Numenta General Neuroscience, 5 Aug 2019

Previous Meeting Next Meeting

⏯

youtube image

►

From YouTube: Jeff Hawkins on Minicolumns & Spatial Pooling

Description

Jeff Hawkins describes minicolumns and Spatial Pooling. Discussion at https://discourse.numenta.org/t/jeff-on-minicolumns-spatial-pooling/6401.

A

A

Okay, here we go you're good to go all.

B

Right so I guess suppose that two weeks ago is a little talked about.

C

In their arms and then last week, student I said hey. That was good, that people liked it, and so we.

B

See what I do another one and I said sure, and we talked about what.

C

Topic to talk about, and so we came up with series of topics and I thought. The not any point of view of next thing would be to talk about columns in many columns. Matt. Do you want me to talk about spatial puller.

D

C

Then any canary or somebody want to talk about Temple memory, I thought I'd start with columns and go from.

B

There it's very informal ask.

C

Questions: I, don't have a big long prepared statements here, so it's really just a bit a little bit of an interaction um but.

B

Just to review what relax left off there's you.

C

Know a lots of neurons in the cortex of course, and roughly 80% of them are excitatory.

B

And those broken at the two broad classes with some people argue deleted one class. So we talked about pyramidal.

C

Cells primarily I'm gonna dull, but find the right place, which you draw like this, which have an apical, dendrite, Babel, dendrite and then.

E

C

B

Then there's the stelae cells, which people have argued that are really the same in the pyramidal cell without one class and then roughly twenty percent of the cells are inhibitory and some people are up to 30 percent and these come in many classes, they're really different.

B

So we can go through some of those differences in a moment here, but there are many many different types of these guys and it's typically they're, classifying and.

C

It's a very amusing area because some people use one set of words up there. The other side.

B

Where the Sun, but the way I'd like to think about this, is that easy, the inhibitory cells are really what implement the algorithm to the brain Marcus. You can jump, please jump in, have anything, that's what I'm saying or get ready if I get anything wrong, so these sort of these sort of like well, you know: how does this thing do something besides just a bunch of neurons right, so the general belief is across the memory is stored in here. Then these synapses, in not all of it.

C

But most of it and then these guys go to control things and they have a different morphologies and shapes and chemical properties and so on and.

B

So the mix together everywhere in the neocortex and so the channels have talked about columns, and so this is also a confusing topic. If you look at a neocortex in a human man, I think we hear you typing I think you need a new, inhuman and your cortex is about two and a half millimeters thick. Your power is bit right and one of the things you see.

B

If you look under this on your microscope, of course, you see lots and lots of neurons, but in some animals some mammals, it's more obvious and others that one level of organization is this thing called a mini column and a mini column is really skinny it's it's literally. It can be anywhere from 50, maybe to.

C

100 microns wide.

B

Or diarrhea depends on the animal depends.

C

A part of the cortex different animals have different assignments, there's somewhere noticeable.

B

In out some animals and the other, and and so you got these guys like organized like this- we'll talk about why they say they exist in a second but they're, this small, roughly roughly in a human there's, 200 million of them.

C

200 million many columns.

B

And the reason you see them is that the cells tend to be clustered in here with the sort of an interstitial between you know. So, if you were to slice the cortex you well, first of all, you'll see this you'll see this sort of clustering here and then I'm just a little gap in clustering.

C

And their relative gap, the clustering irrelevant.

B

Yeah and if you're looking down the surface, you might see sort of what you'd see as he sort of.

B

Areas where there are all these axons coming up together, that would be like Goofy's, vertical axons coming up and down in here and then relative gaps between them, and you can see this visible even in.

B

Some times so these are these are physical things that that seem to exist and and won the debate. It'll be--it'll they do anything because something some animals are harder. The city and other animals are very visible, but that doesn't tell you anything, because you could have the exact same connectivity and not be visibly separated, and you do the same thing and they're all so we know that they're derived from growth, so when an animal's brain starts developing was a progenitor cell or a region that sends these neurons all off.

B

So basically, all of these neurons in this one mini-com seem to come.

C

From the same progenitor cell, so it's an artifact of development.

