DIEGO: Good
afternoon, everybody.
Welcome to another seminar
hosted by the Center
for Brains, Minds, and Machines.
Today the center
is happy to host
Dr. Julio Martinez-Trujillo
coming from Canada.
So he's enjoying the
weather, even though it
looks really bad in here.
A little bit about him--
Julio did, first, his
medical degree in Cuba,
finished in 1991.
And after residencies, ended
up as an assistant professor
in the Javeriana University
in Bogota, Colombia.
One interesting thing
is, as I tell this, try
to plot in your minds what the
temperature of these places is
and see the trend.
So going from Cuba down to--
up in the Andes Mountains.
There, as assistant professor
he got some experience
in neurology, looking
at some patients
with certain deficits
that involved attention.
That's where things kind
of went in that direction.
He realized that perhaps
being a medical doctor had
its limitations in the
contributions you can do,
and that, perhaps,
as a researcher
there are other things
that he could contribute.
And so he went on.
He decided that he actually
wanted to advance the field,
rather than just
directly treat patients.
And then he chose to then go
to start his first master's
and doctorate in the
University of Tubingen
under the supervision of
Steffen Troie and Peter Tier,
which he completed with a
magna cum laude in 2000.
Now, being in Germany,
a little colder,
he decided that's not
cold enough for him.
So he decided to
head, now, to Canada.
And ended up doing his
post-doctoral fellowship
at York University
with Dr. [INAUDIBLE]..
And there he did, also,
very exciting research
on eye movements and
the relationship to what
he was doing in attention.
Just to not be quite satisfied
with the temperature,
then he decided that
he wanted to move
from Toronto to Montreal.
Ended up in one of the most--
one of the coldest cosmopolitan
cities in the world.
And I think there he
thought that was enough.
At McGill, he became
associate professor
in the department of physiology.
And he started
his own lab there,
which I had a great pleasure to
be part of for several years.
I would almost say too many, but
it was so productive, I can't.
So it's, you know,
Julio's close to my heart.
I can say that he is
beyond a researcher,
a neurophysiologist.
He takes care of his
people beyond that
he knows how to bring
some of his personality
into his people,
to tell him how--
tell them about
how to do research.
He cares about those things.
He doesn't just want to
get things published.
He wants to discuss.
He wants to get--
he wants passion in it.
And that's something
that gets you
excited about doing research.
And that's invaluable.
Then, as I said, he had enough
cold, so he ended up heading,
after several years, in 2014, to
University of Western Ontario,
or Western University
now, where he is now
full professor at the
departments of physiology,
pharmacology, and psychology.
And now moved his lab to
Western and is running it also
at the Roberts Research
Institute and the Brain
and Mind Institute.
And there, his lab
continues to thrive.
And he has been able to
introduce a whole lot of really
interesting topics.
So now, the initial
studies on attention
that he did with
Steffen Troie are now
a very small portion of all the
really exciting things he does.
So he went on to
studying memory,
short-term and long-term
memory circuits and learning,
and eye movements, and autism.
And I don't have enough
breath to talk about them.
And I think I should let Julio
tell you everything about it.
So today's talk is titled
probing memory circuits
in the primate brain from single
neurons to neural networks.
So without further ado,
please help me welcome
Julio Martinez-Trujillo.
[APPLAUSE]
DR. JULIO MARTINEZ-TRUJILLO:
So I really
thank Diego for this
generous introduction.
Maybe I don't
deserve it that much.
But I want to say that
I'm very happy to be here.
I think that this is a
great place to do science.
And definitely thank you
very much for the invitation.
And all what I have seen
today is just amazing.
I also want to apologize
for a small misspelling
in pharmacology.
It's not pharmacolary,
it's pharmacology.
Oh, for the lighting?
DIEGO: Yes, better lighting.
DR. JULIO MARTINEZ-TRUJILLO:
That sounds good.
So to illustrate a little
bit about the research
we're talking about
today, this is a small--
oh, yeah, that's that.
This is a video game that
some of my students in the lab
program--
for fun.
It's called Game of Runes.
So what you have is a subject
that basically got into these--
supposed to be some
kind of magic stone.
So you pay close
attention to that.
And now there are two
runes, that they're
going to appear there.
You're going to remember those.
Then, you have to go
through this corridor.
I mean, the graphics needs
to be upgraded because that
was on RealEngine 3.
But now, I think that it
should do better than that.
Now, you choose those runes on
this big cave of the droids.
And then you get into this tomb.
And if the runes are
correct, you keep going.
If the runes are
incorrect, they dumped you
into some kind of dungeon.
I don't want to go into that.
So, basically, in
this task, you need
to pay attention to the runes.
You need to keep in
short-term memory
some representation
of these runes.
And actually, you keep--
if you keep doing the
task again and again,
you're going to probably learn
the runic alphabet, which
is somewhere in northern
Germany and Holland today.
I'm going to be
talking about two
aspects of these cognitive
functions-- short-term memory
and long-term memory
offline storage.
So I always say that a
challenge to neuroscientists
is to find out how are neurons
interconnected in the brain,
within and between
brain regions,
and how do they interact
to produce these functions?
I always try to--
I'm going to show
you these dots.
And I'm going to start
naming some of these dots,
and you're going to
tell me what that is--
probably, some of you will.
Diego, don't say it.
Anyone know what that is?
So that's a picture of
the Montreal subway.
So and I feel that pretty
much where we're going,
it's like if we go
3,000 years ahead,
and we go to the
city of Montreal,
and we have to start
digging and discovered
how these people transported--
go from place A to point B.
What we're doing in the brain
is trying to pump the brain
and trying to do fMRI
[INAUDIBLE] from the nodes
of this network, and to do
anatomical studies trying
to find the connectivity
between the nodes,
and then trying to find out how
many passengers are transported
from one train
station to another--
so at every given moment.
