Memory-inspired spiking hyperdimensional network for robust online learning

Zou, Zhuowen; Alimohamadi, Haleh; Zakeri, Ali; Imani, Farhad; Kim, Yeseong; Najafi, M. Hassan; Imani, Mohsen

doi:10.1038/s41598-022-11073-3

Cited by 14 publications

(4 citation statements)

References 77 publications

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“…via local signals (Bellec et al, 2020 ), or via random projections (Kaiser et al, 2020 ). The latter technique has previously been likened to the biological mechanisms behind short-term memory (Zou et al, 2022 ). We will discuss a specific implementation in Section 3.2.…”

Section: Methodsmentioning

confidence: 99%

Bayesian continual learning via spiking neural networks

Skatchkovsky

Jang

Simeone

2022

Front. Comput. Neurosci.

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Among the main features of biological intelligence are energy efficiency, capacity for continual adaptation, and risk management via uncertainty quantification. Neuromorphic engineering has been thus far mostly driven by the goal of implementing energy-efficient machines that take inspiration from the time-based computing paradigm of biological brains. In this paper, we take steps toward the design of neuromorphic systems that are capable of adaptation to changing learning tasks, while producing well-calibrated uncertainty quantification estimates. To this end, we derive online learning rules for spiking neural networks (SNNs) within a Bayesian continual learning framework. In it, each synaptic weight is represented by parameters that quantify the current epistemic uncertainty resulting from prior knowledge and observed data. The proposed online rules update the distribution parameters in a streaming fashion as data are observed. We instantiate the proposed approach for both real-valued and binary synaptic weights. Experimental results using Intel's Lava platform show the merits of Bayesian over frequentist learning in terms of capacity for adaptation and uncertainty quantification.

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Section: Methodsmentioning

confidence: 99%

Bayesian continual learning via spiking neural networks

Skatchkovsky

Jang

Simeone

2022

Front. Comput. Neurosci.

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“…The Figure 3 shown the general pipeline for this model for a classification task and the detailed architecture is defined in section 3. Previous works have been made inspiring in brain functionality emulating for classification tasks such as MINST dataset classification using SNN and Hyperdimensional computing [41] or in the decoding and understanding of muscle activity and kinematics from electroencephalography signals [42], utilizing Hyperdimensional Computing (HDC) and SNN for the MNIST classification problem [41]. Other works have explored the application of Reinforcement Learning for navigation in dynamic and unfamiliar environments, supporting neuroscience-based theories that consider grid cells as crucial for vector-based navigation [43].…”

Section: Salinementioning

confidence: 99%

Brain-inspired agents for Quantum Reinforcement Learning

Andrés,

Cuellar,

Navarro

2024

Preprint

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In recent years, advancements in brain science and neuroscience have significantly influenced the field of computer science, particularly in the domain of reinforcement learning (RL). Drawing insights from neurobiology and neuropsychology, researchers have leveraged these findings to develop novel mechanisms for understanding intelligent decision-making processes in the brain. Concurrently, the emergence of quantum computing has opened new frontiers in artificial intelligence, leading to the development of quantum machine learning (QML). This study introduces a novel model that integrates Quantum Spiking Neural Networks (QSNN) and Quantum Long Short-Term Memory (QLSTM) architectures, inspired by the complex workings of the human brain. Specifically designed for reinforcement learning tasks in energy-efficient environments, our approach progresses through two distinct stages mirroring sensory and memory systems. In the initial stage, analogous to the brain’s hypothalamus, low-level information is extracted to emulate sensory data processing patterns. Subsequently, resembling the hippocampus, this information is processed at a higher level, capturing and memorizing correlated patterns. We conduct a comparative analysis of our model against existing quantum models such as Quantum Neural Networks and their classical counterparts, elucidating its unique contributions. Through empirical results, we aim to underscore the effectiveness of our approach in optimizing energy consumption in practical scenarios.

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“…While in [16] the Sparse Block Codes model was mapped to an SNN circuit. In other related efforts, an event-based dynamic vision sensor [142,284] or an SNN [291,436] was used to perform the initial processing of the input signals that were then transformed to HVs to form the prediction model.…”

Section: 25mentioning

confidence: 99%

A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges

et al. 2023

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This is Part II of the two-part comprehensive survey devoted to a computing framework most commonly known under the names Hyperdimensional Computing and Vector Symbolic Architectures (HDC/VSA). Both names refer to a family of computational models that use high-dimensional distributed representations and rely on the algebraic properties of their key operations to incorporate the advantages of structured symbolic representations and vector distributed representations. Holographic Reduced Representations [322, 327] is an influential HDC/VSA model that is well-known in the machine learning domain and often used to refer to the whole family. However, for the sake of consistency, we use HDC/VSA to refer to the field. Part I of this survey [223] covered foundational aspects of the field, such as the historical context leading to the development of HDC/VSA, key elements of any HDC/VSA model, known HDC/VSA models, and the transformation of input data of various types into high-dimensional vectors suitable for HDC/VSA. This second part surveys existing applications, the role of HDC/VSA in cognitive computing and architectures, as well as directions for future work. Most of the applications lie within the Machine Learning/Artificial Intelligence domain, however, we also cover other applications to provide a complete picture. The survey is written to be useful for both newcomers and practitioners.

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Memory-inspired spiking hyperdimensional network for robust online learning

Cited by 14 publications

References 77 publications

Bayesian continual learning via spiking neural networks

Bayesian continual learning via spiking neural networks

Brain-inspired agents for Quantum Reinforcement Learning

A Survey on Hyperdimensional Computing aka Vector Symbolic Architectures, Part II: Applications, Cognitive Models, and Challenges

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