B

So one can argue, they don't know function at all, but what's really really telling is that when they, the general idea is when they first started sticking probes into the brain, and they found that if you could measure their on belong this, this perpendicular axis the the months that they measured from that, which is a small subset of all the neurons.

C

They would certainly want this is dr. calf, Whoville reisel thing that you know so Catalan some rated signal and they trying.

B

To find cells, it responds that, but when they did that they found the D cell all had similar properties, and if you went over a little bit a different property and then you know a little bit these cells at a different property and soon, as you moved over to the next mini colony or the property, and so it's somehow, these cells all related to each other, or at least the ones are measuring.

B

From again it's a subset of all the cells would be a subset of the excitatory cells, typically in most of the cells that couldn't figure out what they did so, but the ones they click right opens. You know the ones they do respond to this training. You might say: oh, these always find in the computer. Cortex season always poncho a vertical line he's responding to the next one or like this, and these respond to the next one.

B

This isn't these respond, the next one then, and so as they if they move the probe, if they move the probe across at an angle like this, because often what happened.

C

They go through series of responses, but if they can get it to go vertically, they get the same.

B

Response so that strongly suggests that there's a functional property to them.

C

To the many cars and and that's it so that's just a general idea: there.

C

B

The literature is confusing that yeah.

B

I know it's frames. If you get older everything, the brain strengthened, you know, but I think is a misimpression that is pretty much defined at birth. You know and there's this over perfusion of connections which disappear, but it's not something like it grows as you grow in life. It's not like it's getting bigger as you learn things. It's.

C

Pretty much there and and it's shrink see to get older.

B

So there is one of the things that they've observed is basically the rule of thumb. They say, there's about 110 neurons.

C

Could be tiring, you know, makes Tim. This is one number that's banded about and the.

B

Basic idea is that all the different types of cells are you're going to find you ever find interesting event. That was an idea I'm going to been proven, so he basically, this is a unit of computation that seems to incorporate everything and get it all. These things somehow are related in one way or another.

B

So that's the mini column. It's a physical thing. It's there at during development of the brain when you're in utero. It also seems that a functional role, which is known ability of the sign to control, to no one understood why it's like that. We have a theory of whether it is and, and they vary in size- is based on animals. But roughly you can say about 200 million of them, so mal castle, Verner mountain castle is one of the people who really said you know. This is obviously an important functional thing.

B

We need to understand what this thing does. If you understand what this thing does you get 200 million of similar there's one big exception in primates in primates on like so all, mammals have neocortex, but primates v1 that the primary visual cortex is v1 is twice as many cells and we talk about that's a separate topic, but other animals were not. Primates have visual systems like cats, them dogs and they don't have Isis, and then we have these extra layers and b1.

C

In units, somehow we got better vision and we have these extra layers.

B

In here that other mammals don't have, but the dollars can see and dogs don't have.

C

Straight it'd be wanted; they don't have these exercise.

C

B

Choices make cells with many calm down or any any area in v1. So now get to your question about column when coming to the column, you have to be careful, they might be talking about a meanie column, but they shouldn't, they should say mini column, but there isn't another column which, which is the following. You see of this like those these properties here where this is the vertical line right and then you move over at the response to different lines and if you keep going.

B

You get back to a vertical line again and if you turned out that you look it down to the surface of the cortex and you map out like again, this was all done originally in the visual primary visual cortex. You map our circles, our cortex. So now, looking down on the surface of the cortex.

B

So is this a you know server, so the cortex here looking down on the top, and so, if you might see a column that represents a protocol I'm here in a column up in this work on here, and then one of the members here here and in between it is all these different properties.

B

You'll see anyway, if you take the area which represents all the different properties you can get before it repeats again, it's about roughly a millimeter by a millimeter, and so also like one of these many columns may process a particular type of orientation, a particular ie reference right, our accomodation different color things. So there's other properties that are embedded in here and then you go to the next block over it all repeats again, but it took part of that.

B

So this idea of a sort of ensemble that covers all the different properties you might get in some part of the cortex. Essentially it is what is called a column and it can be- and sometimes you can hear, call hyper column.