So knowing the
structural connectivity,
you don't know the functional
connectivity or the function
still.
But the worst thing is
that now, these nodes
they don't look all alike.
So in all the brain
areas, they don't have
the same anatomical structure.
So, in the talk, I'm going
to talk about calling up
short-term memory by single
neurons and neural ensembles,
mainly in the prefrontal cortex.
Also, I might touch some
other areas of the brain.
The coding of long-term
memory by single neurons
and neural ensembles
in the hippocampus.
So the macaque brain--
information goes into the LGN.
And from there, it goes to V1,
and going to two main pathways.
And information also
flows back into other--
into area.
So there's many connections--
feedback connections
and feed forward connection.
A definition of visual
working memories--
the maintenance and manipulation
of visual information
relevant to behavior for
short time intervals,
generally seconds.
I'm going to stick to this
definition of working memory,
mainly to the maintenance
aspects of working memory, more
than manipulation, which is what
most electrophysiologists do--
working in non-human primates.
So several decades ago,
Funahashi and Goldman-Rakic,
actually--
they described those cells in
the lateral prefrontal cortex
of monkeys.
That if you train a
monkey to remember
one of these different
spatial locations,
you see that during the period
when you turn the targets off
and the monkey's remembering
the location of the target,
you see this increasing
spike in activities.
That's what you call
a persistent activity
or sustained activity.
And this is the most
accepted neural correlate
of working memory, so far.
So, however, people thought
that this persistent activity
was restricted to
association cortices.
But new findings in
the fMRI literature
found that people
could decode, actually,
from visual areas, the
contents of working memory.
So that poses the
question-- is, really,
working memory something that
is neural correlates found only
in regional areas?
Or this is something
that is found, also,
in several other brain
areas, like, for example,
sensory cortices?
So we set in the quest
to answer this question.
And that was Diego's project.
And we talk about how we
could map different brain
areas with the same
task and actually
look at the neural
correlates in working memory.
So what we decided to do was
to put different electrodes
in different brain areas.
One was in MT, the
other in MST, and
the other in the lateral
prefrontal cortex
of macaque monkeys performing
a working memory task.
And to examine whether working
memory actually exist--
the correlates of
working memory exists
in these three different areas.
So, by the way,
these areas actually
project to each other
forward and feedback.
So here is the
results in one slide.
I mean, I can't
summarize all the work.
But I have to move on because I
want to show some of the data.
So in area MT, this is
actually the sensory response
to this sample, where what
the animal has to remember
that was in motion direction.
And what you see here,
in the delay period,
would be the response during
the working memory period.
So it's to see if these
cells actually responds
during this delay period.
And gradually, to these
four different stimuli,
where motion directions-- we say
that these cells encode working
memory--
or encode working memory
for motion direction.
MT didn't show the pattern.
But MST and the
lateral prefrontal
cortex-- they both show
persistent activity
that represented the
contents of working memory.
So, from this work,
we conclude, actually,
that working memory is presented
in association cortices MST
and area 8A 46 in the
lateral prefrontal cortex.
I'm going to use a 8A 46 and
lateral prefrontal cortex
interchangeably
here, in this talk.
But it's not presented--
it's not in sensory cortices.
So what we concluded
was a transitioning
cortical architecture
in this different brain
from sensory areas
to MST that allow,
somehow, a working memory
representation to arise.
Now, the next question
that we set to answer
is-- what are the feature
of cortical microcircuits
that allow persistent
activity to emerge?
And we came with two
very simple hypotheses.
The first hypothesis was
that persistent activity
arises from intrinsic
properties of neurons that
emerge in association areas.
For example, the ion
channels composition.
If you hit a neuron
with the stimulus,
and you stop the
stimulus, the neuron
is going to keep
firing beyond that.
It's equivalent to
a longtime constant.
So it's a very simple model.
And the second model was
a persistent activity
arises from network properties,
such as recurrent dynamics that
happens between neurons
in these different--
within a microcircuit.
This is not that crazy.
For a system
neurophysiology that
is used to see the
brain as a network
it looks a little bit extreme.
But if you look at-- of course,
the third hypothesis wanted
to--
if you look at several
papers, actually, they
have shown that persistent
activity could exist in slices.
Actually, here, in
this paper, what
they did in [INAUDIBLE]
slices, they applied carbocoal.
We're doing patch clamp studies.
So they patched the cells
and applied carbocoal.
They hit the cell
with a stimulus.
And the cell show
persistent activity.
In this paper that is
even more interesting,
what they did was they just--
recording from macaque
lateral prefrontal--
infero temporal neurons--
they inject a stimulus
into their neurons.
And they see persistent
activity in the proportion
of neurons, even
when you isolate
tight synaptic transmission
in those slices.
So that was very
interesting for me.
And also, they found that
happens in human and monkey
neocortex, but it doesn't
happen in the rat neocortex,
at least not if you
don't put carbocoal.
So the first experiments
that I'm going to show you--
we decided to go
and do patch clamp
in slices of lateral prefrontal
cortex in the monkey.
So we put a team together.
And, actually, we developed
a procedure in which we just
take slices of
prefrontal cortex,
and we send it to
the patch clampers.
The patch clampers inject
currents using the Allen
Institute protocol.
And then we observe if,
after injecting currents,
we see persistent
activity in those cells
in the lateral
prefrontal cortex.
Here it is, just a
summary of the procedure.
This is an interoperatory
image, where
you have the arcuate sulcus
and the principal sulcus.
Here, you have the
pieces of the brain.
We have to put that very
quickly into four degree
saline and transport it.
I think that someone is doing
that here with human tissue.
You know, probably, what
we are going through.
And here, you have some
pictures of the patch clamp.
And here, basically,
the experiment.
You block synaptic transmission.
You inject a current.
And then you see if there is
persistent activity in neurons
after you interrupt
the simulation.
