Sequence learning in a spiking neuronal network with memristive synapses

• URL: http://arxiv.org/abs/2211.16592v1
• Date: Tue, 29 Nov 2022 21:07:23 GMT
• Title: Sequence learning in a spiking neuronal network with memristive synapses
• Authors: Younes Bouhadjar, Sebastian Siegel, Tom Tetzlaff, Markus Diesmann, Rainer Waser, Dirk J. Wouters
• Abstract summary: A core concept that lies at the heart of brain computation is sequence learning and prediction. Neuromorphic hardware emulates the way the brain processes information and maps neurons and synapses directly into a physical substrate. We study the feasibility of using ReRAM devices as a replacement of the biological synapses in the sequence learning model.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Brain-inspired computing proposes a set of algorithmic principles that hold promise for advancing artificial intelligence. They endow systems with self learning capabilities, efficient energy usage, and high storage capacity. A core concept that lies at the heart of brain computation is sequence learning and prediction. This form of computation is essential for almost all our daily tasks such as movement generation, perception, and language. Understanding how the brain performs such a computation is not only important to advance neuroscience but also to pave the way to new technological brain-inspired applications. A previously developed spiking neural network implementation of sequence prediction and recall learns complex, high-order sequences in an unsupervised manner by local, biologically inspired plasticity rules. An emerging type of hardware that holds promise for efficiently running this type of algorithm is neuromorphic hardware. It emulates the way the brain processes information and maps neurons and synapses directly into a physical substrate. Memristive devices have been identified as potential synaptic elements in neuromorphic hardware. In particular, redox-induced resistive random access memories (ReRAM) devices stand out at many aspects. They permit scalability, are energy efficient and fast, and can implement biological plasticity rules. In this work, we study the feasibility of using ReRAM devices as a replacement of the biological synapses in the sequence learning model. We implement and simulate the model including the ReRAM plasticity using the neural simulator NEST. We investigate the effect of different device properties on the performance characteristics of the sequence learning model, and demonstrate resilience with respect to different on-off ratios, conductance resolutions, device variability, and synaptic failure.

Related papers

• Single Neuromorphic Memristor closely Emulates Multiple Synaptic Mechanisms for Energy Efficient Neural Networks [71.79257685917058]
  We demonstrate memristive nano-devices based on SrTiO3 that inherently emulate all these synaptic functions. These memristors operate in a non-filamentary, low conductance regime, which enables stable and energy efficient operation.
  arXiv  Detail & Related papers  (2024-02-26T15:01:54Z)
• Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks [74.3099028063756]
  We develop a new method with neuronal operations based on lateral connections and Hebbian learning. We show that Hebbian and anti-Hebbian learning on recurrent lateral connections can effectively extract the principal subspace of neural activities. Our method consistently solves for spiking neural networks with nearly zero forgetting.
  arXiv  Detail & Related papers  (2024-02-19T09:29:37Z)
• Brain-Inspired Machine Intelligence: A Survey of Neurobiologically-Plausible Credit Assignment [65.268245109828]
  We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology. We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors. The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
  arXiv  Detail & Related papers  (2023-12-01T05:20:57Z)
• A bio-inspired implementation of a sparse-learning spike-based hippocampus memory model [0.0]
  We propose a novel bio-inspired memory model based on the hippocampus. It can learn memories, recall them from a cue and even forget memories when trying to learn others with the same cue. This work presents the first hardware implementation of a fully functional bio-inspired spike-based hippocampus memory model.
  arXiv  Detail & Related papers  (2022-06-10T07:48:29Z)
• Neuromorphic Artificial Intelligence Systems [58.1806704582023]
  Modern AI systems, based on von Neumann architecture and classical neural networks, have a number of fundamental limitations in comparison with the brain. This article discusses such limitations and the ways they can be mitigated. It presents an overview of currently available neuromorphic AI projects in which these limitations are overcome.
  arXiv  Detail & Related papers  (2022-05-25T20:16:05Z)
• Memory-enriched computation and learning in spiking neural networks through Hebbian plasticity [9.453554184019108]
  Hebbian plasticity is believed to play a pivotal role in biological memory. We introduce a novel spiking neural network architecture that is enriched by Hebbian synaptic plasticity. We show that Hebbian enrichment renders spiking neural networks surprisingly versatile in terms of their computational as well as learning capabilities.
  arXiv  Detail & Related papers  (2022-05-23T12:48:37Z)
• A Robust Learning Rule for Soft-Bounded Memristive Synapses Competitive with Supervised Learning in Standard Spiking Neural Networks [0.0]
  A view in theoretical neuroscience sees the brain as a function-computing device. Being able to approximate functions is a fundamental axiom to build upon for future brain research. In this work we apply a novel supervised learning algorithm - based on controlling niobium-doped strontium titanate memristive synapses - to learning non-trivial multidimensional functions.
  arXiv  Detail & Related papers  (2022-04-12T10:21:22Z)
• POPPINS : A Population-Based Digital Spiking Neuromorphic Processor with Integer Quadratic Integrate-and-Fire Neurons [50.591267188664666]
  We propose a population-based digital spiking neuromorphic processor in 180nm process technology with two hierarchy populations. The proposed approach enables the developments of biomimetic neuromorphic system and various low-power, and low-latency inference processing applications.
  arXiv  Detail & Related papers  (2022-01-19T09:26:34Z)
• Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi [77.34726150561087]
  We investigate the potential of Intel's fifth generation neuromorphic chip - Loihi' Loihi is based on the novel idea of Spiking Neural Networks (SNNs) emulating the neurons in the brain. We find that Loihi replicates classical simulations very efficiently and scales notably well in terms of both time and energy performance as the networks get larger.
  arXiv  Detail & Related papers  (2021-09-22T16:52:51Z)

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.