B

This is not a physical thing in the sense that you can't see it. There's no demarcation here. There's no one says: oh here's the edge of it and here's the next one it's a continuous sheet, but the idea that if you continue repeating in any direction things repeat, you keep going in a direction repeat. So that's the larger column.

D

B

It that's an interesting question, so Bernie now Castle he's one of the few people really study this, and so a lot of the species is old, but he argued that he argued that he saw this type of column organization and the way he would do that he would say. For example, he studied a lot of somatosensory cortex, so he was tonight a century cortex shadow. Imagine I have like a millimeter blocks here. This.

C

Is like 1, millimeter and.

B

He would show that if he put a probe through here with he argued you had, you would have these discontinuities into the area of the part of the body that was represented. So in this section right here is all the cells in this section would be representing. Let's say this was your forearm. Am I representing some area on your and and when you travel around even show you another area forearm, and so he was on. This is processing one area.

B

This is frosting another area, nice bust, another area, in the same way that he was passing one section.

B

So here's a famous paper.

B

Is that makes the argument that this sort of column, the organization exists everywhere everywhere he's looked everywhere, you could measure some of the higher areas of cortex. It's like they don't know what to look for right. You can do this for like auditory cortex. You can do this with visual cortex. You can do this amount.

C

Of sensory cortex anywhere, where you can.

B

Actually, probe the animal and see what's going on, but other parts, the brain is very difficult to say, but he made he made a long argument and a long paper about all the evidence suggests. This sort of column organizations exist everywhere. So it's a nice idea and threateningly. It's a it's a great idea, so, whether it's true or not remains to be seen, but at the moment I take it. Basically, that's the right thing to go until we know otherwise.

E

B

Well, that's consistent with this idea that would be considered cops so in the mouse cortex yeah they have their whiskers are a very active sensing devices and not just hairs right. They sweep them back and forth and they have the various that they can see with their whiskers and some say if they can learn, recognize objects and number things.

B

Are it's a it's like our fingers in some sense, so it's an active sense and what they've discovered and then looking down on them on the rat or mouse in your cortex you'll see the whiskers is a path of whiskers on their face and those whiskers are represented literally Matt. One two one knows his columns: the bigger columns mac to each whisker.

B

In this case, it's an unusual, you know I, think I've interpreted that it's like well, your retina is a continuous sheet. Your skin is itching you see here. The whiskers are not continuous, there's this unique things and each one has its own column. So this is very consistent with non council's basic proposal for columns everywhere and just in this case, they're very visible, so you could study them and look at them, and so these have been. These have been very modest, huge amount known about the.

C

Parallel cortex see it called bowel cortex, because if you look at the slice through here, it was like they look like a barrel. That's barrel.

D

B

You see these literally if you can stain or look at the neurons right. If you have a way of looking at it, you'll see that the cell bodies would eventually connected here with a little gap in between them and more so, as here, neither literally connected each retina each whisker, so they're visually visible barrel. That's what makes them unique, we're in visual cortex and sensory cortex.

B

You don't see the columns, you see the many columns, but you don't see the columns themselves so again, I think that's a uniqueness due to the fact that they, the hairs, are distinct and therefore they cannot this way, but the photoreceptors on the retina or this on your skin are not separated at all in any way. But anyway, these are neither actually should one think of these are either on the small side. I believe these were about half a millimeter each, but they were still qualified as a column.

B

So these are two dimension.

B

Well, I'm bigger a little actually is more than three dimensions to because you've got depth, you got ocular dominance and you've got color. You got a bunch of stuff, they not so the general rule is, it seems like what cortex does or is bringing the revolution is not it takes multiple dimensions and you have multiple dimensions in your skin. You have you have various property of hot and cold sensors. You have sharp mention, sensors, you have vibration, sensors, you have pressure sensors, all these things are matter and they all sort of overlay.

B

On top of this, when you look at a column in the cortex I, whenever I'm talking about how the visual hyper column of us known is cleared in the air, you have all those dimensions, mat and the imagine these kind of weird little overlapping wavy patterns. But the point was that within one square millimeter, whatever property you have is going to repeat so that's the property of orientations can repeat if it's the property.

C

B

Dominance, it's going to repeat like let thy right eye if it's property of.

