Why I’m Building a Model of the Bee Brain | Andrew Barron | TEDxFulbrightSydney

hello I’m building a model of the bee

hello I’m building a model of the bee brain and I was at a conference recently

brain and I was at a conference recently

brain and I was at a conference recently talking about what I do and I got a

talking about what I do and I got a

talking about what I do and I got a question do bees have brains and I went

question do bees have brains and I went

question do bees have brains and I went off on this mortified pearl clutching

off on this mortified pearl clutching

off on this mortified pearl clutching tirade of how could anybody in 2017

tirade of how could anybody in 2017

tirade of how could anybody in 2017 possibly imagine that bla bla bla bla

possibly imagine that bla bla bla bla

possibly imagine that bla bla bla bla bla bla bla

bla bla bla

bla bla bla and as usual it took my husband to talk

and as usual it took my husband to talk

and as usual it took my husband to talk me down and point out that there are

me down and point out that there are

me down and point out that there are plenty of good reasons why intelligent

plenty of good reasons why intelligent

plenty of good reasons why intelligent well-educated normal people emphasis not

well-educated normal people emphasis not

well-educated normal people emphasis not you may not have thought much about bees

you may not have thought much about bees

you may not have thought much about bees and their brains so this is my attempt

and their brains so this is my attempt

and their brains so this is my attempt to explain what I’m doing and why now

to explain what I’m doing and why now

to explain what I’m doing and why now that’s a model of the sorry that’s an

that’s a model of the sorry that’s an

that’s a model of the sorry that’s an image of the bee brain created by

image of the bee brain created by

image of the bee brain created by Angelique Polk and two key you into this

Angelique Polk and two key you into this

Angelique Polk and two key you into this scale that black bar at the bottom is

scale that black bar at the bottom is

scale that black bar at the bottom is one millimeter so we’re looking at a

one millimeter so we’re looking at a

one millimeter so we’re looking at a millimetric Li scaled structure but what

millimetric Li scaled structure but what

millimetric Li scaled structure but what I want you to focus on is first of all

I want you to focus on is first of all

I want you to focus on is first of all the intrinsic beauty of this but also

the intrinsic beauty of this but also

the intrinsic beauty of this but also its complexity so the bee brain is is

its complexity so the bee brain is is

its complexity so the bee brain is is small but the bee brain is not simple

small but the bee brain is not simple

small but the bee brain is not simple the bee brain like all brains is made up

the bee brain like all brains is made up

the bee brain like all brains is made up of neurons neurons are just nerve cells

of neurons neurons are just nerve cells

of neurons neurons are just nerve cells which are cells that are specialized to

which are cells that are specialized to

which are cells that are specialized to make connections and transmit

make connections and transmit

make connections and transmit information and even in a brain this

information and even in a brain this

information and even in a brain this small there’s just shy of 1 million

small there’s just shy of 1 million

small there’s just shy of 1 million neurons now I’m making a model of this

neurons now I’m making a model of this

neurons now I’m making a model of this now that doesn’t mean I’m sitting in a

now that doesn’t mean I’m sitting in a

now that doesn’t mean I’m sitting in a shared making it out of matchsticks I I

shared making it out of matchsticks I I

shared making it out of matchsticks I I would like to do that but I wouldn’t get

would like to do that but I wouldn’t get

would like to do that but I wouldn’t get paid to do that so my model looks like

paid to do that so my model looks like

paid to do that so my model looks like this so this is a model I’ve made with

this so this is a model I’ve made with

this so this is a model I’ve made with my colleague Alex cope of part of the B

my colleague Alex cope of part of the B

my colleague Alex cope of part of the B brain it’s a high-level abstraction it’s

brain it’s a high-level abstraction it’s

brain it’s a high-level abstraction it’s a computational model where each neuron

a computational model where each neuron

a computational model where each neuron or group of neurons is represented as a

or group of neurons is represented as a

or group of neurons is represented as a connection that transmits information so

connection that transmits information so

connection that transmits information so it’s an it’s a model of the brain as an

it’s an it’s a model of the brain as an

it’s an it’s a model of the brain as an information flow Network now why am I do

information flow Network now why am I do

information flow Network now why am I do this well I wanted to think back to you

this well I wanted to think back to you

this well I wanted to think back to you the last time you had an encounter with

the last time you had an encounter with

the last time you had an encounter with a B however that went for you whether

a B however that went for you whether

a B however that went for you whether you may have got stung that was probably

you may have got stung that was probably

you may have got stung that was probably your fault but what you will have

your fault but what you will have

your fault but what you will have realized in that is that bees are smart