The results were a
little bit surprising.
This is an example neuron.
So, basically, we're using
the one second square pulse
protocol of the Allen Institute.
And what you do is you inject
several pulses of depolarizing
stimulus and also several pulses
of hyperpolarizing stimulus
that doesn't appear here.
If you're familiar with
patch clamp recordings,
or intracellular recordings,
you know that you could do this.
So in this case, this cell is
a very typical, fast spiking
cell.
What you see here is
just the raw traces.
And here, for each one of
the stimulus intensity,
the spike rasters.
These cells, when you
stop the stimulus,
the cell stops spiking.
So there is no
persistent activity.
But we found other cells that
they look very interesting.
For example, what we call a
rebound persistent activity,
which are cells that they don't
have persistent activity when
you inject the polarizing
pulses here in green.
But when you inject
hyperpolarizing pulses,
the cell have
rebound spikes, here.
And here, you see
this big H current,
if you're familiar
with these traces, that
is very prominent in monkeys
as much as in humans.
And the recent paper
by the Allen showing
that these currents actually
are very prominent in humans.
And here, you have a
cell that you stimulate
with, actually,
excitatory pulses
and you see persistent
activity after the cessation
of this stimulus.
Here, for some of the
pulses, not for all of them.
There is a sweet spot that
you can at least [INAUDIBLE]
persistent activity.
Doing this, we found that,
actually, persistent activity
is in about one--
four of 164 neurons that
we patches in five animals.
So, basically, the interesting
thing here-- or the thing
to do here is try to
classify those neurons
and see if there is anything
special with the morphology
of the neurons.
We injecting them with biosetin.
And then we actually tried to
look at the reconstruction.
This reconstruction
is not quite ready.
We still have to
reconstruct the axon.
And we're trying to classify
those neurons that they
show persistent activity.
So the answer to
the two hypotheses
is, well, it is complex.
So in vitro, we find
neurons that open cessation
of the stimulation.
We have persistent activity.
And this is blocking
synaptic transmission.
So it is a complex question.
Now, we publish a
paper not long ago
with Diego in which we look
at different brain areas.
In some areas, you have
persistent activity.
In some areas, you don't show
much of the persistent activity
during working memory task.
And there is this
paper by Boston--
here in Boston by Jennifer
Luebke where she is looking at,
actually, characteristics
of pyramidal
neurons in the neocortex.
This is in the mouse-- compared
V1 and prefrontal cortex.
And this is in the
human-- compared
V1 and prefrontal cortex.
And what we--
I'm sorry, in the monkey.
And what we see is that
this pyramidal cells grows--
like, almost is a giant
relative to the V1 neuron
in the lateral
prefrontal cortex.
So the morphology of
the different neuron
types-- it seems to be,
that has evolved quite a bit
in primates.
And, actually, we don't know
the whole truth about this.
And we are trying
to do more research
to understand cell types and how
they relate to these functions
that we are looking
in completion.
Now, what other features
of cortical circuits
may actually make persistent
activity to arise?
We're looking into a model--
Chao-Ching Huang,
that is basically
based in neuron types.
So neuron types in the
cortex are very diverse.
And they're connected
into a certain way
that, luckily for us,
there is a lot of tools
out there that we're trying
to explore this question.
Chao-Ching's model, actually,
that it was taken from his work
with Patricia
Goldman-Rakic, too--
so proposes that you have
here, in gray, the pyramidal
neurons with the
optical dendrites.
And you have three
type of interneurons.
The parvalbumin interneuron,
the calretinin interneurons,
and the calbindin
interneurons-- these
are calcium-binding proteins
that, if you actually
tag the neurons with certain
antibodies for these proteins,
you can segregate the
neurons into almost three
different exclusive groups.
They're not
completely exclusive.
But more than 90%
of the cells you
can classify according to this.
So the whole idea here
is that the parvalbumin--
the pyramidal neuron excised
the PV interneuron and the PV
interneuron send
feedback inhibitory
signals to the same neuron.
And basically, to the
neurons here on this side,
more strongly to neurons
encoding different features
as these specifically
neurons that is activated.
And now, this synapse is into
the calretinin interneuron.
Now, the calretinin
interneuron almost exclusively
synapse into the
calbindin interneuron.
And the calbindin interneuron
controls the flow of excitation
through the optical dendrites.
This is what we call in the
calretinin interneuron--
so inhibition of inhibition
because it-- basically, it's
an interneuron phone that
facilitates pyramidal cell
excitation.
And you have, now, the PV that
inhibit the pyramidal cells.
So the whole idea
that he was proposing
was that you have an increase--
in association cortices,
you have an increase
in calretinin interneurons.
And, therefore, use
half the inhibition
of this neuron that
actually shuts down
the optical dendrites
of the pyramidal cells.
And you can have
a flow of inputs.
And that may actually
favor recording excitation.
It's a kind of a simple idea.
So that was the idea.
Now, what can you do as a
system neurophysiologist?
You can actually look at the
width of your action potential.
Luckily for us, actually,
the narrow spiking cells--
most of them are
parvalbumin interneurons.
The broad spiking
cells, I don't think
that you can tell much about
whether they are pyramidal,
calretinin, or calbindin,
rather than because they're
more pyramidal cells
than anything else,
the probability to
be a pyramidal cell
if your broad spiking is larger.
But I don't think that you
can tell much about it.
So what we did was, basically,
we sorted our spikes
in these three different areas.
We classified the
action potentials.
Here, you have a narrow spike
and a broad spiking cell,
very clearly.
And we look at the proportion
in the different areas.
And here what we found.
What we found is that--
here is the histogram
with the spike width.
So what you see here in blue
is the broad spiking cell
and in red the
narrow spiking cell.
And for an MT to lateral
prefrontal cortex,
you see that the proportion
of narrow to broad spiking
grows smaller.
So, basically, you have
less narrow spiking neurons
in the prefrontal.
