Understanding the Brain: A work in progress - Professor Keith Kendrick

this is obviously a rather presumptuous title understanding the brain and it definitely is a work in progress and I gave a lecture at Gresham in fact about seven six seven years ago with a similar kind of title and things have moved on already quite a lot in that time and why I can't possibly really give you a sort of complete detailed understanding of every aspect of brain function I'm going to be fairly selective I'd like at least to try to sort of generate your interest in the kind of ways we're beginning to think about what's important about the way the brain is actually encoding information because we all know as you can see from the picture here that brains come in a variety of sizes and shapes although in fact their general structure they're fairly similar whatever species you look at we also know of course that the brain has an absolute fantastic range of functions that it fulfills one of which we are not aware of but in without it we would be dead controls body functions and motivates us to obtain appropriate resources to maintain life of course especially for people who work on movement who feel that the brain is only important for controlling motor function but of course without movement we wouldn't be able to do very much at all the area that I'm more involved in is detecting and interpreting sensory information and particularly in my case social cues for social species like ourselves one of the marvelous things that the brain can do which kind of distinguishes it from most computers it can highly selectively attend to specific things rather than others and must filter out things that it's not interested in and just focus on whatever it is that we're interested in any particular point in time again another area that I'm really very much involved in is brains fantastic capacity for learning and remembering information and particularly the way it integrates it with past knowledge I'm not going to talk actually very much about emotions today but clearly one of the important aspects of emotional response is that they are also very very potent guides for behavior and finally the area that I'll cover right at the end of the lecture perhaps the most contentious and most difficult for any scientist to get their head round is how a bunch of neurons can generate conscious awareness of the external environment self and others yeah we are making some advances at least into trying to understand what the difference is between a brain that's working in conscious mode as opposed to one that is working in unconscious mode it's kind of traditional to emphasize really how fantastic the brain is by immediately comparing it with computers the brain is 920 centimeters cubed it's 1.5 kilograms in weight this is a human brain obviously it has an amazing 100 billion nerve cells and given that there are about 5,000 synaptic connections on each of these nerve cells that equates to these big big numbers about naught point 5 quadrillion synaptic connections which is between 0.5 petaflop so if you're talking about computers and we'll come to those in a minute and if you consider that perhaps they're usually firing away at around maybe 10 Hertz then that gives you 5 quadrillion or 5 petal flops of synaptic n'd and that generates 10 watts that's quite an amazing amount of processing that's going on if we compare that to computers over the last 10 years things have developed quite amazingly in terms of processing power of computers back in 2000 it was 7 point toward teraflops which is like a trillion signups operation 7 trillion by 2002 it had gone up to 35 point eight two thousand four seventy two thousand five in fact quite a big leap up to two hundred and eighty teraflops and finally it gets up to pretty close to the broadly what the equivalent of brain would be by 2007 and eight and now it's gone beyond that this is the the have currently anyway the the operational computer in the Department of Energy as it usually is however here's the big guy this is the IBM Sequoia supercomputer that is currently in process of being commissioned this is reportedly going to have twenty petaflop speed so we you know even getting way beyond the brain 1.6 paid off lots of memory but notice it's 318 meters squared 96 racks of all this stuff and it generates seven megawatts of power so the computers are not doing things the same way that the human brain is or any brain is in fact because rather than transistors we have neurons and they are simple but also amazingly complicated so we have informational see electrical impulses transmitted from neurons to the dendritic receiving field or the neuron here so this is where your 5,000 input synaptic are they alter the electrical properties of the membrane of the cell and when it reaches the critical threshold it fires an action potential which gets transmitted down the axon here through to communicate with the dendritic field of another iran and it's as simple as that but it is phenomenally integrated and complicated if you think about the number of connections that are going on and how much even one neuron can affect the activity of many other neurons this is maybe not going to kind of play this is supposed to actually show a sort of one of these usual simulations you get with light rather than electricity of lots of of neurons and a network just to try and wow you a bit what is often forgotten though is that we all focus on brain neurons because that's the the primary cell type that is actually transmitting information electrical impulses in the brain however there is a very very important supporting caste with which or without which the brain simply wouldn't function and these are called neural glial cells the primary one of these astrocytes somewhere here we are going green here they're not actually green but this is just a pseudo color representation they anchor neurons to blood vessels and they transport nutrients and waste away from so they're highly important to the function of neurons they have receptors just like neurons they produce growth factors as well just like neurons and that they can even in fact act to modulate the activity of neurons through the synaptic transmission finally they can also actually signal to one another but they don't use chemical synapses they use direct electrical connections called gap junctions and the this is in the calcium is important in this type of communication brain also has its own immune systems this is the microglia up here shown in blue these are a defense against pathogens and they monitor the conditions of neurons ependymal cells here lining the brain ventricles which I'm sure you know is all full of cerebrospinal fluid which is also very important for carrying information around through