School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, United Kingdom
Learn Mem. 2024 Jun 11;31(5). doi: 10.1101/lm.053824.123. Print 2024 May.
The insect mushroom body has gained increasing attention as a system in which the computational basis of neural learning circuits can be unraveled. We now understand in detail the key locations in this circuit where synaptic associations are formed between sensory patterns and values leading to actions. However, the actual learning rule (or rules) implemented by neural activity and leading to synaptic change is still an open question. Here, I survey the diversity of answers that have been offered in computational models of this system over the past decades, including the recurring assumption-in line with top-down theories of associative learning-that the core function is to reduce prediction error. However, I will argue, a more bottom-up approach may ultimately reveal a richer algorithmic capacity in this still enigmatic brain neuropil.
昆虫的蘑菇体作为一个系统，其神经学习电路的计算基础已经引起了越来越多的关注，可以在此系统中揭示突触关联是如何在感觉模式和导致行动的价值之间形成的。然而，神经活动所执行的实际学习规则（或规则），以及导致突触变化的规则，仍然是一个悬而未决的问题。在这里，我调查了过去几十年中这个系统的计算模型所提供的各种答案，包括反复出现的假设，即核心功能是减少预测误差，这与联想学习的自上而下的理论一致。然而，我将认为，一种更自下而上的方法最终可能会在这个仍然神秘的大脑神经胶中揭示出更丰富的算法能力。