Now, you could say, well, you
have less-- fewer PV neurons
in the prefrontal.
Well, that's not
fair because when
you put on extracellular
electrodes,
you have sampling bias.
You have a lot of things
happening in electrophysiology
that we know that it's
just-- it's a stretch.
So we decided that we're going
to do immunohistochemistry
and we embarked
into this journey
with some of my colleagues.
This is just to show
what I've shown before,
that there is an increasing
putative PV cells.
But we don't know anything
about these neurons.
So what we did was, again,
some of these animals
were going into
terminal experiments.
So, actually, we made slices
of the three different areas.
And we actually-- here, you
don't see that very well,
but there is a [INAUDIBLE]
stain here that defines layer 4.
And here it is actually,
the different areas--
MT, and MST, and lateral
prefrontal cortex.
MT and MST is good because you
can fit it in one single slice.
So that actually good.
And then we-- it
took us some time
to troubleshoot the antibodies.
What you see here,
the little dots,
are the different
neuronal types.
So we have antibodies
for the four main cell
groups neurogranin for
the excitatory cells here.
And, actually, maybe that
slide you can't see very well,
but, probably, the
next slide is going
to give a summary of that.
What we did was
basically count neurons--
a large number of neurons
here-- the sample size
in each one of the
different areas,
and a large number of samples
in each one of the animals.
And we came up with
the distribution
of the number of PV, calretinin,
neurogranin, and calbindin,
and actually, more importantly,
the ratio of PV to calretinin.
So this ratio actually
goes down as you
go from MT to MST to
lateral prefrontal cortex.
So, actually, we can say
that the number of calretinin
to parvalbumin-- or you can say
in the other way parvalbumin
to calretinin--
basically changes
from MT to LPFC.
Because calretinin
increases in number,
we deduct that there is more
inhibition of inhibition
in lateral prefrontal
cortex, according
to the number of cells.
And that may be a feature
of cortical microcircuits
that allows sustained
activity to arise.
So, again, that's
basically the same review
that we were talking about,
that in these association areas,
like here, for example, IT, here
you find the prefrontal cortex,
you have sustained activity.
But when you look in
primary sensory areas--
here, in visual cortex and here,
in somatosensory cortices--
actually, you don't see that
often, the sustained activity.
Now, what is the link between
persistent activity and working
memory?
In this case, we switched
to a series of experiments.
And the first
question that we asked
is persistent
activity in population
of neurons sufficient to
encode working memory?
So that's the criteria.
It may not be necessary,
but is it sufficient to
encode working memory?
And we implanted [INAUDIBLE]
microelectrode arrays
in the lateral prefrontal
cortex of two monkeys.
And then we trained the
monkey in a very simple task.
So this is the monkey brain,
as many of you recognize.
This is the lateral
prefrontal cortex.
This is the array.
And this is
interoperatory images
that we corroborate the
location of the array
so that we could do a
good mapping after all.
The task is pretty simple.
It's an oculomotor
delayed response task.
The animal fixates.
So a target appear at one of
these 16 different locations.
So the dots is to--
the dotted line is to
just signal the locations.
In reality, there is a blank
screen only with the target.
Then, there is a delay period.
And the animal, when the
fixation point goes off,
[INAUDIBLE] to the
remember location.
It's a typical
working memory task
that you actually-- you cease
the activity during the delay
period in one example neuron.
This is not surprising.
I mean, this is very well known.
And this is actually
the receptive fill
of these specific neuron for
the 16 different locations.
Here, these cells, for example,
light the right lower quadrant.
So has the memory filling
the right lower quadrant.
Now, what we did was to use
a machine learning procedure
in which we actually tell
the best cells that we
have with the best selectivity.
And actually, we start
pairing these cells
with every other
single cell that we
recorded simultaneously.
And we call it the best
of ensemble method.
And then, we start building
ensembles of two neurons,
for example.
And then we choose the best
ensemble of two neurons.
And then with that best
ensemble of two neurons,
we start pairing with any
other neuron in the sample.
So then we take the best
ensemble of three neurons.
I understand-- so let
me make clear here,
this is not really
the optimal ensemble.
Because exploring
this space is going
to take us four years with the
computer power that we have.
So it was-- so what we are doing
is pairing the best two with
the other--
and finding the best trio
that includes the best pair,
and the best quartet that
includes the best trio.
By doing that, we can reach
a performance of close to--
in this case, to 80% with
about 15 or 20 neurons--
the coding performance is
about with 15 or 20 neurons.
So our conclusion
here is that, yes,
the activity seems to
be sufficient to encode
working memory in these lateral
prefrontal cortex neurons.
Now--
AUDIENCE: But only when you
take the population, not
individual cells.
Is that correct?
DR. JULIO MARTINEZ-TRUJILLO:
Population.
Yeah.
Individual cells will make it--
AUDIENCE: Individual
cells are not good enough.
DR. JULIO MARTINEZ-TRUJILLO: No.
Not good enough.
So when you take the
population and you put
into the machine learning into--
well, we use [INAUDIBLE] with
logistic regression super
vector machine, linear,
nonlinear kernels.
Nonlinear kernels
was problematic
because we're
overfeeding the data.
So we decided to stick to
the linear kernels for now.
So that's what we are doing.
But, yes, it's just
the population.
So there is a lot in
this paper correlations
and a lot of things
that we look into it
we can talk about that
later, if you like to.
I wanted to go to
this exciting stuff
about the monkeys
playing video games.
So, basically, do lateral
prefrontal neurons encode
working memory more
realistic situation,
regardless of eye
movements and distractors?
So our experiments are usually
performed in a very reduced
environment.
That's what-- scientists, we
are reductionist by nature.
Of course, we have to
isolate the variables.
If not, we can't
control for them.
