters are emitted and diffuse between neurons. Inspired by neuroscience principles, we present a digital neuromorphic architecture, the Spiking Temporal Processing Unit (STPU), capable of modeling arbitrary complex synaptic response functions without requiring additional hardware components. We consider the paradigm of spiking neurons with temporally coded information as opposed to non-spiking rate coded neurons used in most neural networks. In this paradigm we examine liquid state machines applied to speech recognition and show how a liquid state machine with temporal dynamics maps onto the STPU-demonstrating the flexibility and efficiency of the STPU for instantiating neural algorithms.Comment: 8 pages, 4 Figures, Preprint of 2017 IJCN