On the spatiotemporal behavior in biology-mimicking computing systems

• URL: http://arxiv.org/abs/2009.08841v3
• Date: Wed, 23 Sep 2020 14:20:21 GMT
• Title: On the spatiotemporal behavior in biology-mimicking computing systems
• Authors: J\'anos V\'egh, \'Ad\'am J. Berki
• Abstract summary: The payload performance of conventional computing systems, from single processors to supercomputers, reached its limits the nature enables. Both the growing demand to cope with "big data" (based on, or assisted by, artificial intelligence) and the interest in understanding the operation of our brain more completely, stimulated the efforts to build biology-mimicking computing systems. These systems require an unusually large number of processors, which introduces performance limitations and nonlinear scaling.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The payload performance of conventional computing systems, from single processors to supercomputers, reached its limits the nature enables. Both the growing demand to cope with "big data" (based on, or assisted by, artificial intelligence) and the interest in understanding the operation of our brain more completely, stimulated the efforts to build biology-mimicking computing systems from inexpensive conventional components and build different ("neuromorphic") computing systems. On one side, those systems require an unusually large number of processors, which introduces performance limitations and nonlinear scaling. On the other side, the neuronal operation drastically differs from the conventional workloads. The conventional computing (including both its mathematical background and physical implementation) is based on assuming instant interaction, while the biological neuronal systems have a "spatiotemporal" behavior. This difference alone makes imitating biological behavior in technical implementation hard. Besides, the recent issues in computing called the attention to that the temporal behavior is a general feature of computing systems, too. Some of their effects in both biological and technical systems were already noticed. Nevertheless, handling of those issues is incomplete/improper. Introducing temporal logic, based on the Minkowski transform, gives quantitative insight into the operation of both kinds of computing systems, furthermore provides a natural explanation of decades-old empirical phenomena. Without considering their temporal behavior correctly, neither effective implementation nor a true imitation of biological neural systems are possible.

Related papers

• Covariant spatio-temporal receptive fields for neuromorphic computing [1.9365675487641305]
  This work combines efforts within scale theory and computational neuroscience to identify theoretically well-founded ways to process-temporal signals in neuromorphic systems. Our contributions are immediately relevant for signal processing and event-based vision, and can be extended to other processing tasks over space and time, such as memory and control.
  arXiv  Detail & Related papers  (2024-05-01T04:51:10Z)
• A Review of Neuroscience-Inspired Machine Learning [58.72729525961739]
  Bio-plausible credit assignment is compatible with practically any learning condition and is energy-efficient. In this paper, we survey several vital algorithms that model bio-plausible rules of credit assignment in artificial neural networks. We conclude by discussing the future challenges that will need to be addressed in order to make such algorithms more useful in practical applications.
  arXiv  Detail & Related papers  (2024-02-16T18:05:09Z)
• 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)
• Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
  Predictive coding (PC) has shown promising performance in machine intelligence tasks. PC can model information processing in different brain areas, can be used in cognitive control and robotics.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• There's Plenty of Room Right Here: Biological Systems as Evolved, Overloaded, Multi-scale Machines [0.0]
  We argue that a useful path forward results from abandoning hard boundaries between categories and adopting an observer-dependent, pragmatic view. Efforts to re-shape living systems for biomedical or bioengineering purposes require prediction and control of their function at multiple scales. We argue that an observer-centered framework for the computations performed by evolved and designed systems will improve the understanding of meso-scale events.
  arXiv  Detail & Related papers  (2022-12-20T22:26:40Z)
• Neurocompositional computing: From the Central Paradox of Cognition to a new generation of AI systems [120.297940190903]
  Recent progress in AI has resulted from the use of limited forms of neurocompositional computing. New, deeper forms of neurocompositional computing create AI systems that are more robust, accurate, and comprehensible.
  arXiv  Detail & Related papers  (2022-05-02T18:00:10Z)
• 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)
• Do we know the operating principles of our computers better than those of our brain? [0.0]
  The paper discusses how the conventional principles, components and thinking about computing limit mimicking the biological systems. We describe what changes will be necessary in the computing paradigms to get closer to the marvelously efficient operation of biological neural networks.
  arXiv  Detail & Related papers  (2020-05-06T20:41:23Z)
• Spiking Neural Networks Hardware Implementations and Challenges: a Survey [53.429871539789445]
  Spiking Neural Networks are cognitive algorithms mimicking neuron and synapse operational principles. We present the state of the art of hardware implementations of spiking neural networks. We discuss the strategies employed to leverage the characteristics of these event-driven algorithms at the hardware level.
  arXiv  Detail & Related papers  (2020-05-04T13:24:00Z)
• Quantum computing using continuous-time evolution [0.0]
  Digital silicon computers have reached their limits in terms of speed. Quantum computing exploits the coherence and superposition of quantum systems. Early quantum computers will be small, reminiscent of the early days of digital silicon computing.
  arXiv  Detail & Related papers  (2020-04-01T20:58:15Z)

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.