B

Any proper you have would repeat within that so they're sort of all interlaced on top of one another, but the general believe that was in a mini column. It's one set, so my mini column represents this set of end properties. You know, and so you have a sort.

C

Of mixture of these things, also in on top there, you see these many poems with in.

A

Certainly can put senses, but you also see these like in potential information with you also cities. Many come from like higher levels higher well.

B

I've always said in neurosciences, there's exceptions to every rule and no matter what you say, someone's gonna say it's wrong. The answer is yes. With that cap guy right, someone's.

E

Gonna say: well: we don't everywhere.

B

Or I look at this animal, we look at the aardvark and the aardvark doesn't have an item sees whatever, but that kind of thing. So the general idea is for those who, like or threateningly, inclined and say yeah we've seen the cd's everywhere. It wasn't.

D

Interesting, for example, some.

B

People used to say: well, we don't see many columns in mice, just don't see them, and then that was the standard rule for many many years recently. I think we saw paper, it says all I found any comps in mice.

B

You know who knows so I think from a theoretical point of view, we should go on the assumption of any cons exist everywhere.

C

B

They're not visible because again you don't have to be visible, have the same functional structure right if the neurons are all connected exactly the same way, and you just shift them around a little bit. You're still gonna do the same thing so so with the physicalness of it is not essential, but the fact that we do see them almost everywhere, I think we should assume didn't comps, insist everywhere. That would be the that would be that's my assumption and I. My assumption, I bind to map has the argument that coms exist. Oh, Richard,.

C

Even if we can't.

D

B

His main thing he's think was.

C

B

That his in his first big paper about this, he said many columns as a organizing principle and then later he argued the column for the organizing. You know both and he got a lot of pushback, initially he's initial papers. On this. He argued that the column was dynamic, that it could, under varying conditions at moment-to-moment, could change. Then he retracted that I think his last thing was. He says no I think there fits the Vicks act alarming if you remember anything different Marcus, because what I know that that last part you're my source, okay.

B

Well, there we go.

B

Represent because you can take this video, you take a piece of cord X you plug in something else into it. It represents something else so whether the size of dynamically, the questions and including what it represents is dynamic, for example, the side it could be determined by somebody's inhibitory cells that just span of certain dimension, and they say that's going to define the width of a column you know like I can.

D

B

Cell and it said, okay, I'm gonna inhibit this millimeter by area and that's gonna define the size of a column, so I'm working assumptions all along the boys been many columns are fixed in size. They have, they exist everywhere. The the larger columns exist everywhere.

B

They're different sizes, different animals, just like many columns, but it's a general property of the cortex, and this idea that you multiple, multiple dimensions on top of a region of cortex and they map into a column and all those dimensions are all represented within that in that area, seems to be the general rule that how these things go.

B

So what now we can talk about? The.

D

Remaining columns.

B

So they probably.

B

What they do they do all this stuff, but the general rule would be that if you D innervate, any part of the cortex so no longer receives an input from something I could cut the axons or something like that. What will generally happen is all these I, don't say it's true for the right barrel: cortex, because that could be totally hardwired by evolution right, but the general principle would be forgiving. We did this to your visual cortex or your auditory cortex or some other project cortex.

B

The general thing would be view view D animate, some part of it that those that basically everybody reshuffles to what they did fill in the hole. You won't have this remaining gap here. Similarly, if I cut out part of the cortex, then what was represented by.

D

B

Part of the cortex now gets represented by here, and everybody moves apart, right, R. That was one of the nice things about our spatial Euler. It has this property that, and we tested this extensively, but a human. If you have a trauma to part of your grandmother-in-law Sephardi brain or you lost some of the inputs to the brain facing there were four months heard. These things will movie to compensate as much as possible. It was a small enough trauma. You can completely recover your abilities.

B

So when someone has an accident or mini-stroke, oh my god, they can't speak or they can't do this. They can't do that. Sometimes within four months they recover.

B

If it's not too big an error because to be there, you're closed so, but if it's smaller and then everybody kind of read all the Act, all the inputs they equalize out and our spatial pool, or does that I was reachable because of their and winner take in winners situation and there's a boosting situation. If some of them, if some of the columns and we'll talk about the spatial enrollment is some of the columns are not getting input, then they will try to connect to the other nearby and when they connect those other inputs.