the brain and so these are generating or producing and transporting cerebrospinal fluid and finally there are the oligodendrocytes which are important for producing the myelin sheath around axons which helps them transmit information so this supporting cast is often forgotten but and I'm not going to spend any more time talking to it to you about it but they are very much important for regulating brain function and particularly neurons also something again or they might be fine surprising I'm not going to spend a lot of time talking about I could but I want to sort of go at a higher level about how neurons communicate and actually represent information the molecular brain the we know a huge amount now about how chemical signals are transferred from sign apps to the they met the membrane of receiving neurons and all the nature of the receptors in the membrane here which allow chemicals to affect the cascade of changes within the intracellular domain of a neuron and alter its function permanently and you can see that they are increasing knowledge of these fantastically complex intracellular signaling pathways which are very very useful for us to understand particularly when it comes to drugs the target generally function of neurons let's step back though from the molecular levels and even cells and just consider the major subdivisions of the brain because they're pretty similar whichever brains you look at here we have the the brainstem the pons and the medulla which you will find in all brains and this is a highly important region for controlling automatically pretty much all of the peripheral organs in the body through the parasympathetic and sympathetic nervous systems when someone says you're brain dead it's this area that ceases to function because once this area has gone you are dead there is no the cortex may still be capable of things but it's not possible for you to survive once the activity in this region has gone and on top of that we have the so called reticular activating system which is also is a midbrain and that is activating the cortex permanently arousing it and allowing it to function and is also going to be very important for controlling your levels of arousal sleep and also consciousness of course as well again we focus a lot on the functions of the neocortex the most interesting part of the brain for most but it's useless without these basic brainstem and midbrain activating systems over the years we've begun to learn certain things principles if you like about how brain systems work and obviously one of these is this concept of neural plasticity we're going to spend an awful lot of time on it but the whole really interesting thing about neurons is that they are in the constant state of flux they change as a function of activity and this is generally termed as there are plasticity neural plasticity and this Canadian a neuroscientist Donald had many years ago in the 1950s came up with a very simple rule which was when an axon of cell a is near enough to excite a Selby and repeatedly or persistently takes part in firing it then some growth process or metabolic change occurs in either one or both of the cells such that a sufficiency as one of the cells flowing B is increased now how that sounds mind-bogglingly simple but that's what happens in a large scale within the brain there are changes going on all the time which alter the efficacy of one year on driving another perhaps one of the other big changes really in our understanding of brains in the last decade or so has also been the the concept that we know we know that we learn things by just watching other people all the time where the brain seems to have a phenomenal ability to take shortcuts and learn things much quicker than any other kind of artificial system can and that's one of the most exciting discoveries in fact in neuroscience in the last 10 years has been the discovery of what are loosely termed mirror neurons and these are particularly in areas that were bordering the motor cortex here shown in raylin and yellow and if you imagine that when this is for example neurons firing in these regions when a monkey here is reaching for a piece of food and the cells far away very strongly but when the monkey is actually not doing anything observing a human doing exactly the same action you get exactly the same pattern of cell phone and this has been shown now in each end in humans as well as monkeys and it seems that there is this fantastic system which is allowing us to as it were run through practice what somebody else is doing without actually having to do it ourselves and that gives us an amazing capacity to learn by mimicking watching others and of course unsurprisingly one of the sort of extrapolations from the discovery of this kind of concept about the way the brain can sort of work in the same way as someone else that you're actually viewing is that this may be the sort of particularly at the heart of very complex human emotions like empathy your ability to put yourself in somebody else's place it hasn't been shown that but at least it finally gives us some kind of principle upon which the brain can as it were mimic the actions of others and is particularly well adapted to do that oops I sorry I forgot to point down here this comes back to the empathy side if you look at this is an FM we have a functional magnetic resonance imaging study on control human subjects versus autistic individuals and the take-home message from this this mirror neuron complex is not as strongly activated in the brains of autistic individuals as it is in the brains of non autistic individuals perhaps suggesting that this is one of the the problems that autistic people have but they are unable as it were to really mimic the actions of others so how is information represented in the brain this is really what I want to mainly discuss with you and this is the way that for many years we have considered the brain to operate this is obviously a very spoof picture but of the male brain the main thing that it's supposed to take home to you is somehow or other this idea that everything is compartmentalized separated you know the Eiling representation down here is almost minuscule and you can see down the bottom there decided that they listening to children cry in the middle of the night gland is so small you need a microscope to see it what I would like to sort of disabuse you of is the brain doesn't really work this way yes it's especially if you look at fMRI studies and the way they're done is they're average then you subtract a sort of control period from an experimental period and you end up with a hotspot that's what that's what's different between the brain doing one thing and doing something else and it's very easy from that and this kind of representation of the