人脑的学习原理[转载]

现代神经科学和认知科学认为，几乎没有任何技能是人一出生就会的。哪怕是对简单物体的识别，把东西抓取过来这些简单的动作，也是婴儿后天学习的结果。一个人一出生的时候根本不可能预见到将来自己需要什么技能，基因不可能把一切技能都用遗传的方法事先编程，那样的话太浪费大脑的存储空间。最好的办法是不预设任何技能，只提供一个能够学习各种技能的能力，这就是人脑的巧妙之处。基因的做法是先预设一些对刺激的基本反应和感觉，比如看见好吃的东西我们会饿等等。这些基本的反应需要调动的神经较少。但对于更高级别的技能，比如演奏音乐，需要协调调动很多神经，就必须靠后天学习了。

人的任何一个技能，都是大脑内一系列神经纤维传递的电脉冲信号的组合。解剖表明拥有不同技能的人，其大脑的神经结构非常不同，比如出租车司机大脑内识别方向的区域就特别发达。也就是说与计算机不同，人对于技能的掌握是在大脑硬件层次实现的。

而最近有一派科学家认为，髓磷脂是技能训练的关键，它的作用是像胶皮把电线包起来一样，把这些神经纤维给包起来，通过防止电脉冲外泄而使得信号更强，更快，更准确。不管练习什么，我们都是在练习大脑中的髓磷脂，就好像把一堆杂乱无章的电线被排列整齐变成电缆。直到2000年新技术允许科学家直接观察活体大脑内的髓磷脂之后，髓磷脂的作用才被发现，而且一直到2006年才第一次被在学术期刊上说明。科学家认为髓磷脂是脑神经的高速公路，提高信号传递速度，并且可以把延迟时间减少30倍，总共提速3000倍，甚至可以控制速度，想慢就慢。

人脑之中分布着大量“自由的”髓磷脂，它们观测脑神经纤维的信号发射和组合，哪些神经纤维用的越多，它们就过去把这一段线路给包起来，使得线路中的信号传递更快，形成高速公路。这就是为什么练习是如此重要。

髓磷脂理论可以解释很多事情。比如为什么小孩常会犯错？他们的神经系统都在，也知道对错，只是需要时间去建立起来髓磷脂的高速网络。为什么习惯一旦养成不容易改变？因为所谓“习惯”，其实是以神经纤维电缆组合的形式“长”在大脑之中的，髓磷脂一旦把神经包起来，它不会自动散开 — 改变习惯的唯一办法是形成新习惯。为什么年轻人学东西快？因为尽管人的一生之中髓磷脂都在生长，但年轻人生长得最快。最激进的理论则认为人跟猴子的最显著区别不在于脑神经元的多少，而在于人的髓磷脂比猴子多20%！解剖表明，爱因斯坦的大脑中的神经元数量是平均水平，但他拥有更多能够产生髓磷脂的细胞。

parallel computing and memory networks

when we implementing an algorithm, often we are facing a decision, to match the fork-joint flow shape of our algorithm, we can have either one single long thread or many short parallel threads that do the parallel-able computation task on computation resources with varying amount of inter-threads communication.

obviously it can be modelled as a optimization problem, and depend on the nature of our algorithm (or application) and problem size, we will result different code for execution which will minimize the execution time and possibly satisfying certain constrains.

it all sounds we almost got the solution, but if we think about it carefully, we are still facing a big challenge, the memory band width, although most of our computation resources (CPU,GPU,accelerators) have their own memory system for caching data, we still face a challenge of delivering data and instructions to those devices in time.

for example, we have 128 treads running on 32 cores, when the threads are switching, they will likely to cause cache miss and require a main memory access, if one core does it, it should be ok, but if 32 core all accessing the same memory, we will have a network congestion, therefore resulting a reducing parallel performance.

if we think about how our neurons in the brain communicate,this is a very different architecture, first, we have a dynamic physical communication network, and the dynamic connections are evolved by some degree of competition and cooperation, one example is the ion gate on the synapses are varied by how it is used.

but the real different is possibly how memory is structured in our brain, a very good example would be performing calculation on a abacus and in our mind. surely we can do the abacus way much faster than do it in our mind, unless their some quick algorithm for large problems, but the real point of this is, we don’t have much memory (possibly RAM like memory) for the tedious calculation, where our brain is much more capable of doing visual information analysis and muscle control signal generation, and the same time very deep in our brain, a look up table for conditional branches, and I guess that’s may just be a configuration of our neuron connections.

so where is the memory? you may ask, well, I think most our memory is just a neuron network pattern , which is a ROM (read only and take long time to write) like thing but the different is reading it’s info is by using it, which is more like a FPGA LUT net.

so from a architecture point of view, our neuron network in the brain would not be very good at execute the repetitive simple instructions, since we don’t have the right memory structure (RAM) for them, but we seems to be doing much better vision task than the computer which has very few number of computation units and very large amount of RAM, what could be the issue here? again, the real answer for this should be, computer can do certain specific vision task better than human brain, but when you think about a general case (large data set), the human brain will out perform the computer, one answer to this could be the algorithm in our brain are optimised by a long term evolution, where the computer just execute what we think might be happening in our brain in terms of numerical calculation.

