Synaptic state matching: a dynamical architecture for predictive internal representation and feature detection.

Synaptic state matching: a dynamical architecture for predictive internal representation and feature detection.
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DOI:
10.1371/journal.pone.0072865
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发表时间:
2013
期刊:
影响因子:
3.7
通讯作者:
Tavazoie S
Tavazoie S
中科院分区:
综合性期刊3区
文献类型:
--
作者:
Tavazoie S

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在这里,我们探讨的可能性,感觉皮层的核心功能是实时的感觉环境的内部模拟的生成。对这一想法的逻辑阐述导致了一种动态神经结构,它在两种基本网络状态之间振荡,一种由外部输入驱动,另一种由没有感觉输入的递归突触驱动。突触强度修改的建议突触状态匹配(SSM)的过程,确保两个网络状态之间的尖峰统计的等效性。值得注意的是,SSM在单个突触上本地运行,生成准确和稳定的网络级预测内部表示,使模式完成和无监督的特征检测从嘈杂的感觉输入。SSM是学习和记忆的生物学上合理的基底,因为它将序列学习、特征检测、突触稳态和网络振荡结合在一个统一的计算框架下。
Here we explore the possibility that a core function of sensory cortex is the generation of an internal simulation of sensory environment in real-time. A logical elaboration of this idea leads to a dynamical neural architecture that oscillates between two fundamental network states, one driven by external input, and the other by recurrent synaptic drive in the absence of sensory input. Synaptic strength is modified by a proposed synaptic state matching (SSM) process that ensures equivalence of spike statistics between the two network states. Remarkably, SSM, operating locally at individual synapses, generates accurate and stable network-level predictive internal representations, enabling pattern completion and unsupervised feature detection from noisy sensory input. SSM is a biologically plausible substrate for learning and memory because it brings together sequence learning, feature detection, synaptic homeostasis, and network oscillations under a single unifying computational framework.
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