brain to get the take-home message that there are specific regions of the brain that do one particular function yes as we'll see in a minute there are areas of the brain that are particularly specialized for important functions but it's much more important to have an understanding that the the brain is a very very integrated organ and there's interactions going on across all the different regions all the time and that is probably as we'll see later what generates consciousness so I've given you this sort of concept of spatial encoding which is very sort of humorously depicted in the in that image of the hypothetical male brain and this is a very very simple schematic diagram which really is just for me to try and help to explain what the advantages and disadvantage of having spatial encoding that is information spatially separated in terms of its representation within the brain if we look at the top here if you imagine that these this is a single piece of information each of these inverted triangles a single piece of information doesn't really matter what it is and you have an ear of a are to one another the more likely the information from here is to be relevant to here and less likely information from here is to be relevant here and yes you can you can represent a large amount of information this way but the immediate obvious tako message from this is that it takes a hell of a lot of space to represent information this way and the other thing that should come across immediately is that if you want to integrate information from here to here you have to have very long connections and a lot of them and this is not in the most efficient way in many cases any way of integrating information so far better to end up perhaps with either this which is a sort of spatial representation but where you've got overlapping populations this helps a lot with integrating information but you still have a degree of separation clearly you can get more interference or you could go literally down to everything being represented by the same population of neurons and very with very subtle differences in the pattern of activation you could actually distinguish one piece of information from another and the brain actually you can see examples of all of this in different systems in the brain this was just to remind me that if we had brains that were organized this way might seem like a great idea but we would have to have extremely large brains and heads to decay with it what's becoming more interesting now and it's still early days I think really in trying to unlock the temporal code of the brain but it's clearly an essential aspect of understanding how brains encode information we have been very much fixed in terms of the gain of neurons either going up or down and that's mostly what we measure that's what imaging studies mostly measure the actual firing rate of neurons is either increasing or decreasing but that ignores to a large extent the fact that the lots of different neurons are firing and that they do so obviously in time and is there anything in the way that they are organized temporally which shows that there's information content and there are two things that come out very quickly we've and it's a huge focus on the extent to which information across neurons in a network are temporarily correlated and I'll come back to that one later on since this is an area that I've been particularly interested in but that is clearly something it does happen in fact in this case this is this is real data in fact from from a rat from the smell system in a rat before and during an odor stimulus and although it's difficult to see in fact the extent to which these four neurons these vertical deflections are the output of individual neurons they become actually less correlated during the ODA stimulus than they were before but what I actually want to particularly point out from this image is something else is happening in here which is really quite exciting there are patterns okay and we use software which is for recognizes non-random patterns comes from different forms of analysis and I won't go into it but there is a very robust way of detecting patterns in time series of information where you can record from that in fact in our case often several hundred neurons at the same time and this picks out that the incidence of a pattern that's occurring across these four neurons which is D ABC you can probably see that the color scheme because it's pulling them out down here it's something like orange red green blue and this this sequence is occurring across the the four neurons again and again and you'll notice that during the stimulus it's actually occurring far more often than before now the presence of these kinds of patterns within large-scale neural networks intimates that there's a huge amount of potential additional information processing power that is going on in these circuits which quite apart from whether they're increasing or decreasing their firing rate there's also the patterns that they are generating clearly these patterns have to be decoded downstream and that's difficult but it does seem that neurons are capable responding differently to different temperament temporal patterns of inputs so they are capable of decoding temple sequences of patterns like this this just shows you examples as the olfactory bulb schematically this is one one pattern that's being generated I think about seven elements it can go up to about twenty it only occurs as you would expect during the period when the animals actually inhaling so this is an anesthetized animal in fact and so these patterns are occurring each time you the animal is actually inhaling the odor and this just shows you that the incidence of the patterns increases during odors stimuli it also increases with the intensity of the odor so it's really meaningful not only does the incidence of patterns increase but also the complexity the number of elements and pattern increases during stimulus and also once again during odor concentration so this is a exciting way of really unraveling a whole new form if you like of encoding information in the brain it's quite distinct from firing rate changes and isn't showing us that the brain is actually doing a huge much more information processing than we have yet really understood so overall the conclusion is that really what you need is a combined spatial and temporal encoding system in the brain you still know more about spatial than we do temporal but this is clearly the best most robust solution and also most importantly allows brains to be of a reasonable size for our bodies it also makes it easy easier to separate integrate and decode information a lot is known about the sensory brain and this does show you to start with at least that the senses tend to be