Sánchez José M, Galeazzi Juan M, Burgos José E
University of Guadalajara - CEIC, Francisco de Quevedo 180, Col. Arcos de Vallarta, Guadalajara, Jalisco 44130, Mexico.
Behav Processes. 2010 May;84(1):526-35. doi: 10.1016/j.beproc.2010.01.018. Epub 2010 Jan 29.
This paper investigates the possible role of neuroanatomical features in Pavlovian conditioning, via computer simulations with layered, feedforward artificial neural networks. The networks' structure and functioning are described by a strongly bottom-up model that takes into account the roles of hippocampal and dopaminergic systems in conditioning. Neuroanatomical features were simulated as generic structural or architectural features of neural networks. We focused on the number of units per hidden layer and connectivity. The effect of the number of units per hidden layer was investigated through simulations of resistance to extinction in fully connected networks. Large networks were more resistant to extinction than small networks, a stochastic effect of the asynchronous random procedure used in the simulator to update activations and weights. These networks did not simulate second-order conditioning because weight competition prevented conditioning to a stimulus after conditioning to another. Partially connected networks simulated second-order conditioning and devaluation of the second-order stimulus after extinction of a similar first-order stimulus. Similar stimuli were simulated as nonorthogonal input-vectors.
本文通过使用分层前馈人工神经网络进行计算机模拟，研究神经解剖学特征在巴甫洛夫条件反射中可能发挥的作用。网络的结构和功能由一个强自下而上的模型描述，该模型考虑了海马体和多巴胺能系统在条件反射中的作用。神经解剖学特征被模拟为神经网络的一般结构或架构特征。我们关注每个隐藏层的单元数量和连接性。通过对全连接网络中消退抗性的模拟，研究了每个隐藏层单元数量的影响。大型网络比小型网络更能抵抗消退，这是模拟器中用于更新激活和权重的异步随机过程的一种随机效应。这些网络没有模拟二阶条件反射，因为权重竞争阻止了在对另一个刺激形成条件反射后对某个刺激形成条件反射。部分连接网络模拟了二阶条件反射以及在类似的一阶刺激消退后二阶刺激的贬值。相似刺激被模拟为非正交输入向量。
