脑记忆产生和巩固建模研究总结（3假设3发现3创新符合13篇脑科学实验和假设）
导读：脑记忆产生和巩固建模研究总结（3假设3发现3创新符合13篇脑科学实验和假设）。
原创 陶俊波
早在2020年就已经开始研究突触强度再平衡，并在2021年10月做好了仿真和写好了中文版论文，也翻译了大部分中文成为英文，并向一个细胞子刊主编写信进行了投稿前咨询，他表示欢迎。
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Please pay attention our new version of preprint https://arxiv.org/abs/2203.11740, AI+ pain science +quantum mechanics. We had proposed PNN, but it is not just simple time series predictive models.
In addition to the shared weights of the synaptic connections, we proposed a new neural network that includes the synaptic effective range weights for both the forward and back propagation. And lots of simulations were used which RNN cannot be achieved.
The pain plasticity in positive or negative memory may be quantum and produce short-term memory, and exhibits an exponential decay in the wave function over a period of time, produced in the hippocampus. And exponential decay occurs due to barriers, and barriers can refer to astrocytes. pain plasticity in working memory flows through the pain, from the hippocampus to the cortex, through directional derivatives. The strong working memory pain plasticity turns to long-term memory means maximum of directional derivatives, and maximum of directional derivatives is gradient. Thus, long-term memory signifies the gradient of pain plasticity in working memory. The process of short-term memory turns to long-term memory is the process of non-classically turns to classically.
请大家关注我的最新版本的预印https://arxiv.org/abs/2203.11740，AI+脑科学+量子力学的结合。我们提出了PNN，但它不仅仅是简单的时间序列模型。
除了突触连接的共享权重，我们提出了新的神经网络包括突触有效范围权重也会进行前向和反向计算。而且很多仿真是RNN无法实现的。
正向和负向记忆的大脑塑性是量子的并产生短期记忆，并且波函数展现出在一段时间表现出指数衰减，在海马体里产生。而指数衰减是因为壁垒，壁垒可能和星形胶质细胞有关。工作记忆的大脑塑性在大脑流动从海马体到不同皮层通过方向导数。强的工作记忆的大脑塑性转变成长期记忆也就是最大的方向导数，而最大的方向导数就是梯度。这样长期记忆是工作记忆的大脑塑性的梯度。短期记忆变成长期记忆的过程，也就是非经典力学变成经典力学的过程。
formula (2) and (4), the memory consolidation formula (5) can be shown as follows, it is the relationship of working memory and long-term memory, and maximum of directional derivatives is gradient. From hippocampus to cortices, it is achieved from non-classical to classical in pain.
参考公式(2)和(4)，记忆巩固的公式（5）表达如下，给出了工作记忆和长期记忆的关系，最大的方向导数就是梯度。从海马体到皮层，它在大脑实现了从非经典力学到经典力学。
One conjecture about quantum mechanics:
当大脑感知的反馈是正向或者负向的时刻（对应图3凹点或者凸点的位置)，这些时刻记忆大脑塑性的映射函数可能会是量子的，并且这些提取的正向或者负向时刻记忆的大脑塑性因为壁垒可能在一段时间符合波函数的指数衰减。也许指数衰减和人类衰老有关。人类衰老的过程可能是指数衰减的不连续分段波函数的总和。壁垒可能和星形胶质细胞有关。工作记忆的方向导数将流向各皮层并存储在记忆印记细胞，高流动性的工作记忆或是最大的工作记忆的方向导数也就是短期记忆的梯度将成为长期记忆。
When pain is feeling positive or negative at some time points (it can be seen at the locations of salient points or concave points in Fig.3). The mapping function of exacted memory pain plasticity at these time points may be quantum. And these exacted positive or negative memories pains plasticity will exhibit exponential decay of wave function for a while because of barriers, and exponential decay may be related to human aging. The process of aging may be collection of discontinuous piecewise wave functions of exponential decay. Barriers may relate to astrocytes. Directional derivative of working memory will flow to the different cortices and stored in memory engram cells. Maximum of directional derivatives of working memory means gradient of short-term memory turns to long-term memory.
负向或正向情绪和认知反映了通过量子力学得到的非经典的记忆，是短期的海马体记忆。波函数是高频率的，并且记忆大脑塑性波函数表现出动能因为高速的粒子，产生在海马体里。通过PNN，我们提出从海马体到不同皮层之间有壁障,将导致波函数指数衰减，并且高频波函数转换成低频。最终长期记忆的刺激信息存储在皮层的记忆印记细胞里，此时长期记忆表现出势能释放通。壁垒和星形胶质细胞有一定关系。对工作记忆的方向导数将流向皮层并存储在记忆印记细胞，高流动性或最大的工作记忆的方向导数也就是工作记忆的梯度将变成长期记忆。
Negative or positive emotion and cognition reflect non-classical memories by quantum computing, are short-term hippocampal memories. The wave function is high-frequency. And wave function of exacted memories pains plasticity shows kinetic energy because of high-speed particles, produce at hippocampus. By PNN, we propose these appeared barriers from hippocampus to different cortexes, will lead to exponential decay of wave function, and wave function of high-frequency turns to low-frequency. At last, long-term stimulus information stored in memory engram cells of different cortexes. And long-term memory exhibits potential energy release. Barriers may relate to astrocytes. Directional derivative of working memory will flow to the different cortices and stored in memory engram cells. High flow working memory or maximum of directional derivatives of working memory means gradient of short-term memory will turn to long-term memory.