spatially represented all the different senses the auditory sense taste smell vision somatosensory touch and motor systems all seem to have at least level the cortex here rather separate representations they also seem to map quite faithfully on to external representations in the world so the pattern of information impinging on the retina here a very simple pattern you can see a similar pattern of activation in the visual cortex and the same goes with a somewhat more complex radial stimulus here showing at least a relatively loose representation again at the level of the visual cortex similar thing goes with the turn of topic maps in the auditory system the auditory cortex separates off different frequencies as you can see here and the strongest spatial maps that you see are in both somatosensory and motor systems these are these two bands of cortex in the middle here and this is the representation of different parts of the body both in terms of the motor system and the somatosensory cortex and you can see that they are very distinctly represented and it seems to be very important for both these systems indeed there are a number of conditions where it's particularly in sort of example professional music musicians pianists where if you practice enough any particular motor task you will increase the representation of that part over the body in the cortex but of course with especially with juxtapose things like fingers you can end up with a representation becoming overlapped and they interfere with one another and that does happen in some cases and is a very good example of why this particular type of motor control and also somatosensory control needs to have a very strong spatial distinction between one part of the body and another and yeah this is the representations of the way the brain sees a male and perhaps many other perhaps for many women see my house as well but you can see which parts of the body are clearly most represented however to say that everything of the visual system the auditory system and taste system although they appear to be separately represented they do actually integrate and even interfere with one another a great deal there are key multi-sensory areas of the neocortex for example where multiple senses converge you can also see examples of a number of illusions I just mentioned a couple of ones here where you can see that sight and sound for example are interfering with one another normally they expect to compliment one another but if you make the situation so that the information mismatches you can see how important the integration between the two senses are so for example this is through so-called McGurk illusion which I think was shown on horizon the other day so you see a mouth shape the word bait but you hear the word gate and what you actually think you hear is date they confuse one another another one of these is facial expressions even if they're not actually even consciously perceived can modify the perception of emotion in the voice of a speaker so if someone is speaking in an angry voice and smiling it makes you very confused of course there are other many many other examples of how the brain expects to see things in a particular way and when it doesn't it gets confused unfortunately this is not working very well at large size but it may be that these are actually completely static images but in these illusions you normally see movement of like three barrels here and that these these circles here are actually twisting around because the brain interprets them as as movement even when there isn't any and there are lots of illusions like this one of the things the brain is very very good at and it's take shortcuts it really it interprets things as it expects them to be given the light of previous experience but of course in a lot of cases they don't actually appear in that way but the brain thinks they do this idea of keeping things separate especially in sensory systems is particularly showing the importance of it anyway in a condition known as synesthesia which here Richard Saito Vic has done quite a little work on but many others have - and here this is individuals who actually see or hear specific words for example letters or numbers as colors or another example tasting shapes here another example is a lady who felt musical instruments as if they were touching her in different parts of her body this particular condition has been shown to be quite prevalent in fine art students for example about 23% and it's almost certainly now recognized being caused by cross wiring between these spatially separated sensory maps particularly as you can see here but that's more interestingly than that even it's now generally thought that this kind of synthetic experience of the world where senses are interfering with one another but they are more integrated is the way that young children all right you know actually all of them experience the world early in life may explain why everything goes into the mouth and in babies but it's now thought that in fact the developing brain is very much as a synthetic brain where all the different senses are some extent overlapping and that as you develop they become separated and give the experience that most of us have as adults other things in the brain are also very important for us to keep separated and these are off water often loosely called what and where pathways in the brains to where what something is is primarily at least in the visual system processed by a ventral stream from the visual cortex here down into the temporal lobe where we'll come into a minute where we find for example and specialized circuits for responding and identifying faces and face emotions and then there's a separate dorsal stream up through into the parietal cortex which deals with where things are in space there are interactions between the two obviously you do need to know what and where but it seems to be important as far as the brain is concerned to try and keep these two things separate so I'm now going to focus on faces mainly because I think the work that's been done in this area particularly illustrates what we're grappling with when we're trying to understand how brains are representing highly complex information I won't give you all the answers but at least I think I'll be able to show you some interesting ways that the brain particularly in this part of the brain which is processing faces in a rather specialized way seems to be using different ways of encoding information it's doing amazing things it's being able to sort out questions of who are you how do you feel and do I like you all in less than 300 milliseconds and three species really have so far contributed to our understanding of how the brain actually fulfills this all-important social recognition function initially