This is Paul for sure. I'm talking to Chao-Jing Wang, one of our speakers in the summer school.
And in your talk, you focus very much on the role of working memory,
sustained activity and cognition.
You started with a fairly strong
claim where you said that actually delay activity is the key to cognition.
So why do you think this is the key ingredient that we should worry about?
Right. I guess the general idea is that imagine that you cannot hold in your
mind anything in absence of direct sensory stimulation.
Then it seemed to me that most of the repertoire of behaviors is going to be
reduced to reflex all you can do is you know just,
reactively respond to stimuli right away otherwise you wait you forgot what
was the stimulus so you cannot really act accordingly and you become enslaved
to the external world whereas if you have this ability to hold something in
your mind even when the input is gone on,
then you are freed from the immediate stimulation and so that you can become
a lot more flexible, right?
You can, for example, wait and decide what you do about the stimulus and still
remembering what was the stimulus.
Right. Yeah. And then as an implementation of this, you were pointing very much to cortical circuits.
So what's so special then about cortical circuits that they can actually can
give you this kind of memory functions.
Right. So, in fact, persistent activity itself is probably more widespread than
working memory-related persistent activity. So, I want to distinguish those two things.
For example, even when you try to hold a gaze, your eyes are fixating on something.
That's maintained by persistent activity in the ocular motor system outside of the cortex.
But there's a very long history of studies pointing to the prefrontal cortex,
as a very important structure for working memory.
So that dates back to, I guess, 1920s and 30s, where people showed that if you
do lesion of the prefrontal cortex on an animal like a monkey,
we're not able to do delayed response tasks, which depend on working memory.
So from there on, I think, you know, there is a lot of studies showing that
prefrontal cortex is really important for, you know, working memory.
And that's why there's a lot of focus on cortex.
But then you were saying that it is the special characteristic of this dense
recurrence in these circuits that could sustain this memory function.
Is that correct? That's the idea, yeah. Okay, so then I could argue that actually
this kind of dense recurrence I would find throughout the cortex.
So why is then not my occipital cortex, that's usually more dedicated to vision,
actually a working memory system showing sustained activity?
What's then so special about these frontal areas?
That they can support working memory.
So I guess, empirically, we do not have enough data to answer your question explicitly.
But generally, it probably is a matter of degree, right?
So we know even in the primary sensory areas, the majority of synaptic inputs
are coming from within the local circuits.
But maybe there's just more in the brief on the cortex compared to sensory areas.
And what's interesting from the computational point of view is that you can
show that because of these feedback systems, your dynamics is nonlinear.
And in nonlinear systems, I think it's very important that just some graded
differences, some quantitative differences, for example, in the amount of recurrent connections.
Can lead to qualitatively different behaviors.
Like, you know, if your recurrent connections is below a threshold level,
you don't see persistent activity.
If it's above a threshold level, you start to see persistent activity.
So that may be my take about that. Just to add that it could also be there's
something else, such as neuromodulation, which may be somewhat different.
So, for example, dopamine modulation ventilation may be, say,
somewhat more prominent in the peripheral cortex than in early sensory systems,
for example, that could make a difference as well.
But I would say, again, it's a matter of quantitative differences leading to
qualitative differences.
Okay. But that's still a very much cortex-centered view.
So does it mean that you would be making the strong statement that okay working
memory can be fully realized by cortical circuits and does not depend on subcortical
activity beyond possibly some forms of neuromodulation so.
Yeah we don't know much about that i guess there's one thing certainly people think,
um for why subcortical structures might be important that is skating so you
don't want not any sensory stimulus coming into your brain to be stored in working memory, right?
So basically, you somehow have to gauge what really is behaviorally important
that you have to maintain internally, let's say, in the prefrontal cortex.
And that seems to depend on basal ganglia. There's this idea that,
you know, basal ganglia, for example, through the thalamus can gauge what signal
is important and what is not.
At least you know part of the gating function is dependent on basal ganglia okay yeah so,
It could also be that persistent activity itself may, in part,
depend on reverberation in a bigger loop involving the subcortical system.
But for that, people have speculated about that for a long time.
As far as I know, we do not yet have very strong evidence for that. Yeah.
So then the core ingredients of your model will be, let's say,
recurrently coupled excitatory circuits with, let's say, some inhibitory add-ons to get selectivity.
And this is, I think, some summary of a cortical circuit.
And this would then project downstream to, let's say, other subcortical structures
to trigger action, as in, for instance, your standard two-choice saccade-based tasks.
It would be like the choice aspects would happen in this cortical model,
and then the saccade would be triggered by, let's say, superior colliculus to
which this decision-making system would project.
So is that an assumption you really would like to insist on,
that these action elements are extra cortical, and that more the sensory-based
and the rule-based aspects are processed at the cortical level?
Well, that, I guess, depends. So in a way, saccade is special, right?
So if you talk about manual responses, then maybe parts of the motor cortex
are the command centers.
And so that would be kind of downstream system from decision circuits where
information are integrated, maybe a choice is produced.
So in that case, then even the, I think it's the part of the response generation
is occurring inside the cortex,
you know, always together with basal ganglia and some others like thalamus,
but in that case, you know, a lot of things may happen in the cortex as well. Okay.
So your, the approach, I mean, also given your background in physics,
you have used one of these tools that has been important to theoretical neuroscience
from physics of, let's say, attractor networks to analyze this model.
So why is the notion of an attractive network helping you to understand this
pre-protocol, these cortical dynamics?