But, at the same
time, if you take
a system that has 150 variables
and you reduce it to five,
maybe the responses or the--
when you interrogate the
system, what you're getting
is not exactly the same thing
as when the 150 are present.
So we develop-- looking into
the video game development,
we develop this tool
box that basically
communicate on Real Engine
3 with our software.
And we develop, also, algorithm
to detect eye movements
and to measure eye
movement during
these virtual environments.
Unfortunately, you decay
actually on Real 3's death.
So you have to switch to Real 4.
So I could tell you
to download this,
but it's kind of
useless now because we
are trying to upgrade this.
And this is the monkey.
So this is the monkey
here, with the joystick.
And the monkey's
navigating to pursue,
actually, this blue color.
Someone is telling me,
so do the animals really
think that they're immersive,
and they are really
in a virtual environment?
So I'm going to show
you something now
that is mostly anecdotal, but
it may convince at least some
of you.
There is the monkey going.
And he's chasing.
He's doing just really well.
So now the monkey go for the
red stimulus, and go there.
And I believe that is
happening in the next one.
So, basically, what we
find is that the animal--
oh, no, it's not
happening in this movie.
Maybe I got the wrong movie.
So, basically, what
we find is that when
the animal makes a mistake--
oh, this is not happening.
Excuse me.
The monkey is cheating.
At some point, the animal--
if I keep playing the video,
probably, you would
see, at some point
the animal goes
into the left one.
And he backs up the
wall and actually try
to go to the other one.
That's what it is in the video.
This video probably have
to go for a long time.
But I do believe
that the animals are
navigating the environment.
They avoid obstacles.
They go around them.
So that's the evidence
that I have, so far.
Now, we created
this virtual arena
for doing a virtual
working memory task.
And in the virtual
arena, it's just
motivated by circular mazes.
So we have the animal in
there, in this podium.
And you have these nine
different locations.
The animal is basically
navigating with the joystick.
And the task is kind of simple.
So you shine this
stimulus somewhere.
Then, you have a
virtual gate that
doesn't let the monkey to go.
And then you let the monkey go.
Let's say, now this goes off.
He can go.
That's a delay period.
And now, he goes.
And then he hit the location
and he gets a reward.
The monkey does that again,
and again, and again.
And actually, he does
pretty well this task.
In this task, you can identify
three different periods--
the q, that is
about three seconds.
The delay, that is
about two seconds.
And the response, that
is about 10 seconds.
And during these
different periods,
you can ask two different
questions, in this case,
we ask.
First, do lateral prefrontal
neurons and populations
encode visual memory working
memory in virtual environments
in the presence of eye movements
and visual stimulation?
Remember, we did control for--
we measured the eye movement.
We didn't ask the
animal to fixate.
And the second is more
related with the causality
between the activity and
working memory, which is--
does changes in persistent
activity produced
by blockade of the NMDA
receptors, using ketamine,
actually produce working
memory deficits in this animal?
There is a whole literature
on ketamine, and actually
working memory and
ketamine and depression.
But what we know
about ketamine it's
an MNDA receptor antagonist.
So here, just to show you
the little clinical trial
that we run, here.
So basically, we have
the pre-injection trials,
in which we measure the
performance of the animal
in all these periods.
Then, we have the
injection of ketamine.
We had to train the animal
for doing this injection
so it doesn't get a lot
of-- very stressed out.
And the animal keeps working.
And you have the
early post-injection
and the late
post-injection period.
And you can do first at the
performance of the animal.
Well, first of all, we implanted
two microelectrode arrays
in the dorsal and ventral
lateral prefrontal cortex.
And, actually, we
use these designs
that it is published in
a paper by Blonde, et al.
2018, where you
use these caps that
improve a lot our incidence
of troubles with the animal.
But just to recap--
so there is a ventral
and a dorsal array
in the lateral prefrontal
cortex in the two animals.
Those are [INAUDIBLE] array with
96 active channels, each one.
So you could record
quite a bit of data.
Now, what about the performance?
This is the performance
of the animal.
So the number of
correct trials--
correct trial is where an
animal hit the location
in a certain time window.
This is the number of correct
trials with saline injections
here, in gray.
So we repeated the injection
because the injection could
be an uncontrolled variable.
So that might just
have been affecting
the performance of the animal.
And here with ketamine.
Actually, I just want
to go back to this.
I apologize for that.
And I just want
to say that these
were the doses of
ketamine that we use--
0.25 and 0.4
milligrams per kilo.
We titrated the animals.
We titrated the
performance of the animal.
If the animal start getting
nystagmus, we got to--
that dose is not going to work.
So we have to go to the
lower dose of ketamine.
So this is the
performance with ketamine.
The animal, actually-- the
number of correct trials
went down.
And then it recovers,
actually during the post--
the wash out period, in
about close to an hour.
And this is the reaction
times to reach the target.
They increase with ketamine.
And they recover
during the period
relative to the saline control.
So, in this case, we do have
a deficit in working memory--
performing a working
memory task in the animal.
Now, how does it look like?
This is the trajectories of
the animal from the podium,
all the way down to the target.
And they look pretty
regular there.
When you look at,
under ketamine,
all these black
trajectories-- the animal's
just like roaming around.
And it kind of forget
where the whole thing was.
And sometimes it hits
it, sometimes it doesn't.
And then, during
the recovery period,
it seems to normalize
a little bit.
So somewhere, the animal is just
wandering around in the maze
and he doesn't find
the location of that.
So the whole idea
that we proposed
was that ketamine,
basically, acts
at the level of
this synapse here,
mainly in parvalbumin
interneurons that
contain this type of receptor.
And where low doses of
ketamine may actually
impact mainly this synapse.
And what happened is that the
lateral inhibition of neurons
and the inhibition of the
same pyramidal neurons
doesn't happen.
And therefore, the
tuning curves actually
flatten out with ketamine.