work obviously on on non-human primates monkeys and also humans but also over the last 25 years or so I've also shown that in fact sheep and probably many other ungulate species have similar specialised face recognition systems that allow them to recognize each other and in fact other species such as humans as well and that includes being able to recognize face emotions and the same system exists in all of these species for recognizing faces I know it's pretty difficult to read the schematic down here but this is the specialist area of the human brain the fusiform face area which it would tend to be called in for temporal cortex in other species this interacts particularly with the brain amygdala which is a very important structure for the control and recognition of emotions and the interactions between these two areas are very important for understanding face emotion for example and these areas also interact very with a very important area of the brain the medial prefrontal cortex which is dealing with higher cognitive functions and executive function and so forth now this particular area of research posed problems when it first came out because up until the point where people started looking for really at the face recognition system all of the the sort of work on looking at the complexity of encoding by single neurons in the brain had perhaps disappointed many people particularly for example Hebel and vessel who are working on the visual cortex they were expecting to find neurons that were responding to specific kinds of objects but they didn't they found the majority of neurons responded only to very simple visual aspects for example lines moving in a particular direction colors and so forth it seemed that the brain was primarily breaking up for example the visual world into a myriad of component parts and they didn't seem to be anywhere early brought it back together into a sort of single percept an object but suddenly particularly with the work of Charles Charlie Gross at Princeton in the u.s. well back him would have been I think the 1970s suddenly started to find cells in this temporal lobe which responded to specific faces or body parts and that led to a sort of quite a debate on conceptualization of the way the brain was actually processing information there was a kind of reductio ad absurdum that occurred that people suddenly were saying well you're saying that if we got specific cells that respond to your grandmother that these are the cells that need to be activated in order for you to recognize your grandmother but by definition these hi older cells there are very very few of them and so you could for example go out to the pub one night and have rather too many glasses of your favourite tipple and granny would disappear because she would have killed off you know this handful of cells and of course that's not the way it works at all what we think is that the generation of these very high order cells respond to things like specific individual spaces and there are very many of them it's mostly faces and body parts they do help for assisting the speed and accuracy of recognition of grandmother or anyone else but in fact the recognition process involves all of the different levels of analysis right the way from simple aspects of granny's face right the way through all of these different components and you can put in as many as you like so in the end granny's representation is quite a distributed network which happens to have these high order cells at the top of it and indeed with work on sheep that I've done where we look at the process of forgetting phases over time what happens is the specificity is high older cells gradually disappear as you not sort of forget but over time if you're not seeing the person every day it doesn't seem you need them anymore but you can still recognize them just not quite as well so that kind of emphasizes the fact that recognition isn't about these high order cells being activated it's about the whole network being activated so here's an example of high order cell well-known Pamela Anderson cell which was reported in nature in 2005 it wasn't just her there were many other actresses and these were all male subjects of course and and they were all subjects who were about to undergo operations for epilepsy and it's actually been an extremely useful important source of be able to do brain recordings in humans that a lot of these individuals in order to identify exactly what part of the brain to operate on have implanted electrodes for some weeks very often before they're operated and that allows us to gain through them insights into the way the human brain is operating in the degree of detail that are previously of course we could only have done using techniques in other species so you can see this is a conceptual cell this is Pomerance in in various different views but you'll notice the cell histograms down here fires very strongly just to her name so it isn't just her face it's anything to do with the concept of Pamela Anderson it's as very very high older cell and this area of the human brains is imaging now the fusiform face area shows a greater activation to both faces and also very importantly another aspect of our bodies which also communicate information so faces and bodies seem to be particularly represented in this area as composed to other kinds of objects and there's also for a while it was thought that the kind of face emotions and face identity systems were very separate and that the fusiform face area was all about encoding identity but more recently it's been shown that the emotion actually content of the face will in it can enhance the response in the fusiform face area so this is again an fMRI study where you can see the red line here this is the level of activation with a fearful face compared to the same place which is showing a neutral expression so this shows us that within particular the interactions between the amygdala and diffuser form face area we can actually get into integration of both face emotion and face identity cues and work particularly in monkeys but but also increasingly in humans are shown that there's a kind of like a sequence there's a sort of speed with which processing occurs initially the emotion bit is analyzed any identity and then there's a kind of like a cross-checking bit later on with the emotion bit again so with work for example we've also done with sheep we found that in fact the emotion that the individual face is showing is is the paramount importance it's the first thing that the animal and we assume also humans focus on before identity and clearly you can think it's it's actually pretty it's more important to know whether whoever you're meeting is about to kill you or as angry then perhaps who they are so it makes sense that