Sullivan D W, Levy W B
Department of Neurosurgery, University of Virginia Health System, PO Box 800420, Charlottesville, VA 22908, USA.
Network. 2004 Feb;15(1):45-67.
Despite the fact that animals are not optimal, natural selection is an optimizing process that can readily control small bits and pieces of organisms. It is for this reason that we need to explain certain parameters as found in Nature (e.g., number of neurons and their average activity) to fully understand the biological basis of cognition. In this optimizing sense, the failure of quantal synaptic transmission is problematic because this process incurs information loss at each synapse which seems like a bad thing for information processing. However, recent work based on an information-theoretic analysis of a single neuron suggests that such losses can be tolerated and lead to energy savings. Here we study computational simulations of a hippocampal model as a function of failure rate. We find that the failure process actually enhances some indices of performance when the model is required to solve the hippocampally dependent task of transverse patterning or when it is required to learn a simple sequence. Adding the random process of synaptic failures to the recurrent CA3-to-CA3 excitatory connections results in simulations that are more robust to parametric settings. Not only is the model more robust when synaptic failures are part of the model but there is a notable increase of sequence length memory capacity. Also, the failure process combined with additional neurons allows lower activity settings while still remaining compatible with learning the transverse patterning task. Indeed, as neuron number tended towards the biological numbers (nearly 5 x 10(4) in the simulations), it was not only possible to achieve biological failure rates (55-85%) at the minimally tolerated activity setting but these appropriately high failure rates were required for successful learning. The results are interpreted in terms of previous research demonstrating that randomization during training can enhance performance by facilitating implicit state-space search for interconnected neurons.
尽管动物并非理想的研究对象，但自然选择是一个优化过程，能够轻松控制生物体的各个小部分。正因如此，我们需要解释自然界中发现的某些参数（例如神经元数量及其平均活动），以便全面理解认知的生物学基础。从这种优化意义上讲，量子突触传递的失败是个问题，因为这个过程在每个突触处都会导致信息丢失，这对于信息处理而言似乎是件坏事。然而，最近基于对单个神经元的信息论分析的研究表明，这种信息丢失是可以容忍的，并且能节省能量。在此，我们研究海马体模型的计算模拟作为失败率的函数。我们发现，当模型需要解决依赖海马体的横向模式任务或学习简单序列时，失败过程实际上会提高某些性能指标。在CA3到CA3的循环兴奋性连接中加入突触失败的随机过程，会使模拟对参数设置更具鲁棒性。当突触失败作为模型的一部分时，不仅模型更具鲁棒性，而且序列长度记忆容量会显著增加。此外，失败过程与额外的神经元相结合，能够在保持与学习横向模式任务兼容的同时，实现更低的活动设置。实际上，随着神经元数量趋向于生物学上的数量（模拟中接近5×10⁴），不仅有可能在最低容忍活动设置下达到生物学上的失败率（55 - 85%），而且成功学习需要这些适当高的失败率。这些结果是根据先前的研究来解释的，该研究表明训练期间的随机化可以通过促进对相互连接神经元的隐式状态空间搜索来提高性能。
