基于上下游脑区深度学习模型得到强迫症、选择困难症和偏见的猜想
The conjectures of Obsessive-compulsive disorder, Difficult decisions and Prejudice based on the Deep learning model for upstream and downstream brain regions
参考DSM-5的强迫症、选择困难症的相关文字，尝试用上下游脑区深度学习模型进行模拟。我们补充选择困难症一种新情况，前向传播可能在中途返回上游脑区的前额叶。对于偏见的深度学习模型，不理想的数据可能跳过了前额叶；但喜欢的数据经过前额叶后，会跳过带有情绪记忆的脑区。
Referring to the DSM-5 Obsessive-compulsive disorder and Difficult decisions, try to simulate it with the Deep learning model for upstream and downstream brain regions. We supplement the new case of Difficult decisions, and the forward propagation may return to the prefrontal lobe of upstream brain regions at halfway. For Prejudice Deep learning model, undesirable data may skip the prefrontal lobe; But favorite data passes through the prefrontal lobe and skips brain regions with emotional memories.
1强迫症模型
1 The model of Obsessive-compulsive disorder
我们猜想强迫症的模型有两种情况，都和前额叶皮层有关。
一种是过于相信自己较好的经历和急躁，使得前额叶的突触连接权重和范围权重的前向计算陷入局部最优，见图1和7。图1的权重更新参考了局部优权重，较少参考差的权重很容易陷入局部最优。
另一种是童年受到的心灵创伤和不幸经历，使得前额叶的前向计算结果不好，见图2和7。图2的权重更新参考了局部差权重，较少参考优的权重使得计算结果不好。
图7前额叶皮层过于薄。上游皮层的较大计算误差累积到下游皮层需要更多的情绪才能跳出局部最优，进而产生更多的焦虑。
We guess that the model of obsessive-compulsive disorder has two conditions, both related to the prefrontal cortex.
One is to believe too much in their own good experience and impatience, so that the forward propagation of synaptic connection and range weights of the prefrontal lobe falls into local optimum, see Fig. 1 and 7. The weight update in Fig. 1 refers to the local good weights, and with less reference inferior weights can easily fall into the local optimal.
The other is the trauma and unfortunate experience of childhood, which makes the forward propagation of the prefrontal lobe poor, as shown in Fig. 2 and 7. The weight update in Fig. 2 refers to the local inferior weights, and with less reference to the good weights make the calculation results poor.
The prefrontal cortex is too thin in Fig. 7. Too large computational errors in the upstream cortexes accumulate to the downstream cortexes and require more emotions to jump out of the local optimum, which in turn produces more anxiety.
2选择困难症模型
2 The model of Difficult decisions
而选择困难症是另一种相反情况，也和前额叶皮层有关。
考虑过多的不利情况、有利情况、约束条件和期待过高，特别是不利情况使得上游皮层权重范围较广，搜索效率低也使得上游皮层前向计算不好，见图3和8。
图8前额叶皮层过于厚。上游区更多皮层的海森矩阵进行反向传播，使得算法空间复杂度增多。上游皮层的较大计算误差累积到下游皮层需要更多的情绪才能跳出局部最优，进而产生更多的焦虑。
图4和6是正常的前额叶神经元分布及其皮层厚度。图5和6是更理智的神经元分布及其皮层厚度。
我们考虑一种新的情况，由于选择困难，前向计算的中途返回上游脑区的前额叶，局部陷入死循环。一般而言，前向计算的中途将会到达下游脑区，见图8的紫色箭头。
The Difficult decisions is another opposite situation, also related to the prefrontal cortex.
Considering too many unfavorable situations, favorable situations, the constraints and expectations is too high, especially the unfavorable situations make the range of upstream cortexes weights wider, and the low search efficiency also makes the upstream cortexes forward propagation poor, see Fig. 3 and 8.
The prefrontal cortex is too thick in Fig. 8. The Heisen matrix with more cortexes in the upstream regions is calculated by back propagation, which increases the space complexity of the algorithm. Too large computational errors in the upper cortex accumulate to the downstream cortexes and require more emotion to jump out of the local optimum, which in turn generates more anxiety.
Fig. 4 and 6 show the normal prefrontal neurons distribution and their cortical thickness. Fig. 5 and 6 show a more rational distribution of neurons and their cortical thickness.
We consider a new situation where, due to the Difficult decisions, the forward propagation returns to the prefrontal lobe of the upstream brain regions at halfway, locally caught in an endless loop. In general, the forward propagation will reach to the downstream brain regions at halfway, as shown in Fig. 8.
Fig.1 Distribution of neurons at OCD situation 1, past good experience and impatience
Fig.2 Distribution of neurons at OCD situation 2, childhood misfortune
Fig.3 Distribution of neurons at Difficult decisions
Fig.4 Normal IQ
Fig.5 High IQ
Fig.6 Normal brain regions
Fig.7 Brain regions of OCD
(a) The prefrontal cortex is too thick
(b) The forward propagation returns to the prefrontal lobe
Fig.8 Brain regions of Difficult decisions
3 偏见的模型
3 The model of Prejudice
对于偏见的深度学习模型，不理想的数据可能跳过了前额叶，到达带有情绪记忆的脑区；但喜欢的数据经过前额叶后，跳过带有情绪记忆的脑区。一种是低估目标函数因为缺乏高阶优化简单信号没有很快收敛；一种是高估目标函数因为缺乏情绪记忆之后复杂信号无法跳出局部最优。两种情况见图9，不理想的数据和喜欢的数据的前向计算路径见图中的紫色箭头。
For Prejudice Deep learning model, undesirable data may skip the prefrontal lobe and reach brain regions with emotional memories; But the favorite data passes through the prefrontal lobe, skipping the brain region with emotional memory. One is to underestimate the objective function because of the lack of higher-order optimization, and the simple signals do not converge quickly. One is to overestimate the objective function, because of lack of emotional memory, complex signals cannot jump out of the local optimum. Both cases are shown in Fig. 9, the forward propagation path for undesirable data and favorite data are shown in the purple arrows in the figure.
(a) Undesirable data
(b) Favorite data
Fig.9 Brain regions of Prejudice
作者：陶俊波， 哈工大在读博士。
