Welcome to the final ninth lesson. This one is unique, both because I'm going to touch the unusual subject of free will and so I want to connect what we learn about the mechanistic aspect of the brain and what we all feel. That we have free will to take this course. I have free will to provide you with this course. So let's talk about this later on. And so my plan for today is as follows. Basically I want to summarize very, very briefly about what we've learned through this lesson, through this interaction among us, through the interesting interaction among us which I will try to summarize at the end, say a few personal words. And I want to touch on the whole subject of neuroethics. And so what is the implication of what you've learned about our brain in terms of ethical aspects. What does it mean ethically to have such a brain? So we will discuss that. And especially questions like are we going to be able to read our brains, our thoughts? Can I look at your brain and read your thoughts. What are the limits for brain intervention? When can I, or should we as a society, decide that this is something that needs an intervention? And when do we not want to intervene with the brain? Can we augment cognition? Can we make us better, so to speak, by intervention with our brain? Via pharmaceutical or electrical or other means. In all this of course the issue of brain reading, brain polygraphs, interventions with the brain, for diseases but maybe not only. These are ethical issues that emerge from modern medical research. Which certainly we should discuss, this is not appropriate place in a complete sense, but I will still highlight a few issues that bother me, should bother all of us and we should all discuss it together as a society. And specifically I want to deal with some recent understanding of the whole very complicated, very very delicate issue of do we have free? Am I free to choose everything? To come and lecture to you, to come and move my hand now and so on. So the issue of free will, will be discussed later. And finally, at the end of this part, at the end of this lesson, at the end of this course I want to discuss the new phenomena which Coursera is part of, the MOOC phenomena, MOOC phenomena, or other several related phenomenas, like the Frontiers idea which I'm part of and the general Open Access idea for disseminating knowledge, transferring knowledge in an astute way, to everybody who wants to listen, anywhere in the world, including you sitting in Iran, in Pakistan, in Alaska, anywhere in the world. Listening online, in an interesting way hopefully. What's going on in terms of human knowledge? This is a new phenomenon I'd like to discuss, and then end with a farewell with a few personal words. So this is the plan for today, and let me start by remind you how I did start this course. I started the course by citations of two people. Francis Crick, Nobel laureate, the genome, he was saying here, as I read in the first lecture, that we are only no more than synapses, molecules. Everything we do, our feelings, our creativity, our action, our perception. Everything comes from interaction of vast interacting molecules, networks, synapses, neurons, something that you now know much better than you knew before, I hope. So this is a saying that means in many way that we are a mechanistic machine, a physical machine, nothing more than a physical machine. A beautiful physical that generate beautiful things, especially feelings, create new things, sometimes terrible things. But it's a machine and we need it to understand the machine both in order to understand ourselves but also to repair ourselves, our brain, whenever it gets sick. Similar in different ways I mentioned the saying of Claude Shannon, the father of information theory we are thinking yes we are machine. We are living proof that we are physical machines. So we started by saying that in a general sense. But I started hopefully to teach you that indeed when you look at the brain inside, you don't find any kind of miracles. Any kind of spirits, any kind of I don't know, other non-physical things but you find practically, you find very, very, very few physical things, like genes, ions, membrane, channels, neurons, axons, networks, and eventually something comes out of it. Beautiful things like love, but also terrible things like Parkinson. So I raise the original question. I raise can we understand it. Can our own brain with its own tools, mathematics. Invention of tools to see things, to probe things. And you heard, along the course, about some new developments, like the clarity method, that was invented while we were talking during this course. So, we invent things because our brain is so plastic, so dynamic, so interactive and so eager to do new things. This is very special to human beings, this issue of creativity. And we never touched, and we don't really understand what makes us so unique that we are so creative. But this is maybe for another course. So, can we understand? The answer you look for is yes. We understand a lot. Do we understand everything? The answer is no. There is a lot of things to understand. A lot of fundamental issues that we don't understand. I'll touch about this a bit later on. But what did we learn in this course? What did we learn? So let me summarize what we learned very briefly, so to give you how I see the essential things I would very much like you to remember, while you will contemplate on the beach with friends about this course. What did we learn? The first thing, again that we are in unique times for brain research. These are really, really for me, being in the field for 35 years, these are unique times in many aspects. One of the aspects is this development, is these new