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Quantifying neural information content: A case study of the impact of hippocampal adult neurogenesis

机译：量化神经信息含量：以海马成年神经发生影响为例

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Through various means of structural and synaptic plasticity enabling online learning, neural networks are constantly reconfiguring their computational functionality. Neural information content is embodied within the configurations, representations, and computations of neural networks. To explore neural information content, we have developed metrics and computational paradigms to quantify neural information content. We have observed that conventional compression methods may help overcome some of the limiting factors of standard information theoretic techniques employed in neuroscience, and allows us to approximate information in neural data. To do so we have used compressibility as a measure of complexity in order to estimate entropy to quantitatively assess information content of neural ensembles. Using Lempel-Ziv compression we are able to assess the rate of generation of new patterns across a neural ensemble's firing activity over time to approximate the information content encoded by a neural circuit. As a specific case study, we have been investigating the effect of neural mixed coding schemes due to hippocampal adult neurogenesis.
机译：通过支持在线学习的各种结构和突触可塑性，神经网络不断地重新配置其计算功能。神经信息内容体现在神经网络的配置，表示和计算中。为了探索神经信息内容，我们开发了度量和计算范例来量化神经信息内容。我们已经观察到，传统的压缩方法可能有助于克服神经科学中采用的标准信息理论技术的某些局限性因素，并使我们能够近似神经数据中的信息。为此，我们使用可压缩性作为复杂性的度量，以便估计熵以定量评估神经集成体的信息内容。使用Lempel-Ziv压缩，我们能够评估随着时间的推移，整个神经集合的激发活动中新模式生成的速率，以近似于由神经电路编码的信息内容。作为一个具体案例研究，我们一直在研究海马成年神经发生对神经混合编码方案的影响。

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