The effect of connectivity on information in neural networks,Integrative Biology

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The effect of connectivity on information in neural networks
Integrative Biology ( IF 1.5 ) Pub Date : 2018-02-02 , DOI: 10.1039/c7ib00190h
V. Onesto _{1,

2,

3,

4} , R. Narducci _{4,

5,

6} , F. Amato _{1,

2,

3,

4} , L. Cancedda _{4,

5,

6} , F. Gentile _{4,

7,

8,

9}

Affiliation

We present a mathematical model that quantifies the amount of information exchanged in bi-dimensional networks of nerve cells as a function of network connectivity Q. Upon varying Q over a significant range, we found that, from a certain cell density onwards, 90% of the maximal information transferred I(Q) in a random neuronal network is already reached with just 40% of the total possible connections Q among the cells. As a consequence, the system would not benefit from additional connections in terms of the amount of I(Q), in agreement with the tendency of brains to minimize Q because of its energetic costs. The model may reveal the circuits responsible for neurodegenerative disorders in that neurodegeneration can be regarded as a connective failure affecting information.

中文翻译：

连接性对神经网络中信息的影响

我们提出了一个数学模型，该模型可以量化在神经细胞的二维网络中作为网络连通性Q的函数交换的信息量。在相当大的范围内改变Q时，我们发现，从一定的细胞密度开始，随机神经元网络中已传递的最大信息I（Q）的90％已经达到，而在神经元网络中可能的总连接Q中只有40％细胞。结果，与大脑将Q最小化的趋势相一致，系统将不会从I（Q）的数量上受益于其他连接。由于其高昂的成本。该模型可能会揭示造成神经退行性疾病的电路，因为神经退行性变可被视为影响信息的结缔性衰竭。

更新日期：2018-02-02

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