realized in that is that bees are smart

realized in that is that bees are smart they are incredibly smart and to show

they are incredibly smart and to show

they are incredibly smart and to show you just how smart look at this video

you just how smart look at this video

you just how smart look at this video from last chick ker Ollie Luke Ola and

from last chick ker Ollie Luke Ola and

from last chick ker Ollie Luke Ola and Clint Perry

Clint Perry

Clint Perry so this bee has learned that if it moves

so this bee has learned that if it moves

so this bee has learned that if it moves that yellow ball into the yellow circle

that yellow ball into the yellow circle

that yellow ball into the yellow circle the well beneath the ball fills up with

the well beneath the ball fills up with

the well beneath the ball fills up with nectar and it gets a drink and that’s a

nectar and it gets a drink and that’s a

nectar and it gets a drink and that’s a bee now if you wanted to you could

bee now if you wanted to you could

bee now if you wanted to you could describe that as a form of tool use and

describe that as a form of tool use and

describe that as a form of tool use and all of that intelligence all of that

all of that intelligence all of that

all of that intelligence all of that smarts come somehow from the bee brain

smarts come somehow from the bee brain

smarts come somehow from the bee brain and I want to understand this be level

and I want to understand this be level

and I want to understand this be level of intelligence but to do that I’ve got

of intelligence but to do that I’ve got

of intelligence but to do that I’ve got to understand how the bee brain works

to understand how the bee brain works

to understand how the bee brain works and that’s a mighty question even for

and that’s a mighty question even for

and that’s a mighty question even for something like a bee brain so how can we

something like a bee brain so how can we

something like a bee brain so how can we go about dealing with a question with

go about dealing with a question with

go about dealing with a question with that kind of complexity well one

that kind of complexity well one

that kind of complexity well one approach would be we could take the

approach would be we could take the

approach would be we could take the brain to simpler bits we could work out

brain to simpler bits we could work out

brain to simpler bits we could work out what the bits do and then put it back

what the bits do and then put it back

what the bits do and then put it back together the problem with that is for

together the problem with that is for

together the problem with that is for brains what matter are the connections

brains what matter are the connections

brains what matter are the connections when you take it apart there comes a

when you take it apart there comes a

when you take it apart there comes a point where you realize all you’ve done

point where you realize all you’ve done

point where you realize all you’ve done is get good at breaking it so in other

is get good at breaking it so in other

is get good at breaking it so in other approaches we could map it we could we

approaches we could map it we could we

approaches we could map it we could we could learn the structure of the brain

could learn the structure of the brain

could learn the structure of the brain because a map is a form of understanding

because a map is a form of understanding

because a map is a form of understanding and the nice thing about contemporary

and the nice thing about contemporary

and the nice thing about contemporary neuroscience is we can do that we’ve got

neuroscience is we can do that we’ve got

neuroscience is we can do that we’ve got techniques now to build incredibly

techniques now to build incredibly

techniques now to build incredibly detailed maps of the brain but brains

detailed maps of the brain but brains

detailed maps of the brain but brains are dynamic structures it’s dynamic

are dynamic structures it’s dynamic

are dynamic structures it’s dynamic information flow and a map therefore

information flow and a map therefore

information flow and a map therefore doesn’t give you the dynamics it’s only

doesn’t give you the dynamics it’s only

doesn’t give you the dynamics it’s only part of the story

part of the story

part of the story so another approaches we could observe

so another approaches we could observe

so another approaches we could observe brains working and with contemporary

brains working and with contemporary

brains working and with contemporary neuroscience we can do that we can look

neuroscience we can do that we can look

neuroscience we can do that we can look at living neurons in living brains of

at living neurons in living brains of

at living neurons in living brains of living animals that doing their thing

living animals that doing their thing

living animals that doing their thing the problem with that is there were so

the problem with that is there were so

the problem with that is there were so many neurons with so many connections

many neurons with so many connections

many neurons with so many connections that you really quickly almost start

that you really quickly almost start

that you really quickly almost start drowning in the volume of data that

drowning in the volume of data that

drowning in the volume of data that you’re generating there is so much

you’re generating there is so much

you’re generating there is so much detail it’s hard to step back

detail it’s hard to step back

detail it’s hard to step back and find the meaning of your system from

and find the meaning of your system from

and find the meaning of your system from the mass of detail of all of these newer

