BIC: Neural Computation That Supports Multiple Cognitive Tasks

大脑可用的计算资源可以用一系列数字参数来概括。这些数据包括神经元的数量，每个神经元与之突触的平均神经元数量，以及神经元对其输入的基本反应时间。第四个参数是神经元通过其突触对与其突触的典型神经元产生的影响的强度。鉴于大脑拥有的资源有限，正如目前所理解的这四个参数所总结的那样，事实证明，解释大脑如何执行甚至最基本的信息处理任务是非常具有挑战性的。特别是，连接模式的稀疏性加上一般较弱的突触似乎施加了非常大的限制。拟议研究的目的是利用抽象的计算模型，探索在这些限制条件下信息处理是如何可能的。特别是，挑战是解释如何在这样的系统中同时支持多项任务，如记忆、联想和归纳学习。

The computational resources available to the brain can be summarized in terms of a number of numerical parameters. These include the number of neurons, the average number of neurons each neuron synapses with, and the basic response time of a neuron to its inputs. A fourth parameter is the strength of influence that a neuron has through its synapses on a typical neuron with which it synapses. Given the limited resources that the brain has, as summarized by these four parameters as currently understood, it turns out to be highly challenging to explain how the brain can perform even the most basic information processing tasks. In particular, the sparsity of the interconnection pattern in conjunction with generally weak synapses appears to impose very considerable constraints. The aim of the proposed research is to explore, using abstract computational models, how information processing is possible at all under these constraints. In particular the challenge is to explain how multiple tasks, such as memorization, association and inductive learning, can be supported together in such a system.