So the tuning curve
of the population
is what gives you the tuning
curve for the memory signal,
of course.
So this is an example
neuron, in which we have,
here, in different
colors, the firing rate
of the neurons during
the delay period.
What you have here is a plane
that fits these firing rates
and actually gives you,
for example, in this case,
this neuron was more
selected for the target that
was proximal and to the left
when the animal comes here.
So and these are the different--
super smooth-- actually,
it's by density functions.
Now, when you give
ketamine, what you see
is that the response to a lot of
these non-preferred directions
start going up.
And this thing becomes yellow.
So basically, what you have
is just a flat surface.
And now, in this case,
after the ketamine,
you have the recovery
where the tuning
looks a lot like the tuning
during the pre-ketamine period.
So the conclusion here
is that during ketamine,
relative to saline, a
lot of the neurons--
what you see here
in this pie graph
is the pre-injection
selectivity.
Here, the percentage of
neurons known selective.
And here, the
percentage of neurons
selective for the different
periods of the task.
What you see is that the
percentage of selective neurons
for the different
period of the task
decreases a lot during the
early post-injection period
relative to the pre
and then recovers
during the post-injection.
That doesn't happen with saline.
With saline, you see
that these proportions
remain very similar.
So, basically, we can conclude
that ketamine actually
decreases the tuning
of LPFC neurons.
Now, you can put all
this data that we're
recording simultaneously
into a classifier--
a linear classifier.
And you're trying to classify
the remembered location
in the different task periods.
So what you see in green
and blue is the performance
of the classifiers during the
different period-- cue, delay,
and response--
actually during the pre and
the late post-injection.
And during the early
post-injection,
injection the classifier
actually drops in performance.
We're investigating
why these drops happen.
I suspect that it's a loss of
the signal, rather than noise
correlation, so spike
on correlations.
But we have to
investigate that more.
Now, interestingly, if
you do that with saline,
the classifier doesn't
drop in performance
during the early post
injection period.
So we have evidence that the
single neuron at the population
level that this is happening.
What about eye movements?
I know that many of you
are dying to ask me,
but you have to gain
some constraint.
So here is the thing--
their mind's looking everywhere.
So it doesn't matter if the
animal is navigating to there.
He's not going like--
navigating to that position.
He's just navigating there
and he's looking everywhere.
He's just taking his
time to look everywhere.
So I don't think that we can
predict from the eye movements,
actually, from the
eye fixations where
the animal is going to go.
This is just-- I want to
back up this statement.
We took, actually,
the eye positions,
and we put it into a
linear classifier--
the eye position-- to decode
the location of the cue,
and where the animal was
going during the delay period.
And the classifier is
almost like chance.
So, basically, the
way that you do this,
you take the eye
movement and you
try to classify where the
animal is going from these nine
different location from the
pattern of eye fixations.
So the classifier is
almost like chance.
If you look at the neural data,
the classifier is almost--
is not a chance.
We compensate for the
number of features
in these classifiers-- number of
neurons and number of features
that we enter in the classifier.
So, basically, I don't
think that the eye movement
is a very reliable estimate
of where the animal is
going if we were thinking that.
That's bad news because
we use a movement a lot
in many of our tasks,
including my lab.
So sorry to be the
bearer of the bad news.
That doesn't mean that--
well, anyway, not going
to talk in the module.
Probably, you're going
to ask questions.
So a summary-- LPFC neurons
encode working memory
in naturalistic conditions in
the presence of eye movement
and distracting information.
So ketamine reduces performance
in the working memory
tasks and the spatial tuning
is lost in individual neurons.
And the ketamine decreases the
accuracy of population decoding
of target locations.
I think that I
started about 4:15.
So I still have 15 minutes.
And I want to talk
to something that
may be interest to some people
in the audience of this coding
of long-term memory by
single neurons and ensembles
in the hippocampus.
[VIDEO PLAYBACK]
- During life as
a brain surgeon,
it has been necessary to operate
on a good many men and women--
good many hundreds--
and expose the brain,
under local anesthesia,
with the patient conscious.
In those operations, it is a
useful, practical, procedure
to stimulate the
cortex electrically.
These are not experiments.
In that process, we have
stumbled, quite accidentally,
on the fact that
there is, recorded
in the nerve cells
of the human brain,
a complete record of the
stream of consciousness,
all those things
in which a man was
aware in any moment of time.
[END PLAYBACK]
DR. JULIO
MARTINEZ-TRUJILLO: So I'm
going to stop here because,
probably, many of you
have seen this video.
That's Wilder Penfield.
So those were the times
of the [INAUDIBLE]..
He said, they were
not experiments.
They were just findings--
casual findings.
So, after that, Brenda Milner--
actually, this is a patient--
HM-- Henry Molaison-- that
has a bilateral resection
of the temporal lobe.
And many of you knows
the seminal studies
on Brenda Miller, which is
one of the pride of Canada.
So is Penfield.
So, yeah.
I have to stop every time to
say how incredible she is.
I just can't get over it.
So she's just an amazing
person and researcher.
So she published
these paper with--
that it becomes an icon of
people doing memory research,
just basically.
So the whole idea was that
it was lost in the ability
to form memories, especially
declarative memories,
or associative memories
in this patient.
And people associated
the hippocampus
with the memory
field, obviously.
But in the rodents, a little bit
of a different picture arises.
So John O'Keefe
received the Nobel Prize
for describing place cells in
the rodent hippocampus in 1971.
So that was what
started the research.
And basically, when the rodent
navigates through a maze,
there are place that responds on
a certain position in the maze.
So this is very critical.
There is now a
split in the field--
kind of a split, I
find, between people
that do spatial navigation and
people that do memory research.
And this kind of
touches a little bit--
the humans, and
non-human primates,
and the rodents literature--
and the rodent researchers,
so as many of you know.