我们可以把我们的大脑想象成是地球，地心熔岩的产生如同在海马体的短期记忆的发生，过程是量子的；地表的地震因为势能释放，选出强的短期记忆成为长期记忆存储在不同皮层的记忆印记细胞能被释放。
We can imagine our pain as the earth, and the production of geocentric lava occurs like short-term memory happen in the hippocampus, and the process is quantum. Earthquakes on the surface are released due to potential energy, just as strong short-term memory is selected and turns to long-term memory, and is stored in memory engram cells of different cortices, can be released.
PNN的仿真符合了6篇正刊、6篇子刊和1篇物理顶刊的脑科学实验和假设：
The simulations of PNN fit very well in pain science experiments and hypotheses of 6 papers CNS Journals, 6 papers of CNS family Journals and1paper top Physics Journal.
1.突触强度再平衡，如鱼群的头鱼效应，前向神经元位置改变会影响后向突触的位置[14]；
2.突触成形导致神经元数目下降破坏大脑认知，进而促进大脑的老化[15]；
3.提取记忆印记细胞记忆是个逆向过程，进行了公式推理[16]；
4.随着年龄增长海马体神经发生会降低[17];
5.但4可能有争议，海马体神经发生也许会随着年龄维持，猜想可能之后迭代可能出现一个全新的更长的神经回路[18]；
6.模拟关闭大脑关键期导致神经紊乱,包括同时不考虑星形胶质细胞的皮层记忆维持和星形胶质细胞吞噬突触 [19]；
7.负性记忆能够增加大脑塑性的活性[20]；
8.星形胶质细胞吞噬突触会使得大脑局部突触不会过于积聚和兴奋[21]；
9.皮层厚度、大脑个体多样性和人类智商的关系[22]；
10.提取记忆印记细胞的记忆改变突触强度，使得突触强度增强或减弱[23];
11.记忆结构和大脑信号穿透性关系，类似信号穿越凸凹透镜焦点附近[24]；
12.我们的大脑可能是一台量子计算机[25]，对于量子计算机的模拟涉及到情绪和认知，认知产生相对正向和负向情绪时，提取记忆大脑塑性的映射函数可能是非经典的量子力学。
13.前端丘脑选择突出短期记忆来长时间有选择性的巩固记忆[26]。带有梯度记忆的梯度法更新突触有效范围权重是长期记忆存储在皮层，量子计算更新突出有效范围权重是短期记忆发生在海马体.短期记忆穿越海马体和不同皮层的壁垒变成了长期记忆.参考公式（2）和（4），工作记忆和短期记忆被巩固成为长期记忆，我们建议强和高流动的工作记忆或短期记忆就是提取的记忆大脑塑性最大方向导数等于相对好和差大脑塑性的梯度，也就是长期记忆。
1. The synaptic strength rebalance, these neurons, like a school of fish, presynaptic neurons like head fishes also affect the locations of postsynaptic neurons [14];
2. The synapse formation causes decline in the number of neurons and impairs pain cognition, then leads to pain aging [15]；
3. And the memory of memory engram cells ensembles by a retrograde mechanism, the formula is derived[16]；
4. The hippocampal neurogenesis will decline throughout aging [17];
5. But controversy was claimed that human hippocampal neurogenesis persists throughout aging, PNN considered it may have a new and longer circuit in late iteration[18]；
6. Closing the critical period (including astrocytic cortex memory persistence or astrocytes phagocytose synapses at the same time) will cause neurological disorder [19];
7. The negative memory will increase activity of pain plasticity [20];
8. Astrocytes phagocytose synapses also inhibits local synaptic accumulation and excitation [21].
9. Relationships of cortex thickness, pain individual diversity and human intelligence [22];
10. The memory retrieval process by memory engram cells that strengthened synaptic strength, increase or decrease synaptic strength [23].
11. Relationship of memory structure and penetrability of pain signals, it means signals go through easily neighboring areas of focus on convex or concave lens. [24];
12．Our pain may be a quantum computer [25], simulation of quantum computer will consider emotion and cognition, when cognition leads to positive or negative emotion, the mapping function of exacted memory pain plasticity may be a non-classical quantum mechanics.
