Fusi Stefano, Senn Walter
Institute of Neuroinformatics, UNIZH/ETH, Wintherthurerstrasse 190, CH-8057 Zurich.
Chaos. 2006 Jun;16(2):026112. doi: 10.1063/1.2213587.
The variables involved in the equations that describe realistic synaptic dynamics always vary in a limited range. Their boundedness makes the synapses forgetful, not for the mere passage of time, but because new experiences overwrite old memories. The forgetting rate depends on how many synapses are modified by each new experience: many changes means fast learning and fast forgetting, whereas few changes means slow learning and long memory retention. Reducing the average number of modified synapses can extend the memory span at the price of a reduced amount of information stored when a new experience is memorized. Every trick which allows to slow down the learning process in a smart way can improve the memory performance. We review some of the tricks that allow to elude fast forgetting (oblivion). They are based on the stochastic selection of the synapses whose modifications are actually consolidated following each new experience. In practice only a randomly selected, small fraction of the synapses eligible for an update are actually modified. This allows to acquire the amount of information necessary to retrieve the memory without compromising the retention of old experiences. The fraction of modified synapses can be further reduced in a smart way by changing synapses only when it is really necessary, i.e. when the post-synaptic neuron does not respond as desired. Finally we show that such a stochastic selection emerges naturally from spike driven synaptic dynamics which read noisy pre and post-synaptic neural activities. These activities can actually be generated by a chaotic system.
描述现实突触动力学的方程中涉及的变量总是在有限范围内变化。它们的有界性使得突触具有遗忘性，并非仅仅因为时间的流逝，而是因为新的经历会覆盖旧的记忆。遗忘率取决于每次新经历对多少突触进行了修改：大量的变化意味着快速学习和快速遗忘，而少量的变化意味着缓慢学习和长期记忆保留。减少被修改突触的平均数量可以延长记忆跨度，但代价是在记忆新经历时存储的信息量减少。每一个能够以巧妙方式减缓学习过程的技巧都可以提高记忆性能。我们回顾一些能够避免快速遗忘（遗忘）的技巧。它们基于对突触的随机选择，这些突触的修改在每次新经历后实际上会被巩固。实际上，在符合更新条件的突触中，只有一小部分被随机选中的突触会被实际修改。这使得能够获取检索记忆所需的信息量，而不会损害旧经历的保留。通过仅在真正必要时，即当突触后神经元没有按预期反应时改变突触，可以以巧妙的方式进一步减少被修改突触的比例。最后我们表明，这种随机选择自然地源于由尖峰驱动的突触动力学，该动力学读取有噪声的突触前和突触后神经活动。这些活动实际上可以由一个混沌系统产生。