So one of the questions
that we wanted
to answer, by going
into the hippocampus
and look at the nature
of the representations
in the hippocampus of the
memory representations,
was-- how does mnemonic coding
of space and visual feature
interact in primate
hippocampus neurons
during virtual navigation tasks?
Of course, visual navigation
became available at this time
because we have, now,
the virtual environments.
The monkey can use a joystick.
And it was also a development
of brain navigation techniques
that some of the
companies that were based
in Montreal at that time
wrote research-- work with us
and we could actually
develop a very good targeting
of the hippocampus, even
with subfield resolution
that we are doing at this point.
And, at this point, we aim
to target the hippocampus
in a virtual environment using
different tasks-- navigation
tasks and associated memory.
This is the X maze, which is
inspired by rodent research.
This is a mountain.
And this is a tree.
Don't ask me why they're there.
I'm still asking this question
to Rob, my grad student that
put them there.
Why do you want them there?
Oh no, I like them.
Apparently the monkey likes the
trees more than the mountain.
That's the only thing
that I can say about that.
But what I'm going to talk about
is not about allocentric cues
now.
So you can think
about allocentric cue,
but what I'm going to talk
about is about a little bit
of a different paradigm.
So here is the X maze.
In one task, we
train the monkey just
to hunt for an object that
is going to be rewarded.
And you position the object
at any different position
in the maze.
There is no trial
structure here.
The monkey navigates.
Hit the object.
Get the reward.
Keep going, finding, keep going.
So that's what the monkey does.
In the second task, what we call
it the associative memory task,
we anchor the targets here,
to the arms of the maze.
So the targets could
be these colored dots.
And the walls of the maze
would be either wood or steel.
And dependent on
the wood or steel,
the animal has to figure
out this color scale.
And the animal has to
navigate to the item that give
the animal the highest reward.
So if it is wood and you
show green and orange,
animal has to go to the orange.
But if is actually
steel, and you
show the animal
green and orange,
the animal has to
go to the green one.
So the animal has to reverse
the context association.
They do that very quickly.
This is an example
trial of one animal.
You see is not spiking.
You got a spike there.
So it amazes me how these
hippocampus cells actually
fire.
It's just a little
bit of a crazy thing.
So on the cell,
seems to be spiking--
there, as you see, this more
random spiking or, I'm sorry,
sparse spiking.
So this is the cell
where the animal is just
foraging the task.
And in the second
task, the animal
is actually doing the
associated memory task.
So what you see is
the eye movement.
And you actually can
recover the object
that the animal is looking at.
So here the walls are
steel and the animal
has to go to the green target
and bam, he gets a reward.
Now the animal goes back.
And this is amazing.
Every time-- I mean,
some cell is amazing.
But I'm going to
talk about that.
So here is the wall.
The animal goes back.
So that context is wood.
The animal has to
go to the orange
and not to the green one.
It's very clear,
the task, right?
It's just a context
association task.
And again is the cell
firing after the animal
receives the reward,
the animal goes away
and the cell keeps firing.
So how the cells firing
the two different tasks--
we could hold the cell into
two different sections.
For example, here
you have a cell
that doesn't fire in
the foraging task.
So here is the distribution
of the positions on the maze
and here the spikes rasters
for the different positions
in the maze.
In this case, the
cell doesn't fire.
But when you go into the
associative memory task
here, in the arms
of the maze where
the objects are
appearing, right, where
the different objects appear--
so the cell fires.
In this cell, they fire a
little bit here in this task.
But in the other task,
the cell fires again here,
in the arms of the
maze, and a little bit
here in the corridor.
And this cells seem to fire
in the corridor in this task.
And in here, it's just
firing everywhere.
So, basically, there is
quite a bit of change.
The same cell change
firing, when you have
the animal in the same context.
The animal is navigating
the same space,
but now you have changed
the task contingencies.
And now this cell changes.
That reminds a little bit
of how play fields actually
change in the
hippocampus literature
when you change the context.
So what you have here is the
distribution of the different
what we call place fields in
this animal in the foraging
tasks that is all
around the maze.
Here is the proportion
of place plays
fields for different
areas of the maze that we
have in different colors here.
So what you see is that is kind
of homogeneous in the foraging
task.
Now, in the associative
memory task, what you have is,
in the arms of the maze,
you have a sudden appearance
of what we call place fields
in these areas of the maze.
Are those really place fields?
I'm not sure.
So we sent this paper and some--
people from the
rodent groups actually
made us to do a lot of
spatial information content
and a lot of things.
At some point, I was accused of
being actually like a dirty--
doing kind of sloppy rodent
hippocampus research.
So I want to talk about,
that is traumatic.
So now we have
refined our analysis.
And we have done a
lot of the analysis
that they do in the
rodent, which is true.
They have developed a lot of
this analysis very thoroughly.
This is a field that
has evolved a lot.
But the results are
the same, whether you
deal with spatial
information content
or we do it with actually
looking at rates.
So what mediates the
changes in single neuron
in factual information
contents across that?
I just want to take you
to this part of the task
where, actually, the
objects appear right
in the arms of the maze.
So this is exactly where
you see these resurgents
or this appearance
of place fields
right in this part of the maze.
Now, we have three variables.
One is the context.
The other one is
the objects that we
have in every single trial.
So what you could do is
basically divide the maze
into three-- into different
periods that correspond
to different spatial areas.
So post-reward or
trial, pre-context,
context appearance,
here in the corridor,
object appearance in this
case, and object approach.
And the trial ends, and the
animal goes again and loops
through that.
So, now, these are the
responses of several neurons
to different of these during
the associated memory tasks,
specifically.
What you see is that
actually, for example,
in the object
approach or trial end,
this neuron is firing
a lot of spikes.
Here, in the x-axis,
is the firing rate
of the neurons in
different trials.
You see that there is a
lot of spikes fired here,
but not in the other
areas of the maze.
But these neurons, you
have the opposite thing.