the mass of detail of all of these newer

the mass of detail of all of these newer ones with all of these complex

ones with all of these complex

ones with all of these complex connections and that’s where the

connections and that’s where the

connections and that’s where the modeling approach really comes in so

modeling approach really comes in so

modeling approach really comes in so model is is really scientific jargon for

model is is really scientific jargon for

model is is really scientific jargon for best guess it’s a hypothesis so when

best guess it’s a hypothesis so when

best guess it’s a hypothesis so when you’re building a model what you do is

you’re building a model what you do is

you’re building a model what you do is you take everything that you know about

you take everything that you know about

you take everything that you know about the brain and you ask how could this

the brain and you ask how could this

the brain and you ask how could this work as a system the things that are

work as a system the things that are

work as a system the things that are known are what model is call constraints

known are what model is call constraints

known are what model is call constraints because the rule of modeling is you

because the rule of modeling is you

because the rule of modeling is you can’t make anything up you can’t imagine

can’t make anything up you can’t imagine

can’t make anything up you can’t imagine connections that don’t exist you can’t

connections that don’t exist you can’t

connections that don’t exist you can’t imagine neurons that aren’t there so

imagine neurons that aren’t there so

imagine neurons that aren’t there so within those constraints of the biology

within those constraints of the biology

within those constraints of the biology that constrains the kind of model you

that constrains the kind of model you

that constrains the kind of model you can make and then you ask yourself okay

can make and then you ask yourself okay

can make and then you ask yourself okay given these constraints how could this

given these constraints how could this

given these constraints how could this work as a system and that becomes your

work as a system and that becomes your

work as a system and that becomes your model and once you’ve got your model

model and once you’ve got your model

model and once you’ve got your model then your job is to test the model and

then your job is to test the model and

then your job is to test the model and that’s also not straightforward to test

that’s also not straightforward to test

that’s also not straightforward to test a model you’ve got it on a different

a model you’ve got it on a different

a model you’ve got it on a different thought experiment you ask yourself if

thought experiment you ask yourself if

thought experiment you ask yourself if this models right what have we learned

this models right what have we learned

this models right what have we learned from it what can we infer from it and

from it what can we infer from it and

from it what can we infer from it and then you test whether that inference is

then you test whether that inference is

then you test whether that inference is actually true so to give you an example

actually true so to give you an example

actually true so to give you an example I want to talk about the work of Monday

I want to talk about the work of Monday

I want to talk about the work of Monday I’m suin Avastin who gave a brilliant

I’m suin Avastin who gave a brilliant

I’m suin Avastin who gave a brilliant TED talk a couple of years ago now

TED talk a couple of years ago now

TED talk a couple of years ago now swinney studies insect flight control

swinney studies insect flight control

swinney studies insect flight control and he had a model of how insects

and he had a model of how insects

and he had a model of how insects control their behavior in flight is

control their behavior in flight is

control their behavior in flight is their coming into land but one of the

their coming into land but one of the

their coming into land but one of the weird consequences of Sweeney’s model

weird consequences of Sweeney’s model

weird consequences of Sweeney’s model was that if his model was right he

was that if his model was right he

was that if his model was right he proposed that insects aren’t judging

proposed that insects aren’t judging

proposed that insects aren’t judging distance in meters or kilometers they’re

distance in meters or kilometers they’re

distance in meters or kilometers they’re judging distance by how much they’ve

judging distance by how much they’ve

judging distance by how much they’ve seen which sounds kind of weird to us so

seen which sounds kind of weird to us so

seen which sounds kind of weird to us so Sweeney designed an experiment to test

Sweeney designed an experiment to test

Sweeney designed an experiment to test how insects judge distance and found yet

how insects judge distance and found yet

how insects judge distance and found yet actually insects aren’t judging distance

actually insects aren’t judging distance

actually insects aren’t judging distance by distance they’re judging distance by

by distance they’re judging distance by

by distance they’re judging distance by how much they’ve seen now that finding

how much they’ve seen now that finding

how much they’ve seen now that finding didn’t prove that Sweeney’s flight

didn’t prove that Sweeney’s flight

didn’t prove that Sweeney’s flight control model was right but it did mean

control model was right but it did mean

control model was right but it did mean there was no reason for Sweeney to

there was no reason for Sweeney to

there was no reason for Sweeney to imagine his model was wrong

imagine his model was wrong

imagine his model was wrong and when you’re modeling being wrong is

and when you’re modeling being wrong is

and when you’re modeling being wrong is good because of all the possible million

good because of all the possible million

good because of all the possible million ways that I could imagine a brain to

ways that I could imagine a brain to

ways that I could imagine a brain to work if I’m wrong about something I can

work if I’m wrong about something I can

work if I’m wrong about something I can categorically rule out all the

categorically rule out all the

categorically rule out all the possibilities I have now proven to be

possibilities I have now proven to be

possibilities I have now proven to be wrong and I’ve also learned a new

wrong and I’ve also learned a new

wrong and I’ve also learned a new constraint so what I can do is recast my

constraint so what I can do is recast my

constraint so what I can do is recast my model taking into account this new

model taking into account this new

model taking into account this new constraint and try again and by

constraint and try again and by

constraint and try again and by iterating that process you incrementally

iterating that process you incrementally

iterating that process you incrementally step closer and closer towards the truth

