Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks
- URL: http://arxiv.org/abs/2408.13996v2
- Date: Wed, 28 Aug 2024 08:28:57 GMT
- Title: Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks
- Authors: Tianyu Zheng, Liyuan Han, Tielin Zhang,
- Abstract summary: Spiking neural networks (SNNs) are gaining popularity in the computational simulation and artificial intelligence fields.
This paper explores the historical development of SNN and concludes that these two fields are intersecting and merging rapidly.
- Score: 8.315801422499861
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Spiking neural networks (SNNs) are gaining popularity in the computational simulation and artificial intelligence fields owing to their biological plausibility and computational efficiency. This paper explores the historical development of SNN and concludes that these two fields are intersecting and merging rapidly. Following the successful application of Dynamic Vision Sensors (DVS) and Dynamic Audio Sensors (DAS), SNNs have found some proper paradigms, such as continuous visual signal tracking, automatic speech recognition, and reinforcement learning for continuous control, that have extensively supported their key features, including spike encoding, neuronal heterogeneity, specific functional circuits, and multiscale plasticity. Compared to these real-world paradigms, the brain contains a spiking version of the biology-world paradigm, which exhibits a similar level of complexity and is usually considered a mirror of the real world. Considering the projected rapid development of invasive and parallel Brain-Computer Interface (BCI), as well as the new BCI-based paradigms that include online pattern recognition and stimulus control of biological spike trains, SNNs naturally leverage their advantages in energy efficiency, robustness, and flexibility. The biological brain has inspired the present study of SNNs and effective SNN machine-learning algorithms, which can help enhance neuroscience discoveries in the brain by applying them to the new BCI paradigm. Such two-way interactions with positive feedback can accelerate brain science research and brain-inspired intelligence technology.
Related papers
- Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling [52.06722364186432]
We propose a biologically-informed framework for enhancing artificial neural networks (ANNs)
Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors.
We outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, bioinspiration and complexity.
arXiv Detail & Related papers (2024-07-05T14:11:28Z) - 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) - SpikingJelly: An open-source machine learning infrastructure platform
for spike-based intelligence [51.6943465041708]
Spiking neural networks (SNNs) aim to realize brain-inspired intelligence on neuromorphic chips with high energy efficiency.
We contribute a full-stack toolkit for pre-processing neuromorphic datasets, building deep SNNs, optimizing their parameters, and deploying SNNs on neuromorphic chips.
arXiv Detail & Related papers (2023-10-25T13:15:17Z) - To Spike or Not To Spike: A Digital Hardware Perspective on Deep
Learning Acceleration [4.712922151067433]
As deep learning models scale, they become increasingly competitive from domains spanning from computer vision to natural language processing.
The power efficiency of the biological brain outperforms any large-scale deep learning ( DL ) model.
Neuromorphic computing tries to mimic the brain operations to improve the efficiency of DL models.
arXiv Detail & Related papers (2023-06-27T19:04:00Z) - A Hybrid Neural Coding Approach for Pattern Recognition with Spiking
Neural Networks [53.31941519245432]
Brain-inspired spiking neural networks (SNNs) have demonstrated promising capabilities in solving pattern recognition tasks.
These SNNs are grounded on homogeneous neurons that utilize a uniform neural coding for information representation.
In this study, we argue that SNN architectures should be holistically designed to incorporate heterogeneous coding schemes.
arXiv Detail & Related papers (2023-05-26T02:52:12Z) - Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks.
Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
arXiv Detail & Related papers (2023-03-30T02:40:28Z) - Deep Reinforcement Learning Guided Graph Neural Networks for Brain
Network Analysis [61.53545734991802]
We propose a novel brain network representation framework, namely BN-GNN, which searches for the optimal GNN architecture for each brain network.
Our proposed BN-GNN improves the performance of traditional GNNs on different brain network analysis tasks.
arXiv Detail & Related papers (2022-03-18T07:05:27Z) - Towards efficient end-to-end speech recognition with
biologically-inspired neural networks [10.457580011403289]
We introduce neural connectivity concepts emulating the axo-somatic and the axo-axonic synapses.
We demonstrate for the first time, that a biologically realistic implementation of a large-scale ASR model can yield competitive performance levels.
arXiv Detail & Related papers (2021-10-04T21:24:10Z) - A brain basis of dynamical intelligence for AI and computational
neuroscience [0.0]
More brain-like capacities may demand new theories, models, and methods for designing artificial learning systems.
This article was inspired by our symposium on dynamical neuroscience and machine learning at the 6th Annual US/NIH BRAIN Initiative Investigators Meeting.
arXiv Detail & Related papers (2021-05-15T19:49:32Z) - A Spiking Neural Network Emulating the Structure of the Oculomotor
System Requires No Learning to Control a Biomimetic Robotic Head [0.0]
A neuromorphic oculomotor controller is placed at the heart of our in-house biomimetic robotic head prototype.
The controller is unique in the sense that all data are encoded and processed by a spiking neural network (SNN)
We report the robot's target tracking ability, demonstrate that its eye kinematics are similar to those reported in human eye studies and show that a biologically-constrained learning can be used to further refine its performance.
arXiv Detail & Related papers (2020-02-18T13:03:06Z)
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.