but how does it relate to the memory architecture problem? we can see the trend of adding more computing resource on a single chip, but should we try to go towards the brain like structure where dynamically routing the connections of different resources and have millions of them? that perhaps will work if we don’t use digital format for computation and lose the machine like robust properties, but do we really want to do that? I guess that will just denied the purpose of building machines, we want to have a high degree of certainty of what we do at each step, this is just a complementary behaviour to human, and that’s why we need them to be like that.

so if we have decided to go the machine way, what is the problem we need to solve? the network? the memory hierarchy, or the load balancing and scheduling on computation resources? I think all these issue can be solved by a good communication protocol, with a good protocol, we can reduce global communication and help reduce the main memory traffic, we can also make good use of memory hierarchy and automatically solve the resource sharing problem. this is more or less like how human communicate with each other, we have a good protocol that allow us to communication in small groups, large lecture theatre, and one to one talk.

so what’s the secrete of this protocol then, although I am not fully confident with my answer, but I think it’s has a strong link with model predictive control or MPC for short, because in order to optimize our behaviour, we much know as much information of our communication objects as  possible and build a model of it, then a dynamic optimization process goes on and we find the current best action for a goal of better resource utilization. obviously this is not a simple scenario when many node in the network is doing MPC, but with more in depth  research, I hope we can have more robust framework for this future intelligent communication protocol.                   

resources

http://www.brains-minds-media.org/current

how genome build the brain

Then there's the mystery of the developing brain. How does something so complex manage to build itself? The Allen Institute is also measuring genetic expression in the mouse brain, from embryo to adult, to explore how the orchestra of genes is switched on and off in different areas during development. Which snippets of DNA transform the hippocampus into a center of long-term memory? Which make the amygdala a warehouse of fear and anxiety? "One of the things I've come to appreciate about the brain is the importance of location," Allen says. "It's not just a set of interchangeable parts that you can swap in and out. These brain areas are all so distinct, and for reasons we can finally begin to understand."

One unexpected—even disheartening—aspect of the Allen Institute's effort is that although its scientists have barely begun their work, early data sets have already demonstrated that the flesh in our head is far more complicated than anyone previously imagined.

The brain might look homogenous to the naked eye, but it's actually filled with an array of cell types, each of which expresses a distinct set of genes depending on its precise location. Consider the neocortex, the so-called CPU of the brain: Scientists assumed for decades that most cortical circuits were essentially the same—the brain was supposed to rely on a standard set of microchips, like a typical supercomputer. But the atlas has revealed a startling genetic diversity; different slabs of cortex are defined by entirely different sets of genes. The supercomputer analogy needs to be permanently retired.

Read More http://www.wired.com/medtech/health/magazine/17-04/ff_brainatlas?currentPage=5#ixzz10RZv3R5E

 

also if you are interested in memory system in the brain, for example difference between remember and record, hierarchy of information caching and information abstraction. spatial and temporal pattern exploration and learning.

read more at http://www.numenta.com/ , they have been doing many interesting research in this areas.  

brain, memory, learning and creativity

understanding the architecture

• sensors
• filters
• processors

 

hierarchy of information

• vision
• voice
• emotions
• language
• semantics

the higher the information level, the faster the memory for it but smaller quantity in our brain. the visual memory has huge quantity, but quite slow in terms of erase and update. so we can use our permanent memory to generated vision info and sore them into our visual memory which can store many many information,but come with a draw back that it can not erase and update very quickly, but don’t worry they are just buffers so eventually you can still reuse them.

a good strategy is to generated information( compact and structured) to a visual form and then take a look at your picture. then you will be able to remember them for a much longer time and access them on demand.

so the key is the picture or voice generation, so how to generated them, any guidelines?

imagine you have two picture to look, the more interesting the picture it is, the more likely you are able to remember them longer. same goes with the voice. so how exactly we find things interesting, one thing might be related to the new information and our knowledge, say how novel it is and how well does it fit our knowledge and rules.

this process of getting a picture shows some aspect of information synthesis and creativity which I will talk about in the later part. 

algorithm of learning

the idea of learning is the opposite to synthesis, where we extract level by level from the raw information, and finally discover the gold. very like a filtering process, but with more sophistication and recursion.

so when we learned some thing, say a language, we can use it to learn further knowledge about some thing, and keeps going, so inside our brain, we have a hierarchical knowledge base and we keep building it up, kind of like a graph which model the environment we live in, but not a firmware which means we can change part of it if it does not model correctly.

 

information synthesis and creativity

one we have a model of the world in our brain, we can run our simulations in side our brain and generate some outputs which after some refinement or optimization, we can speck them out or write them down. that is the essence of information synthesis. but how creative our information is generated, has much more thing involved.

think about generating a joke, that will obviously require much more creativity than just a small talk.

joke is great example, but it has some formulas which is good for us to study more about creative info synth. of course there are many different kind of jokes, some are so called stupid jokes and some are called intelligent jokes, here we have look at the intelligent jokes, dramatic change but logically reasonable, dirty but beautiful, push and pulls.

so it seems all about a good balance, or symmetry, or many other other principles such as recursions, reflections,etc which we perceive them as our fundamental knowledge, which again is part of our model has the most agreement with the nature!