Developing neural networks with neurons competing for survival

Zhen Peng, Daniel A. Braun
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Abstract

We study developmental growth in a feedforward neural network model inspired by the survival principle in nature. Each neuron has to select its incoming connections in a way that allow it to fire, as neurons that are not able to fire over a period of time degenerate and die. In order to survive, neurons have to find reoccurring patterns in the activity of the neurons in the preceding layer, because each neuron requires more than one active input at any one time to have enough activation for firing. The sensory input at the lowest layer therefore provides the maximum amount of activation that all neurons compete for. The whole network grows dynamically over time depending on how many patterns can be found and how many neurons can maintain themselves accordingly. If a neuron has found a stable firing pattern, a new neuron is created in the same layer. It is also made sure that there is always at least one neuron in each activated layer that is searching for novel patterns. If a layer stops growing for a certain amount of time, a new layer is created starting with a single neuron.
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发展神经元竞争生存的神经网络
我们研究发育生长的前馈神经网络模型的灵感来自自然界的生存原则。每个神经元都必须以一种允许其放电的方式选择进入的连接,因为在一段时间内不能放电的神经元会退化并死亡。为了存活,神经元必须在前一层神经元的活动中找到重复出现的模式,因为每个神经元在任何时候都需要不止一个活跃输入来获得足够的激活。因此,最低层的感觉输入提供了所有神经元竞争的最大激活量。整个网络随着时间的推移动态增长,这取决于可以找到多少模式,以及有多少神经元可以相应地维持自己。如果一个神经元找到了一个稳定的放电模式,在同一层就会产生一个新的神经元。它还确保在每个激活层中至少有一个神经元在寻找新的模式。如果一个神经元层停止生长一段时间,就会从一个神经元开始形成一个新的神经元层。
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