Grossberg S, Levine D S
Appl Opt. 1987 Dec 1;26(23):5015-30. doi: 10.1364/AO.26.005015.
Selective information processing in neural networks is studied through computer simulations of Pavlovian conditioning data. The model reproduces properties of blocking, inverted-U in learning as a function of interstimulus interval, anticipatory conditioned responses, secondary reinforcement, attentional focusing by conditioned motivational feedback, and limited capacity short-term memory processing. Conditioning occurs from sensory to drive representations (conditioned reinforcer learning), from drive to sensory representations (incentive motivational learning), and from sensory to motor representations (habit learning).The conditionable pathwas contain long-term memory traces that obey a non-Hebbian associative law. The neural model embodies a solution to two key design problems of conditioning, the synchronization and persistence problems. This model of vertebrate learning is compared with data and models of invertebrate learning. Predictions derived from models of vertebrate learning are compared with data about invertebrate learning, including data from Aplysia about facilitator neurons and data from Hermissenda about voltage-dependent Ca(2+) currents. A prediction is stated about classical conditioning in all species, called the secondary conditioning alternative, and if confirmed would constitute an evolutionary invariant of learning.
通过对巴甫洛夫条件反射数据的计算机模拟,研究了神经网络中的选择性信息处理。该模型再现了阻塞、学习中作为刺激间隔函数的倒U形、预期条件反应、二级强化、条件性动机反馈的注意力聚焦以及有限容量的短期记忆处理等特性。条件作用发生在从感觉表征到驱力表征(条件性强化物学习)、从驱力表征到感觉表征(激励动机学习)以及从感觉表征到运动表征(习惯学习)的过程中。可条件化路径包含遵循非赫布联想定律的长期记忆痕迹。该神经模型体现了对条件反射的两个关键设计问题,即同步和持久性问题的解决方案。将这种脊椎动物学习模型与无脊椎动物学习的数据和模型进行了比较。从脊椎动物学习模型得出的预测与无脊椎动物学习的数据进行了比较,包括来自海兔的关于易化神经元的数据和来自海兔属的关于电压依赖性Ca(2+)电流的数据。提出了一个关于所有物种经典条件反射的预测,称为二级条件反射替代方案,如果得到证实,将构成学习的进化不变量。