step closer and closer towards the truth

step closer and closer towards the truth of the system but along the way you’re

of the system but along the way you’re

of the system but along the way you’re also learning what matters for your

also learning what matters for your

also learning what matters for your system from what’s just detail so along

system from what’s just detail so along

system from what’s just detail so along the way you’re approaching the truth but

the way you’re approaching the truth but

the way you’re approaching the truth but you’re also learning the meaning from

you’re also learning the meaning from

you’re also learning the meaning from the detail now the beauty of this be

the detail now the beauty of this be

the detail now the beauty of this be brain modeling project is what we will

brain modeling project is what we will

brain modeling project is what we will learn from it

learn from it

learn from it so we will learn how intelligent

so we will learn how intelligent

so we will learn how intelligent self-directed autonomous behavior

self-directed autonomous behavior

self-directed autonomous behavior emerges from the circuits of the B brain

emerges from the circuits of the B brain

emerges from the circuits of the B brain now that’s just B level sure but this

now that’s just B level sure but this

now that’s just B level sure but this project is not going to stop with a B

project is not going to stop with a B

project is not going to stop with a B because once we’ve got a model of a B

because once we’ve got a model of a B

because once we’ve got a model of a B brain we can use that as its self a

brain we can use that as its self a

brain we can use that as its self a model to explore how larger and more

model to explore how larger and more

model to explore how larger and more complicated brains could work including

complicated brains could work including

complicated brains could work including our own now I’m not saying that the

our own now I’m not saying that the

our own now I’m not saying that the human brain is just a Big B brain that’s

human brain is just a Big B brain that’s

human brain is just a Big B brain that’s absolutely not what I’m saying

absolutely not what I’m saying

absolutely not what I’m saying but our brain like the B brain is made

but our brain like the B brain is made

but our brain like the B brain is made out of neurons and the neurons in our

out of neurons and the neurons in our

out of neurons and the neurons in our brain the way they work is actually very

brain the way they work is actually very

brain the way they work is actually very similar to the way the neurons in a pea

similar to the way the neurons in a pea

similar to the way the neurons in a pea brain work and the way our neurons make

brain work and the way our neurons make

brain work and the way our neurons make connections and transmit information is

connections and transmit information is

connections and transmit information is actually very similar to the way neurons

actually very similar to the way neurons

actually very similar to the way neurons make connections and transmit

make connections and transmit

make connections and transmit information in the B brain but here’s

information in the B brain but here’s

information in the B brain but here’s the kicker our brain is 80 thousand

the kicker our brain is 80 thousand

the kicker our brain is 80 thousand times larger than the B brain so our

times larger than the B brain so our

times larger than the B brain so our brain contains eighty thousand million

brain contains eighty thousand million

brain contains eighty thousand million neurons that’s an eight with ten zeros

neurons that’s an eight with ten zeros

neurons that’s an eight with ten zeros after it so just take a moment to

after it so just take a moment to

after it so just take a moment to appreciate the scale of that number now

appreciate the scale of that number now

appreciate the scale of that number now that is a legitimately dizzying level of

that is a legitimately dizzying level of

that is a legitimately dizzying level of complexity and I would argue that we’re

complexity and I would argue that we’re

complexity and I would argue that we’re never going to understand a system that

never going to understand a system that

never going to understand a system that complex by brute

complex by brute

complex by brute for Salone we’ve got to be smart and one

for Salone we’ve got to be smart and one

for Salone we’ve got to be smart and one way that we can be smart is we could

way that we can be smart is we could

way that we can be smart is we could take our understanding of the be brain

take our understanding of the be brain

take our understanding of the be brain and use that as a model for certain

and use that as a model for certain

and use that as a model for certain aspects of the human brain so the B

aspects of the human brain so the B

aspects of the human brain so the B brain model will become our first best

brain model will become our first best

brain model will become our first best guess it will become our way in to start

guess it will become our way in to start

guess it will become our way in to start to understand the complexity of the

to understand the complexity of the

to understand the complexity of the human brain and ultimately that’s why

human brain and ultimately that’s why

human brain and ultimately that’s why I’m building a model of the B brain it’s

I’m building a model of the B brain it’s

I’m building a model of the B brain it’s not just to understand B’s in their

not just to understand B’s in their

not just to understand B’s in their behavior it’s because ultimately

behavior it’s because ultimately

behavior it’s because ultimately modeling the B brain is going to help us

modeling the B brain is going to help us

modeling the B brain is going to help us understand the human mind thank you