the first thing you should do is to actually identify what emotion the face is expressing the face system also illustrates the brain as an interpreter or this one for the minute this is so called Thatcher illusion if you just focus on these top two panels here you can see there's something wrong here with Margaret's face but in fact it's only when you turn it to be the right way up you can see that the eyes and the mouth are totally inverted so we don't expect to see the face like that and this system is working like many other aspects of the visual system it is interpreting this complex image the way the brain expects to see it as opposed to the way it actually is so promised you some sheep I think this will them so this is sheep showing that they can discriminate between faces by pressing panels with their nose and just in case you didn't believe it and yeah they get food every time they get it right and the positioning of the faces is randomized because so it's not always left or always right and that they're extremely good at doing this they can remember or discriminate up to about 50 different sheep faces we haven't gone it's probably more than that was as far as it went and at least 10 different human faces and they can remember them for several years and with these kind of animals we are able to implant large numbers of electrodes into this face area and this shows you a pseudo color representation of the firing of 240 or neurons in both the left and right hemisphere and you can see that there are nicer spatial clumps if you like a responses to the phases that are occurring indicating that there is a degree of spatial encoding that's going on across this large-scale network for specific phases but if you then plot what's going on with all of them the neurons across the leads of 240 neurons in a sort of 3d plot what we found was that there's a yes there isn't that is the difference between the representation of one place and another but it's quite small it's only about maybe 10% and this seems to be telling us and is also evidence in in monkeys as well that the wave phases are encoded isn't a sort of pure spatial code it's a population code where all of the neurons in the system are actually encoding each face but in a slightly different way and you can distinguish one face from another by even a very very small change in the pattern of the population response and this is a very very powerful way of encoding information it gets back to that if you remember that spatial representation I showed you where there was a single triangle it meant that within a very small space just because you can actually manage to discriminate between one face and another one it was a very small shift in the representation of the population level you can encode thousands of different faces that way with a fairly limited population so it's a very powerful way of encoding information at a population level rather than individual cells we now go to a perhaps more complicated concept that one that's becoming increasingly important for us which is this temporal dimension again I've sort of shown you that there are large scale changes going on across regions of the brain and they're integrated but what is it that's actually coordinating it if you like across large areas of the brain and one of the key aspects that does this are these very important brain rhythms that perhaps many of you all heard of in the context of sleep the cortex undergoing different patterns of rhythmic activity as you go down into deep sleep and then coming back into REM sleep but it now appears that these types of rhythms which range from very very slow electrical rhythms the delta rhythm down to about two Hertz through up to the high frequency gamma rhythm which is about 30 to 120 Hertz and this is generated by local neuronal circuits firing whereas these slow rhythms down here are generated by long-distance electrical activity going on throughout the brain and for a while these we just thought to be perhaps some kind of side effect of electrical activity of the brain but it's now increasingly being shown that these are performing very important modulatory functions which allow us to understand how information can be coordinated across wide areas of the brain and particularly the it's now increasing evidence that slow and fast oscillations are actually coupled and perform different functions so for example theta which is the four to eight Hertz low frequency which is occurs in a very synchronised way across large areas of the brain and is thought to be one of the important things for integrating a clot in time information across water as the brain that has is coupled with this high frequency gamma rhythm which is the the local neuronal circuits firing away so that you end up with the gamma waves which we'll see a much higher frequency locked to a phase of theta and interestingly enough you can you can only get about seven cycles of gamma on top of each individual fetal wave and the idea is each of these identify gamma waves here for example is encoding a specific piece of information and the fact that you can only get seven on top of a theta wave has led people to speculate the perhaps this is what an explanation for one of the well-known psychological aspects of memory which is the magic 7 plus or minus 2 which is you can only remember in keeping in your mind 7 items of information at a time and this would actually provide and it's a speculation I hasten to add but nevertheless it's interesting that this kind of idea that you've got the coupling between slow and high-frequency rhythms in the brain might actually provide us with a way of understanding why there is this limitation for how much information we can hold in memory at any one particular time the interesting thing there about these rhythms is that they do couple with neuronal activity so this is theta activity this is real data in fact them from a sheep and the firing of neurons occurs the same phase of theta different phases depending on the neuron so there's a link between the activity of theta and the firing of neurons and this shows that during learning in the sheep nearly three-quarters of the temporal cortex electrodes which are in this area show a link or coupling between theta phase and gamma amplitude this is shown in pseudo color here and that increases during or after learning this is just shown here this is the animal learning to get it right it starts off about 70 percent and it's not until it gets to over 70 percent that we believe that it actually knows what it's doing and you get an increase in the amplitude of theta the theta gamma ratio as well and there is an increase in this coherence between theta and gamma and the extent to which these changes occur are significantly and positively correlated