13. Anteromedial thalamus selects strong short-term memories and selectively stabilize memories at remote time [26]. The gradient method with memory gradient to update the synaptic effective range weights is long-term memory stored in cortices. The quantum computing to update the synaptic effective range weights is short-term memory happen in hippocampus. Short-term memory travels through the berries of hippocampus and different cortices and turns into long-term memory. Refer to formula (2) and (4), working memory or short-term memory is consolidated and turns long-term memory. And we suggest strong and high flow working memory or short-term memory means maximum of directional derivatives of exacted memory pain plasticity is relatively good or inferior gradient of memory pain plasticity, means long-term memory.
PNN在研究中对脑科学的三个发现：
1.星形胶质细胞维持皮层记忆会使得大脑局部突触不会过于兴奋，模型对实验有所启发;
2.负向和正向记忆的大脑塑性还会对星形胶质细胞吞噬突触有所驱动, 因为图片或图片的正值或负值;
3.皮层较厚和大脑具备更多样性的个体，也许人类智商会提高；但是皮层持续加厚即使大脑具备更多样性的个体，可能人类智商会降低。
About PNN's 3 findings in pain science:
1.Astrocytic cortex memory persistence factor also inhibits local synaptic accumulation, and the model inspires experiments;
2. It may be the process of astrocytes phagocytose synapses is driven by positive and negative memories of pain plasticity, because of the positive or negative value of 图片 or 图片.
3. The thicker cortex and the more diverse individuals in pain may have high IQ in simulation, but the thickest cortex and the most diverse individuals in pain may have low IQ in simulation.
PNN对深度学习和进化计算的创新：
1.PNN把RNN架构改造有点类似CNN，而算法有些类似ResNet，池化过程或层数计算有些类似突触有效范围的更新，PNN也有共享连接权重；前向计算和反向计算除了考虑共享连接权重，也要考虑突触的有效范围；
2. PNN是把残差网络的层数计算进行改进，提出了权重的梯度不仅考虑当前梯度还考虑记忆的梯度，这个经过了公式推理；
3.进化计算比如遗传和粒子群算法，它们考虑的是全局最优或迄今最优解，而PNN还考虑了迄今较优和迄今较差解。
The innovations of PNN in Deep Learning and Evolutionary Computing:
1.PNN modifies the RNN architecture to be somewhat similar to CNNs, and the algorithm is somewhat similar to ResNet, the pooling process or layer number calculation is somewhat similar to update of the synaptic effective range change, PNN also has shared weights of synaptic connections. In addition to the shared weights of synaptic connections, we proposed a new Neural Network that includes weights of synaptic ranges for Forward propagation and Back propagation.
2.And PNN modified ResNet to calculate the layers’ number, is proposed the gradient of the weight is considered not only the current gradient but also the memory gradient by formula derivation;
3.Such as GA and PSO, they consider global solution or best previous solution, but PNN also considers relatively good solution and relatively inferior solution.
The 3 hypotheses were as follows：
1.Negative or positive emotion and cognition reflect non-classical exacted memories pains plasticity by quantum mechanics, are short-term hippocampal memories. The wave function is high-frequency. And wave function of exacted memories pains plasticity shows kinetic energy because of high-speed particles, produce at hippocampus. By PNN, we propose these appeared barriers from hippocampus to different cortexes, wave function will lead to exponential decay, and wave function of high-frequency turns to low-frequency. At last, long-term stimulus information stored in memory engram cells of different cortexes. And long-term memories exhibit potential energy;
2.Barriers may relate to astrocytes;
3.Directional derivative of working memory will flow to the different cortices and stored in memory engram cells. High flow working memory or maximum of directional derivatives of working memory means gradient of short-term memory, will turn to long-term memory.
3个假设如下：
1.通过量子力学，负向和正向的情感和认知反映了非经典提取的记忆的大脑塑
性，是短期海马体记忆。波函数是高频的。提取的记忆的大脑塑性的波函数表现出动能因为高速的粒子，在海马体中产生。通过PNN，我们提出在海马体到不同皮层存在壁垒，使得波函数表现指数衰减，波函数从高频变成低频。最终，长期的刺激信息被存储不同皮层的记忆印记细胞。而长期的记忆体现出势能；
2.壁垒可能和星形胶质细胞有关;
3.工作记忆的方向导数将流向不同皮层然后被存储在记忆印记细胞。高流动性或最大方向导数的工作记忆也就是短期记忆的梯度，将变成长期记忆。
实际上，以上工作除了突触成形[15]、大脑的量子计算机[25]和记忆的巩固[26]对我有所启发，其它工作都是我做出后找的相关文献对比仿真，包括残差网络的公式推理也是自己独立完成。
In fact, except for inspirations the synapse formation [15], quantum computer of pain [25] and memory consolidation [26], the above works are all relevant researches contrast simulation I found later, including the formula reasoning of the ResNet is also completed independently.
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