B

Nearby I was looking at these sort of inputs and then you're over here saying you start stealing some of mine and then I say: okay, you're selling to the land and some of my neighbors and everybody kind of shifts over a little bit and working for the nicely. Now it was beautiful fact. As far as I know, that was a first normal simulation that actually captured that property. You know.

C

And it's kind of cool.

B

So, okay, so that's it for many columns and now I'm gonna throw a spaceship over that's a good time to do that. Okay, so this is like this is an anime. So now we're talking about our theories about this.

B

So one of the things we I want to recreate the whole history here, but we were trying to come up with a sequence, Meyrin algorithm, which became our temporal memory and one of the things we had to do is we had to come up with a way of representing inputs in a sparse way, and so we just took the mini-com idea or I took them any common.

B

Okay, basically doing we're taking some input space and dividing it up right in some sort of basis set I and and so when you think about vision, the monthly simplest basis that would be maybe lying orientation. So if I look at the patterns in some section of the retina, what are the most common patterns? I'm going to see it would be, it would be. The most common would be edges. Were you know the portafilter time so and then it says: okay, but let's.

E

B

Going to divide this up into ten different or 20 different categories, whatever it is I'm going to I'm going to.

B

What we did the spaceship Euler, we sort of said, like magic, each mini column is competing with each other so that they all represent something different and they can only in this case they can only repeat within some distance from each other, so options you get beyond that, the intermission distance they can start representing the same thing again, so that was the basis of the spaceship Euler and and then we said what the spaceship puller is doing is essentially determining the basic function of each mini-com, or you know he bit in in some way and if you give it a visual system, we picked this out and gave it some other and put this and would pick something else out.

B

And then we complex function of untimely me calm. It's really complex, like you've got 110 Styles you've got many many cell types. You've got all these different layers in here, so we just said: why would we just put one simple there like a layer, four we're just getting the infos and within that layer? If I said they were like six layers here, and maybe they would be very roughly I could say there might be 16 cells in here. Just six.

B

And so I'd say: okay in this little section here, I've got, you know a little mini columns and then that's me come over and the next mini column over and the next minute column over the next mini column over and because I say 16 cells and each of these- and each of these is gonna, be representing something different, the spaceship or basically any input and allow you to sparse defy it in such a way that every once in a while, we have to call.

B

Boosting function and learning- and you just feed it data over time it parses up the input space Wow now there's one two things are different in the space pool of the real brain, the real brain. We have this topology, where I.

B

D

Do that we just take.

B

Some number or some input something and we weren't initially we weren't doing this topology we weren't trying to be visual.

B

So we just said: okay, we're just gonna, take a bunch of any comms feed it some variables and we're gonna have them compete with one another and sort of learn how that works. Now a couple of the advantages by the way, another compact more to them special pullin among them, because well you have 16 cells in each one. One of the things we knew from biology is have we didn't have a recognized pattern like hearing the sequence that you or the visual scene or something you can recognize? Well, you have as much sparser activity.

B

This is an observed property of grunts right, so we know the unexpected in input to get a lot of activity and we never expected it to it. You have a and so I simplify that I said. Okay, let's assume that we have an unexpected input. All these neurons become active, but again a predicted input unexpected, but then maybe fewer, not this for a while.

B

Because, essentially, you're active in any college.

B

You can represent that input. That represents an input that says that's what the current input is, but the pendulum we sold you out to me.

B

Right so one of sixteen sixteen way. So if I'm, representing some input with 20 active bits, then each one can be represented is one of sixteen lady got. You got twenty to the sixth or 60 to the twentieth, different ways of representing attempt a huge number. You know and going much you know going up. We did a lot of our 32 now once you're in the good number you're sixteen large.

B

So this idea of these two representations, many common representation, which represents the base input and then individual self active in those many columns represents that and put in a particular context, and the capacity of that is extremely high. So you can represent a statement but in truly different, no problem, so that was one of the core things you needed for for sequence. Remember, it's be able, take an input represent and very high ORAC context, and then.