to the animals behavioral discrimination performance and we can generate Network models which effectively can define or produce exactly the same results as we get from recordings and the animals will generate these patterns of theta and gamma and and use these models to help us to understand how they modulate neuronal activity and the one thing this is a complex thing but the one thing we found is that as you increase the strength of the coupling between theta and gamma this leads to a progressive D synchronization of neuronal firing in the network that is the neurons are not firing quite as much in time together as they used to and this even without a change in firing rate in the neurons will lead to an amplification of the response of a downstream neuron which is an amazing sort of way of getting potentiation simply by changing the temporal parameters of the firing of neurons so you don't have to actually increase the game you just have to change the difference in the temporal pattern and this goes on both in the model and in fact it also occurs in the temporal cortex so we we went from the model back to the brain and found that the brain actually corresponding to the model it shows us the synchronization so why can you get a distinct realization alone producing some form of potentiation this is a very simple model right and they all you need to know about this model is it it takes these are the excitatory neurons inputting to a downstream neuron and it takes two action potentials reaching this downstream neuron to provoke to actually provoke an output and when you have synchronized patterns same number of action potentials you only get three in this model action potentials produced whereas if when you D synchronize them you get five and the reason for that hopefully is because there's cancelling out this only requires two action potentials to drive this neuron and in some cases there are three occurring at the same time so one is wasted so you don't really want to synchronize information too much in terms of neuronal firing because if they all arrive at the same time that they'll eat the downstream neuron some of them will cancel out and they won't have any and the other thing that you get when you D synchronize patterns like this across a network is that immediately you can see that in time at least this is a much more complex pattern than the synchronized version so you're also generating more discriminable patterns by D synchronizing this is a showing calcium imaging in fact of activity of neurons in the hippocampus and the only reason I'm showing it is because if you look at it carefully with either the top or the bottom they're just different representations of the same thing the neurons are not synchronized and D correlational D synchronization whichever way you want to put it is a very powerful way also of reducing noise so this is a theoretical experiments but where you've got a sine wave with a certain amount of added noise if you negatively correlate the the noise only to a degree of about more point one you can cancel out the noise and leave the signal whereas if you actually positively correlate you amplify the noise more than you do the signal this has actually been well known for a lot of time this is the the basis for central limit theorem in statistics but it's an important principle that it's actually better in a lot of cases to negatively correlate will D synchronize than to correlate doing stuff together might seem like a great idea but in fact in many cases do things things do slightly differently is better and this just shows you that in terms of you when you de correlate you also actually expand the representation of information in physical spaces the red plus is here so you end up with a greater theoretical distance between elements of patterns which makes them more discriminable and yeah we actually found them we thought how great this is good we can patent this this is a great way of them for example cleaning up images or representing images using a negative correlation we did patent it in fact only to find out that in fact it's almost the same time as us engineers had already patented this and it's the most sophisticated way of reducing noise and systems that currently exist so it's nice to know that the brain does things exactly the way there are engineers have worked out is the best way of reducing noise in artificial systems but the the problem was they actually did it before we did I'm going to finish on the most difficult aspect of for all of us I think really of understanding what's going on in the brain there are certain things we absolutely know there is no single seat of consciousness in the brain it's not the pioneer gland as Descartes for once thought when there a really reasonable assumption that there couldn't be more than one seat of consciousness and most structures of course in the brain are bilateral there's a dual representation the Pioneer gland happens there's only one of them but he's wrong of course it's not the seat of consciousness is not the biennial gland many things we already know a process without conscious awareness and often similar patterns of activation are seen when information is processed with all without conscious awareness and that's been quite confusing to us we know there are also different levels of consciousness and how to explain those and individuals may be aware even when they show no obvious signs of consciousness and this is a recent example which I think was quite shocking to many people it's a study was actually carried out in Cambridge and here they train subjects to during fMRI experiments to either imagine themselves walking through their house so it's a spatial imagery and that activates a particular part of the brain or that they're watching someone playing tennis and that activates another part of the brain so it's a direct instruction to think about things and that generates highly distinguishable patterns of activation in the brain what they found was in the study that 10% of patients who were in a defined vegetative state they showed no obvious signs of having the ability to be consciously aware were able to perform this task with instructions so just like control subjects they activations in the motor area or the spatial area and they when went on from this say well okay if we can get them to generate specific patterns of activation in their brains we can get them to answer questions on the basis of let's say motor imagery is yes and spatial imagery pattern is no and they show that they could do this so they ask questions over the sort of what's your father's name is it yes eggs and their yes or no and do you have any brothers yes or no and they were able to use this very simple way of feeding back information on the basis of