Additive neurogenesis as a strategy for avoiding interference in a sparsely-coding dentate gyrus
Additive neurogenesis as a strategy for avoiding interference in a sparsely-coding dentate gyrus
复制标题
DOI：
10.1080/09548980902993156
复制
发表时间：
2009-01-01
影响因子：
7.8
通讯作者：
Wiskott, Laurenz
中科院分区：
文献类型：
--
作者：
Appleby, Peter A.;Wiskott, Laurenz
最近，我们提出了一种线性前馈神经网络中的加性神经发生模型，该模型在不断变化的输入环境中执行编码-解码记忆任务。随着时间的推移，神经网络的增长使得网络能够适应输入统计数据的变化，而不会破坏检索特性，并且我们提出，成人神经发生可能在海马齿状回中发挥类似的计算作用。在这里，我们通过检查简化的海马记忆模型中的附加神经发生来明确评估这一假设。该模型结合了从内嗅皮层到齿状回的单位数量差异以及齿状回的稀疏编码，这都是海马处理的显着特征。我们评估了两种不同的适应策略；神经元周转，其中网络具有固定大小，但可以删除单元并添加新的单元，以及附加神经发生，其中网络随着时间的推移而增长，并量化网络在整个适应水平范围内的性能，从固定网络中的零到完全适应网络中的一。我们发现加性神经发生总是优于神经元周转，因为它允许网络对输入统计数据的变化做出响应，同时保留早期环境的表示。
Recently we presented a model of additive neurogenesis in a linear, feedforward neural network that performed an encoding-decoding memory task in a changing input environment. Growing the neural network over time allowed the network to adapt to changes in input statistics without disrupting retrieval properties, and we proposed that adult neurogenesis might fulfil a similar computational role in the dentate gyrus of the hippocampus. Here we explicitly evaluate this hypothesis by examining additive neurogenesis in a simplified hippocampal memory model. The model incorporates a divergence in unit number from the entorhinal cortex to the dentate gyrus and sparse coding in the dentate gyrus, both notable features of hippocampal processing. We evaluate two distinct adaptation strategies; neuronal turnover, where the network is of fixed size but units may be deleted and new ones added, and additive neurogenesis, where the network grows over time, and quantify the performance of the network across the full range of adaptation levels from zero in a fixed network to one in a fully adapting network. We find that additive neurogenesis is always superior to neuronal turnover as it permits the network to be responsive to changes in input statistics while at the same time preserving representations of earlier environments.