technologies. We mentioned the brainbow. We mentioned connectomics. We mentioned the Human Brain Project, the European, 1 billion euro, human brain project. We mentioned the Obama project, which is now called the B.R.AI.N project. So a little switch in the name. [COUGH] We mentioned the clarity. And we also mentioned that's something very important for me to emphasize. With all these techniques, wonderful techniques really, enable us to probe the brain and get huge, fantastic amount of data. New data. Anatomical, physiological, combined anatomy and physiology, manipulative data like optogenetics and so forth. All this, all this data, requires a theoretical framework. So I emphasize several times and I re-emphasize now, that without a theoretical framework, in trying to put all this data in some conceptual framework. That we can understand all this data, so that we can really take the data and link it to higher level phenomenon like emotions, like computation, like perception. Requires theoretical efforts, required theoretical framework. And so neuroscience now heavily depends on the development of new theoretical mathematical concepts, tools, that we are now trying to develop worldwide. So that's one message. The other message is that the elementary signals in the brain, electrical signals in the brain, are the spike. The action potential initiated mostly in the axons, sometimes also in the dendrites. We did not discuss it but you should think about this output element of the nerve cell the axon, that's carrying bits of information, the spike. Either there or not, zero or one, all or none. The spikes, this is a universal trick for the nervous system. Every cell, or almost every nerve cell has this capability to generate or not to generate the spike. So, it's a universal trick. For all nervous systems from the lowest animal to the most sophisticated animal like you and me. The spike carries information, represents information in the world. So when I see a face. When you hear my voice. When I move my hand. Our spikes in particular, neural networks. These spikes represent, whatever is represented, but also you use them to process this information through neural networks, and eventually comes to an action, to a decision, to a creative idea, and so on. These are the spikes, and the other electrical signals carried by the nervous system are the post-synaptic potentials through the connection between the cells, the synapses. So you have two major electrical signals in the brain. The spikes mostly in the axons, the post-synaptic potentials mostly in the dendrites, and they converge one to the other via PSPs many of them converging on the dendritic tree, eventually giving rise if enough of them depolarize the axon giving rise to a spikes. And the connection between cells are the synapses which are fantastic unique device. Enabling one cell so to speak to talk to the other cell via a chemical transmission between the spike in one side, and the EPSP or IPSP, inhibitory or excitatory on the other side. I also told you that underlying all these two signals, the spikes, the EPSPs, underlying these electrical signals is current flow through the membrane of these neurons. So these two key electrical signals, the spike, and the post-synaptic potentials, are carried by a very specific ion going through the membrane. These are from inside to outside of the membrane of the neuron or from outside inside. So these charged ions would be sodium, it could be potassium, it could be calcium, these charged ions carry these signals. So these are the underlying microscopic currents that eventually give rise to the macroscopic phenomena of spike and post-synaptic potentials. The fourth summary, so to speak, of point is that, the input to cells, the converging input to cells as I mentioned before are the synaptic inputs so synapses are the input to a cell. Many of them helping the network to converge into a cell, a given neuron. So many, many thousand, sometimes 10,000 synapses converge into a single cell. And this single cell is a microprocessor. Should take all this information from many, many, many synapses and making sense of it, so to speak, by generating spike output that reflects something about the input. So the neuron is an input output microprocessor taking many, many, many, many synaptic input converging on its dendritic tree. They all sum, integrate, and eventually come get to the soma near the axon soma region where they spike output is generated. So the cell is a very sophisticated microchip with two separate regions, the input regions, the output region, in the input region there are many synapses impinging, converging decorating the dendrite and the axon there are the spikes output. The fifth point I want you to remember is that eventually beyond speaking about the neuron as an input output device. The input also participates in some computation. So when I talk to you now, when I see a visual lying in the world. When I see a face. When I generate a voice. Somehow these microchips and the network they are a part of compute. Some aspect of the word. The need to compute the velocity of the car. They need to compute the weight of the glass. They need to compute the taste of the coffee and so on. It is our computation performed by single neurons which perform elementary computations but eventually the network as a whole generate a computation that is essential for the behavior of the animal, of myself, of you. Without such computations, online and successful computations, we would not be able to survive. We and every other animal. So the neurons compute from the information they collect, so to speak, from the environment plus memory processes. During learning, tell them what to do with this