利用尖峰神经元和前馈要点信号进行预测编码

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-04-12 DOI:10.3389/fncom.2024.1338280
Kwangjun Lee, Shirin Dora, Jorge F. Mejias, Sander M. Bohte, Cyriel M. A. Pennartz
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引用次数: 0

摘要

预测编码(PC)是神经科学领域颇具影响力的一种理论,它认为大脑皮层存在一种不断生成和更新感官输入预测表征的结构。由于其层次性和生成性,PC 激发了文献中的许多感知计算模型。然而,由于现有模型使用的是人工神经元,这些神经元以连续时域的发射率近似神经活动,并同步传播信号,因此这些模型的生物学合理性尚未得到充分探讨。因此,我们开发了预测编码尖峰神经网络(SNN-PC),其中神经元使用事件驱动和异步尖峰进行通信。SNN-PC 采用了之前 PC 神经网络模型的分层结构和希比安学习算法,并引入了两个新特性:(1) 从输入到高级区域的快速前馈扫频,可生成空间缩小的抽象输入表示(即场景要点的神经代码),为任意选择先验提供了一种神经生物学替代方案;(2) 正负误差计算神经元的分离,解决了具有极高基线发射率的双向误差神经元在生物学上的不可信性。在使用 MNIST 手写数字数据集进行训练后,SNN-PC 开发出了分层内部表征,并能重建它在训练期间未见过的样本。SNN-PC 提出了大脑以无监督方式进行感知推理和学习的生物学机制。此外,它还可用于神经形态应用,利用其节能、事件驱动、局部学习和并行信息处理的特性。
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Predictive coding with spiking neurons and feedforward gist signaling
Predictive coding (PC) is an influential theory in neuroscience, which suggests the existence of a cortical architecture that is constantly generating and updating predictive representations of sensory inputs. Owing to its hierarchical and generative nature, PC has inspired many computational models of perception in the literature. However, the biological plausibility of existing models has not been sufficiently explored due to their use of artificial neurons that approximate neural activity with firing rates in the continuous time domain and propagate signals synchronously. Therefore, we developed a spiking neural network for predictive coding (SNN-PC), in which neurons communicate using event-driven and asynchronous spikes. Adopting the hierarchical structure and Hebbian learning algorithms from previous PC neural network models, SNN-PC introduces two novel features: (1) a fast feedforward sweep from the input to higher areas, which generates a spatially reduced and abstract representation of input (i.e., a neural code for the gist of a scene) and provides a neurobiological alternative to an arbitrary choice of priors; and (2) a separation of positive and negative error-computing neurons, which counters the biological implausibility of a bi-directional error neuron with a very high baseline firing rate. After training with the MNIST handwritten digit dataset, SNN-PC developed hierarchical internal representations and was able to reconstruct samples it had not seen during training. SNN-PC suggests biologically plausible mechanisms by which the brain may perform perceptual inference and learning in an unsupervised manner. In addition, it may be used in neuromorphic applications that can utilize its energy-efficient, event-driven, local learning, and parallel information processing nature.
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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
期刊最新文献
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