computational biology blog 2024年11月27日
Functional Principles of Neural Plasticity
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本文提出了一种名为水平垂直整合理论的新型记忆理论,挑战了传统的突触可塑性理论。该理论认为神经元拥有内部和外部参数,外部参数影响神经元之间的水平交互,而内部和外部参数之间的信息交换则为垂直整合。这种理论更符合生物学事实,也更利于计算,因为它拥有更强的稳定性和持久性,并能结合感知与现有知识,提供快速反应和稳定性。此外,该理论强调了离子交换,尤其是钙离子,在连接内外参数中的重要作用,以及核参数和细胞质参数之间的缓慢交换。总而言之,水平垂直整合理论为理解记忆和认知提供了一个新的框架,并具有潜在的计算应用价值。

🤔 **水平垂直整合理论的核心:**该理论认为每个神经元都具有内部和外部参数,其中外部参数主要通过电生理学影响神经元之间的水平交互,而内部和外部参数之间的信息交换则构成垂直整合,形成一个双向的信息处理系统。

💡 **突触可塑性理论的局限性:**传统突触可塑性理论缺乏稳定性和持久性,难以构建结构,所有信息都在不断变化的细胞膜上,难以解释记忆的稳定性和持久性。

🧠 **离子交换在记忆中的作用:**离子交换,特别是钙离子,是连接神经元外部膜和内部细胞质参数的重要途径,可能也是最快的途径,为记忆形成和信息传递提供了关键机制。

🧬 **核参数和细胞质参数的缓慢交换:**神经元的核参数和细胞质参数之间也存在信息交换,但速度较慢,涉及转录因子等蛋白质,这为记忆的长期稳定性提供了保障。

⏱️ **快速反应和稳定性的结合:**水平垂直整合理论提出的系统,外部具有快速的突触可塑性,内部则具有更缓慢、持久的可塑性,这使得系统兼具快速反应能力和稳定性,是现有计算架构所不具备的。

It is really difficult to conceptualize things in a novel way – when one has been conditioned for decades to believe in the synaptic plasticity theory of memory.
I offer a new type of theory, first outlined in the vision statement linked above, which is called a horizontal-vertical integration theory. There could be several such theories. All such theories would agree that each neuron has internal and external parameters, and only external parameters influence their horizontal interactions with other neurons (mostly by electrophysiology). The exchange of information between the membrane (external) and internal zones is the vertical integration. The horizontal integration uses contact points (synapses) on the membrane, in highly plastic environments with spines as compartmentalized vertical integration sites.
In addition to being more adequate for the biological facts, this will be friendly for computation as well. Synaptic plasticity doesn’t have much stability or permanence and we can’t build structure. All information is at the membrane where it is constantly changed. That is a huge problem.
The vision statement above already contains a very specific theory. In the background it is understood that spatiotemporal spike timing patterns are the representations, which is where patterns, perceptions and thoughts reside. But those representations use the existing neurons with their own plasticity. Thus representations are not only signal-driven, they integrate perceptions with existing knowledge. And the neurons’ individual memories influence the patterns that result from ongoing sensations.
Ion exchange, especially calcium, is quite important for linking external (membrane) and internal (submembrane, cytosolic) parameters. It is quite apparent from the experimental literature that this is a major gateway, probably the fastest. There is also exchange between the core, nuclear parameters, and the internal parameters in the cytosol (including the spine). These are slower exchanges involving proteins like transcription factors. Noticeably we have a system with fast plasticity on the outside and slower, lasting plasticity distant from external signaling. Such a system offers both stability and fast reactivity, and is unique in its properties compared to existing computing architectures.
It will be more work to flesh this out.
Theories are not true, they are useful. Only the individual fact can be established as true. But theories need to anchor individual facts, and a horizontal-vertical integration theory has the potential to cover a large amount of what is known. At the same time, its computing abilities are fascinating. LTP/LTD has outlived its usefulness.

aianesthesiaBayescell assemblyCellular intelligenceconsciousnesscortexcortical microcolumnscritical periodcyberneticsdopamineelectrophysiologyensemblesepigeneticsfeature learningfeedbackheavy-tailed distributionshierarchyinhibitionion channelsknowledgelearningLTDLTPmemorymodelsmodulesmousenetworkneural codingneural plasticityneuronperceptionpredictive codingprocessorpsf systemsignal transductionspikingstimulation protocolsynapsesSynaptic Plasticitytheoriestransfer functionsTuring patternsvertical-horizontal

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水平垂直整合理论 记忆 神经元 突触可塑性 计算模型
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