whether they could generate a spatial or motor imaging that to answer questions so they are capable some of them anyway of conscious awareness even though they show absolutely no signs on other studies have tried to look at very basic changes in consciousness so for example the emergence of a consciousness at first or a hunger for air as a result of manipulations of your physiology and these have shown that you get increases in signals particularly in some key areas of the brain like the cingulate at the point at which or they build up and then they get to their maximum at the point of which the subject reports that they are consciously aware of a raging first or they need to take oxygen other studies and we've also been involved in this have used anesthesia or sleep as a way of trying to understand the difference between a conscious brain and an unconscious one and these studies have shown increasingly that under lack of consciousness due to anesthesia or sleep there seems to be a very large scale loss of cortical integration and in that studies for example the this was shown particularly by a weakening of the feedback pathways from the frontal cortex to the visual cortex back of the brain and so these the normal strong feedback in both directions was broken down and this is a human brain where electrical stimulation is given to a particular area of the brain while the subject is either awake or asleep and the big difference is that when they're awake the elect stimulation causes a integrated change of activity not just where you stimulate the brain but also all around it in a sequence so this crosses the maximum area of activation and you can see it's moved well away from the circle which is the area where the electrical stimulation is given but when the same subjects are asleep this doesn't happen information doesn't seem to transmit around large areas of the brain and we've done recordings in sheep where we're looking at the same thing with recording now electrical activity in three different structures of the brain either when the animals are resting they're not doing anything or they're looking at face pictures and we use a particular method of establishing connectivity which comes from economics it's called Granger causality and won't go into it but it allows us to quantify the strength and direction of functional connections between structures what you can see is that there's a very nice unidirectional flow of information across these three different regions the temporal cortex and also the cingulate when they're in the control state is in the reverse direction that when they're looking at face pictures but when the same animals are anesthetized there's a breakdown of this you know directionality there's no connection anymore between the left and the right hemispheres and there's a sudden increase in the amount of connections within structures so it would appear that there's a kind of loss as the previous slide show of an integrative flow of information across the brain when you lose consciousness and instead we've actually shown that there's also an increase in local processing that's going on and perhaps what's happening is that there's a wide spread in during consciousness integrative flow of activity in the neocortex which generates if you like a meta representation a representation of a representation the representation is the different nodes in the in the in the circuit responding to particular for example certain stimuli but when you have consciousness these are all linked up with a sort of coordinated integrated unidirectional flow of information across structures which forms this meta representation on top of the physical representation whatever it is I mean it's it's an idea but it's a way that it may actually that consciousness may emerge that you once you've got beyond a representation of information to information being that's sort of simultaneously involving a sort of flow of information across wide areas of the brain that turns into this conscious metal representation that we experience and when we information process or information process unconsciously we don't form that meta representation because there's lack of integrated flow between the different cortical processing nodes and indeed you get an increase in information processing within these nodes in order to compensate for the fact that you don't have the ability to generate this meta representation and it's kind of likely you're boosting the simple automatic feedback loops within structures in order to process information because you don't have the ability to form a metal representation caused by the coordinated activity across large areas of the brain but this will also help us that's to understand why it is when you see the same patterns of activation of structures of the brain when you're consciously or unconsciously processing information yes the same structures are activated but when you're actually consciously experiencing whatever it is they're all linked up in a flow which causes the emergence of this meta representation it's an idea anyway what we need though in order to really understand these kind of things and not just consciousness is the ability to look at connectivity within the brain in much more detail and this kind of technology will process that I talked about using this causality uncle of them's this is fMRI data now from humans from 20 Chinese subjects in fact where we have drawn ability to draw connectivity's and resting state between 90 different regions of the brain and this is going to be perhaps the most powerful way of unlocking what is actually going on in the brain either conscious or unconscious or during performance of one behavior compared to another when we start to be able to dissect out the functional connection changes that are occurring not just which areas of the brain are activated or deactivated so in future that means for sure they have to be much stronger links between mathematicians and computer scientists and also because neuroscientists there has to be a greater emphasis on revealing key functional changes in the brain connectivity changes we need desperately really to provide a better understanding of temporal and patterning aspects of neural encoding and we also of course to help us still further need further advances and technologies for measuring the activity of course in real-time of the working brain thank you very much you

Channel: Gresham College

Views: 185,006

Keywords: Neuroscience, Brain, neural encoding, Neuron, Brain Science, Science Research, Neuroscience Research, Neuroscience Lecture, Neuroscience Talk, Biology, Biological Science, Gresham College, Gresham, Lecture, Talk, Free Education, Education

Id: enSy28rBIIM

Length: 62min 41sec (3761 seconds)

Published: Mon Aug 22 2011