information, so that the computation will become more and more efficient, more and more relevant, more and more adaptive, so eventually, I will cross the road correctly without the car bumping into me. So this computation aspect and the term computation, borrowed from computer science, is of course, more complicated than we think about, because it's not multiplying numbers. That's what the computer knows to do. It's not that. It's taking information from the environment, and making a very sophisticated use of this information in order to be able to behave in this environment and also change this environment. So when I'm talking now, I'm not only computing what words should I say now, but I'm also changing you. Then you change me back with your blogs and feedbacks. So this is the perception action loop. And that's something I wanted really, really to emphasize, because this is so optimistic aspect of the brain is that it's plastic. So neurons and especially synapses, so the synapse is probably the most amazing material in the world, the synapse. Very minute, very plastic, wants to change, physically wants to change. And you now better understand what do you mean, what do I mean by saying change. Some of it is structural, so there are new synapses. Some of it is functional, so some synapses will become more or less efficient. So this change, this plasticity, really underlies the uniqueness of the brain. The total uniqueness of the brain and its capability to interact, change, adopt to the ever changing environment. Ever changing environment. So the plasticity aspect due to these synapses is really something absolutely miraculous in a sense of material. What kind of material it is that changes from the time? The best we know. And so we can use synapses in order to generate machines that learn or at least the principles we learn from synapses, how to change With interaction while interacting with the environment. So synapses are really the miraculous aspect of the brain is this capability to connect and change adaptively. In final and point number seven, just before the end of this eight points. Overall, when I speak about the brain. When I'm talking now or we're listening to you or to music or you listen to me, there are large groups of neural networks. I mean, millions, sometimes billions that are now active electrically and chemically via the synapses. So this million of neural networks composed of millions or billions of neurons. When something about the dynamical aspect of it, the electrical activity of it as a whole, operates within a regime, which we call normal, so to speak, then I behave normally. I'm not sick with Parkinson's. I'm not sick with Schizophrenia. I'm not autistic. I'm not depressed. So large neural networks with a particular range of activity will enable me to behave normal, so to speak. It's a matter of definition what is normal. But the point is within this large neuron network, somehow go out of balance due to connectivity within the network or due to excitability that goes wrong within the network or combination of many parameters go wrong. Suddenly, the whole network start to generate a new phenomena, a new electrical phenomena. A new electrical activity like in Parkinson's which you saw, then the brain become disease, there is a disease. And a particular disease involved a particular system and a particular mechanism and this is something, this emerging of disease, like emerging of some normal behavior. The emerging of large, large interactive group that emerge in particular activities that is normal or unnormal, not normal. This we don't completely understand. We don't know how this elementary unit, elementary aspect together as a whole, generate a phenomena that we will call normal or not normal and that's one of the really major challenge of neuroscience today. To try to go beyond the single cell, the single scene of the single gene and understand how a connected network of elements interacting now in my brain generate a disease, generate a feeling. So, it's beyond the unit. It's the network and for this, we absolutely need a theory. A theory for large elements interacting and generating dynamically and adaptively. A phenomenon that enables me to behave or sometimes not, like in Alzheimer's. What's going on with the network in Alzheimer's? Why suddenly the network generate phenomena and activity where you forget things? Why in Parkinson, the network suddenly generates this oscillation where I start to tremor? What's going on there? As the whole, we don't understand this. We don't even have good theory for it. So it's a big challenge for the 21st century, the century of the brain. So theory, again theory. So, I can tell you about synapses and I can tell you about genes and I can tell you about spikes and EPSGs and inhibition and excitation. But how all this mechanistic aspect becomes a phenomena? In the global sense, we need a theory. A theory for neural networks, a theory that connects the mechanistic aspect to the phenomenon. Could be conscious phenomena, unconscious phenomena, could be disease, could be vision, perception. We don't know how to link these levels and I said, this is their biggest challenge of the 21st century from neuron, from gene neurons to the network level and the behavior in phenomenological level, this is the biggest. And so, I think the course actually tried to give you the basics that will be causing psychology, which is the phenomena. There are no good yet course on the connection between we don't understand this connection as yet, but there are some beginnings of understanding. And I hope that in the near future, some of the Coursera courses will touch on this connection, the theoretical aspect, theoretical neuroscience.
