被忽视的情境敏感树突的作用

Mohsin Raza, Ahsan Adeel
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引用次数: 0

摘要

迄今为止,大多数树突研究主要集中在锥体两点神经元(TPNs)的顶端区,这些神经元只接受来自更高知觉层的反馈(FB)连接,并利用它们进行学习。最近的细胞神经生理学和计算神经科学研究表明,来自反馈和横向联系的顶端输入(上下文)是多方面的,而且远比以前认识到的更为多样,对大脑的持续学习和处理具有更大的影响。除 FB 外,顶端丘还接收来自同一网络中相邻细胞的信号,作为近端(P)上下文;接收来自大脑其他部分的信号,作为远端(D)上下文;接收来自整个网络的全部连贯信息,作为通用(U)上下文。整合上下文(C)分别放大和抑制一致性和冲突性前馈(FF)信号的传输。具体来说,我们证明了复杂语境敏感(CS)-TPNs 能灵活地将 C 与 FF 体电流在体瘤处逐时整合,当前馈(FF)和 C 相一致时,体电流被放大;反之,则被减弱。这样,只有当 FF 电流和 C 电流相干时,才会产生事件,然后根据 FB 信息将其转化为单次或脉冲串。尖峰模拟的结果表明,这种灵活地整合了意义电流和情境电流的方法能够传播更多的一致性信号(脉冲串),从而以更少的神经元提高学习速度。在传统人工网络中使用这种功能时,也能观察到类似的行为,在传统人工网络中,使用反向传播(BP)技术处理大量异构的真实世界视听(AV)数据所需的神经元数量要少得多。本文介绍的计算发现证明了CS-TPNs的普遍性,表明了一种以前被忽视的树突叙事。
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An Overlooked Role of Context-Sensitive Dendrites
To date, most dendritic studies have predominantly focused on the apical zone of pyramidal two-point neurons (TPNs) receiving only feedback (FB) connections from higher perceptual layers and using them for learning. Recent cellular neurophysiology and computational neuroscience studies suggests that the apical input (context), coming from feedback and lateral connections, is multifaceted and far more diverse, with greater implications for ongoing learning and processing in the brain than previously realized. In addition to the FB, the apical tuft receives signals from neighboring cells of the same network as proximal (P) context, other parts of the brain as distal (D) context, and overall coherent information across the network as universal (U) context. The integrated context (C) amplifies and suppresses the transmission of coherent and conflicting feedforward (FF) signals, respectively. Specifically, we show that complex context-sensitive (CS)-TPNs flexibly integrate C moment-by-moment with the FF somatic current at the soma such that the somatic current is amplified when both feedforward (FF) and C are coherent; otherwise, it is attenuated. This generates the event only when the FF and C currents are coherent, which is then translated into a singlet or a burst based on the FB information. Spiking simulation results show that this flexible integration of somatic and contextual currents enables the propagation of more coherent signals (bursts), making learning faster with fewer neurons. Similar behavior is observed when this functioning is used in conventional artificial networks, where orders of magnitude fewer neurons are required to process vast amounts of heterogeneous real-world audio-visual (AV) data trained using backpropagation (BP). The computational findings presented here demonstrate the universality of CS-TPNs, suggesting a dendritic narrative that was previously overlooked.
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