That is happen,
actually, when the animal
is the post-reward trial.
You can interpret that
as the animal, here,
is perceiving the stimuli.
Here, the animal,
maybe, is rehearsing
what kind of object
pair association
was shown in the maze.
So we did a linear regression
in every single neuron.
And what we see is actually that
the Beta coefficients for these
specific areas of the
maze-- object approach,
and in this case, post-reward--
actually are increased.
Let me explain this,
maybe, a little bit better.
This is what we
call trial N. That's
when the animals start a trial.
They see the different--
the association and the targets
and then they get the reward.
And this is the
trial N plus one.
Where we are regressing here is
the context object association
of the previous trial.
So this is actually
retrospective coding.
So there is no object
shown in this post-reward.
The object disappears.
So our conclusion is
that maybe the animal
is rehearsing what he saw in
the previous trial, something
that you find in the
hippocampus literature.
So what we believe
that is happening here
is that the neuron encoded
behaviorally-relevant stiumli
of the associated
memory tasking the arm
and branches of the
maze, and they encode it
in two ways-- perceptual
and mneumonic.
I'm going to go, maybe, quickly
through the next slides.
So what we tried to do is to
put all what I have told you
about the spatial
decoding in single neurons
and featured decoding or
context object decoding
in single neurons, and now
we bring it into classifiers.
So, basically, we produce
pseudo populations
and we tried to classify one,
spatial position of the animal
in the animal in the maze,
two, what kind of trial episode
it was--
was wood, plus green and
red on the left side--
green on the right and
red on the left side.
This is just a
classified performance.
Maybe you should focus here
and there in the middle row.
This is a classifier performance
relative to chance performance
that we use.
We do it by
shuffling the trials.
And what you can do is
that you can classify
the position of the animal
in these different areas
of the maze using
the population firing
rate with a level that is
higher than predicted by chance.
And you can do
that in both tasks.
But this is an allocentric
coding, basically.
We use an allocentric
reference frame.
Now, if we convert this
allocentric reference
frame into a direction
dependent frame--
in this case, a trial that goes,
for example, left up, here,
to right down, and a trial
that goes from left--
right down to up is
considering the same thing
because you kind of
linearize the maze.
And you consider that's like
an egocentric frame in which
the animal is navigating
from the post-trial,
from the reward,
toward the next goal.
Your performance
increasing substantially.
So that's actually
in both cases.
These are the
confusion matrices.
That maybe we should
focus on this for now.
Now, the most
interesting thing here
is that the performance-- the
classifier doesn't generalize.
So when you train the classifier
during associative memory
and you try to test it during
navigation foraging tasks,
it doesn't generalize.
The neurons change
the firing rate.
Something happened that the
classifier doesn't generalize.
So our interpretation is
that the spatial code changes
from one task to another.
So this code is not stable.
It's task specific.
Or maybe it's stable for a
task, but not across tasks.
Now, again, what
we wanted to do--
and this is the last
piece of information
that I have-- was to
decode trial episodes
from the population activity.
I don't want to talk
about episodic memory
because episodic memory
is very hard to define,
but about trial episodes.
Can I decode that
it was this context,
and there were two different,
actually, green and orange,
in this case?
The answer is, actually, yes.
So we produce here
the predicted label
for the different
combinations that we
have of context and object.
Here, for example, wood.
Here, steel context.
Here, the different objects.
And here, we have
the real label.
This is a confusion matrix.
And we have a very nice diagonal
of the confusion matrix,
where we can actually decode
both chance, the trial episode
that we show to the animal.
And in this case, we decode
that, also, prospectively,
which is during the time period
that the animal is approaching
the target, we integrate fire
rate over all this period.
Or we can encode
it retrospectively,
when the actual stimuli went
off and the animal is going back
to do the next trial--
during that period.
Actually, this is prospective
and the other one is--
so which is one-- yeah,
this is the memory--
retrospective and perceptual.
And you increase, actually,
your decoding accuracy,
in this case, if
you join all these--
the two periods.
Here, we have to compensate
for the number of features
of the classifiers,
if you can imagine,
because here you have two time
periods time number of neurons.
So but the conclusion here is
that we could actually decode
the trial type from the neuron.
So to conclude, I believe that
single neurons and ensembles
in primate hippocampus is
called a spatial position.
This code is very
similar to the one
that they have seen in rodents.
However, this
spatial code is not
specific-- it's specific
to a particular task.
So the cells within
the same session
change the firing rate when
you change the contingencies.
The neuron ensembles
in primate hippocampus
encode information
about trial episodes
prospectively and
retrospectively.
And it could be--
this could provide building
blocks for episodic memories.
Now, I have to say
that a person that
guided us, really,
in our soul search
in this hippocampus research
was Howard Eichenbaum.
I was really heartbroken
when I heard the news.
I think all what I can say about
him that it was the most kind
person that I have known.
And he predicted this.
So the role of the hippocampal
in the navigation of memory--
he thought about the hippocampus
as this multidimensional device
that actually encodes
time in many dimensions.
He was really an incredible
mentor to my student
and to me, too, actually.
Sorry.
I should actually shut
down the volume, here.
So these are the people that
did actually the real job.
So we have a happy lab.
We do a lot of science.
And then we tried to party
hard also, sometimes.
I have to say that
here is Diego.
Diego you don't recognize
yourself here, probably.
That was in Montreal
with our old set up.
We have the old
[INAUDIBLE] at that time.
And here are the other guys.
This is a neurosurgeon that
has collaborated with us.
And here, a picture of
different guys in the lab.
Rob Gulli, they lead a lot
of the hippocampus research,
and Megan and Marion,
actually, they
paired together to do a
lot of the ketamine trials.
So without these guys,
we haven't been able--
we would have not
been able to do this.
So thank you very much to
everyone and to our funding
agencies.
[APPLAUSE]