A

B

I can go through that in more detail if you want, but let me go back to the neuroscience a little bit. One of these ok.

B

Ok, one of the challenges of this sort of new proposal. As far as we know, no one is that the proposals, besides a functional purpose, for why you have these many columns.

B

One of the challenges with this Thank You scholars here just one layer right now why.

C

B

Are missed this thing, I call the cells in the main column, say 16 or so, and.

B

Basically fired at the same time.

B

They all represent the same basic.

B

How do I want to respond to the same thing right, and the second question is: how do they respond? You know, uniquely under different contexts, so the first one is an interesting anatomical problem, which we didn't even put in our further papers about this.

B

Respondent the same thing and one of the.

B

You go back to the inhibitor itself and one of the classes of inhibitory cells essentially defines the meaning column, those various ones, they're basic of a class called bipolar, meaning they have to let funds that go up and down in the very, very skinny metal onto like this and.

B

B

These terms are very changed, sometimes.

B

With very skinny projections vertically up and down like this, and they offended all of these cells, they define in some sense of any column. They are. The functional definition in comes a name is what we learned later was that these cells also have the same orientation property, and they also are learned that foundation property plucked. So this is exactly what we needed, because he.

D

C

Way of forcing all these neurons, which.

B

Actually, we need some walls, basically.

B

So the beginning of a mechanism for set becomes an inventory cells tend to be first of all,.

B

And learn the pattern, and this guy then enforce these guys. So once a matter, and it could do that through a doubled, inhibition.

B

The father, at the same time, kind of mechanism is required for this in the work and.

B

Cool a bit or Facebook mini column has a danger. Excitement that recognizes that the input path well more complicated in the inventory so like this, which recognizes these are also to recognizing.

B

Partially organizer and then there's the other hand, doing the normal operation of the system, the temporal memory with you. So these are like this would be like a bipolar cell or a double portunity cell. There are also these basket cells which stand out there all over the places, the most common inhibitory cell and they're very tightly coupled very pronounced. Now what.

D

We need is what.

B

A gram of stuff spikes and spikes all of its neighbors, but only the spikes a little bit earlier. So we have this mechanism where one cells earlier.

B

So all the mechanisms we need in the temporal memory and the station for all this is support all of them in these basic cell types. So, where again,.

B

The input comes in all these cells, a spike or two, but then what we eat. What we require after that is that then there's gonna be a winner, because we have to take or whatever continuous learning, but initially they all spike. And then we pick a winner or one of those depolarize. We just go right to that one there and we don't so. These mechanisms support that.

C

They do all that stuff. You pick one we're.

D

Meeting on that's.

B

The way we do it any sparse, so you could do two or three there's really matter. There's not much advantage to doing that. So I, don't want to mistake. That's to be one, that's how the Koala, but it has to be sparse, I think you might have done some simulations once early on hoth allow to.

B

Have the set of active cell.

B

B

The assumption is.

B

B

Great really stop the generalization problem.

B

I still think I still go back to I, don't see the difference. It's the same. Three cells became active every time in a particular context. I have to say under I have to say on the two related context, somehow generalize that I would pick a subset of the same cells. I know that it's interesting question. It's also not as simple as this.

B

What you'll see is the neurons here like in this menu calm these neurons to be most active, but these will be a little active. These would be a little active and then less so so it's a graded potential, that's different.

B

So that mean you know the things we're not incorporating into the well. That is like an interesting distinction that that, when.

E

We talk about the spatial cooler. We talk about these many colony codes, those STRs those representations have this overlap: property, where semantically, similar inputs get semantically similar outputs like sperm, whereas once we go to this stuff, this parts a.

B

Sparse vacation and it throws that out the window where that they overlap properties just off, and should we try to begin to bring it back in some way like you just brought it yeah there's a bit of perennial question, so this whole idea of the semantic representation in the SDR and this participation pool output. That's that's the basic of all of the particles work. They start like an old semantic representation, but as Martin's point out soon as we go to this, like random selection in a new we've lost it now, you just have this.

B

You know there's one power and it just means this is the 23rd in a wallop efficient, beethoven's, you know 4/5 and that's all it means, and it doesn't mean anything else, it's ass it more than thing you need in the world. I can't relate it to something else. This idea of lack of generalizations always bothered me, but I never saw way out of it and more in my recent work and what Mark has proposed with the displacements of else I'm more hopeful about that I think this placement cells inherently represent.

B

They give us the ability to generalize and that's a longer topic, but though,.

C

The generalization may be occurring elsewhere, like.

B

Modeling one little layer of cells there's a lot of time here, and so you know our goal all along.

B

And a lot of stuff done recently is into that. So I think I. Think Jeff relation is a more complex problem. My current guess is: if it's not solved in this input layer- and that is.

C

Solved in other mechanisms in the calm.

B

So I was just talking about station pool I didn't go through it in detail. I totally gave you the biological relevance for it. Is there any questions about that.

B

This idea that individual self permanent they characterize what a cell represents. They do that by giving a sort of a vision, they would do that make every sign of song ratings.

B

And so okay, this all seems prefer. You know this type of visual input. Then they show that visual input to that cell, but in the context of sequences like videos- and so they know that the cell on the retina mean they know the.

D

Retina is getting the saxy.

B

Pattern and goblet responding the first time right, but now it's in some temporal and spatial content and what they've shown is that these cells do not fire most of the time when you would expect them the fire. So they only be spark certain parts in the video and they fire fairly reliably at that point, so they seem to say I'm gonna fire that particular context 119 put but mostly other times like.

B

And so that data fits beautifully with all this and I haven't seen anything I can enroll at each other now, but they've done this vision and the general rules seem supply even sort of the level of when I read those papers. I say always defining about the right now hi, you know, and it is so there's a lot of evidence that this basic mechanism is actually under play paper. So people look at me, how could you know you know?

B

Why is it do that by firing, but mostly research was done before we publish the stuff, so we can explain that I think it's a stronger.

B

They all fire at the same time.

B

To respond to this input.

B

So that's not really I mean.

B

B

Think they don't test that again, but they say okay, what does this cell respond to responds reliably under under these very basic test conditions? The way they determine they come up with? What does that sound like and they require to realize? Okay, that's all your fires from this condition, and then they put it in a temporal context of realistic context, both temporal and spatial context, and it doesn't fire you're liable. It's still only only fires when they have that input, but it doesn't do it on the most complex.

B

So it's still representing the same thing, but most times it's inhibited and stuff and fire yeah do they have solution.

E

Is to the visual system situation curses and the.

B

Situation is this that I did that that neurons stop responding, because you know they could have to act right, okay, right! That's what habituation is they're not like. You keep kidding me my office. They stop hitting them your honor. They keep going up.

C

Things get tired, they stop responding, you know, and after a while, you have to do something.

B

Else so an iteration is not generally fair to do it. As a I mean you can download this work with a pussy-ass snail, they have a simple sea slug and they hope they poke it enemy, traction skill, that's that's its behavior, you know, and then, after a while, you keep poking in people. Then, after a while, it doesn't attract them skills, that's kind of learning and you might call that habituation. Not they call it line.

B

I think will determine situations usually use is the things get soda either no I, don't associated learning I associated with a slight you know, wearing out stops responding after one I just wanted through this more active form of it, because when you stare at visual pattern.

E

There's a tendency.

B

Freeze the eye right yeah, so you can't do that, but if someone actually does that but yeah they actually immobilize the eye or they equivalent when they moved it in because the eye, then you stop seeing it.

B

So that's a well-known fact: I, never really thought about as a reticle important function to me, it's more like okay, the brain is built on tumble processing. If you stop.

C

Giving a temporal patterns- it's just everybody, it's hard to stop firing! That's a.

C

B

This was wondering you so yeah I.

E

B

We thought about this properties like that. Oh really, there's a bunch of physiological properties that cells do that are incredibly important. So the fact that the photocell is naked, we're now not really threat important. That's what.

C

B

C

You you deplete your you know some, your energy, it's hard! Well, okay, fine, but.

B

Yeah so that was talked about today, I just go through the mini columns and the columns, and if we do this again, maybe we talk about layers.

B

All right, we do what you want to.

B

Be done this session thanks Jeff.

D

B

D

Right I'm going to stop the stream thanks, everybody for watching.