You know sometimes i like to say especially to
experimentalists that the the uh the
word attractor networks seem to
have certain seem to provoke certain reactions in some people right so i would
sometimes start out uh by saying that uh attractor dynamics is not like a black
hole so it's not something that's very rigid that if you are in a tractor state
then it's like if you're You're sucked in.
You cannot get out. And it's a very rigid kind of thing.
Attracted states are simply relatively stable states.
That's all. It could even be something, you know, more than just a steady state.
For example, you could have chaotic attractors where you have a lot of temporal
dynamics going on inside that, you know, chaotic attracted state, right?
That's one thing. The other thing is that any inputs or neural modulation can
easily change, if you like, the landscape of attractive states.
So you can easily actually control,
manipulate, you know, the landscape of multiple attractive states.
So, you know, simply, as I was saying, you know, attractive states are simply
relatively stable states.
And if you want to describe mathematically what is a persistent activity, right?
Then it's natural to think about that as a relatively stable state that's all, right?
So as a result the concept of attractive states is very natural people still
debate about the actual dynamical structures,
of persistent activity that should be described well by attractive networks,
but that's I think a separate issue from a more general question whether,
attractor networks is the right framework or not to describe persistivity.
Because I could argue that in some sense any system that will show some persistence
of whatever spatial temporal scale you want to express this I can re-describe
in terms of an attractor network.
So the risk might then be that the framework of an attractor is so,
let's say, super powerful powerful, that it might not give you much leverage
with respect to a specific phenomenon.
So what's the leverage it has given you to really understand prefrontal cortex?
Sorry, I'm not sure I really understand the... Well, it's very simple.
In some sense, you could argue that if you look at the nervous system,
you will find at many different levels, you will find persistent states in some temporal window.
It can be a microsecond. It doesn't matter. Yeah.
In all those cases, I could step in with an attractor network formulation.
Ah, you see, it's an attractor because the system in some way is returning to
this state and then I define that state in some way.
So that means the attractor framework is super powerful.
You can describe anything with it you want, as long as there's some persistence
in the system, in some definition of its possible states.
I see. So what's special about, say, prefrontal cortex? Exactly.
Why does it give you leverage to understand prefrontal cortex?
I guess the important thing is that you have multiple attractor states at the same time.
If you think about if you want to design
a working memory system it basically is a system with multiple states so that
you can switch on and off between different states and that may not be so universal
in different systems for example you don't need and you don't want multiple.
States in not necessarily in early sensory systems for example And so even if
you have some persistency,
the question is whether you have this ability to go back and forth between many
different states with very brief input.
Does that make sense? So that seemed to be the distinction between what you
want for working memory system versus a sensory processing device.
Okay, so you're saying the framework of attractors gives you leverage in this case,
because actually you're looking at a system that can be in a large number of
possible states, and this is an efficient way to describe these. That's right.
And the persistency should be at the long time scale compared to the time constants
you have in the system, like milliseconds versus tens of milliseconds by physical time constant.
Let me also just briefly say that, as I was discussing in my lecture, that,
in fact, fast switches tend to be not the right conceptualization in the model I described.
So you also have very slow transients, too, and that's not just steady states.
And those slow transients, like a gradual ramping activity, turns out to be
a very good computational mean to, say, integrate information in decision-making.
So this is kind of an unusual type of attractive networks where you,
at the same time, have multiple stable states, But you also have this ability
to have very slow transients for time integration.
Okay, but do you see that as an intrinsic property of these circuits or as an
add-on that might be supported by a different substrate? Right.
So at least for the mechanism we found in the model, it's the same circuit.
So the same circuit, because the recurrent dynamics is mediated by,
you know, kind of slower cellular mechanism,
you can have both slow transients and multiple statist. Okay. Yeah.
Whether, you know, in the brain, those two things, actually,
there is evidence, I guess, at a single neuron level that in the brain,
those two things can happen at least in the same circuits, right?
Yes. And actually very commonly, you observe decision-related neural signals
in a decision task, and working memory-related signals in a working memory task,
in very different experiments done in different labs, but from the same code.
Adjusting, you have a shared mechanism. Okay.
Although you can never exclude that these might be interspersed circuits that
perform different functions, no? Right, in theory, sure.
But before we move into this issue of timing and decision-making,
another issue around attractor networks is that in order to be,
let's say, a certified and card-carrying member of the club of attractor networks,
you must satisfy certain minimal criteria.
Like when it's under perturbation, you must resort to the same attractor state, and so on.
So are you sure that these cortical, the frontal cortical network should look
at do satisfy all these conditions?
Well, I guess that's rather difficult to test, especially in behaving animals
or behaving primates, especially.
So I imagine that if people now are making efforts to develop maybe simpler
model systems like rodents,
if you can design a good, say, working memory or decision-making task,
it's probably more likely you
can really go down into microcircuit mechanisms and get
into more detailed information however you
can you know do certain experimental manipulations to test some model predictions
so for example as I showed that you know mentioned earlier that we found that
that reverberation should be slow.
In particular, it probably depends on a particular receptor,
NMDA receptors at the recurrent synopsis, right?
And we, in collaboration with Amy Anstin at Yale, actually we tested this idea
in behaving monkeys, you know, working memory task, using a technique called
ionophoresis to inject the drug locally.
Onto neurons you record from. So you see persistent activity in those neurons
in the prefrontal cortex of a behavior monkey.
And then when you apply the drug, the blocks and MDA receptors,
you actually see that process activity goes away.
That's, in my mind, a very direct confirmation that MDA receptors that are slow,
mediating slow repopulation, are critical for process activity.
Okay, yes, I see that. But then I could argue, well, but maybe you're blocking...
The NMDA receptors are the part of the thalamic projections.
Can you exclude that? No, you cannot.
That's right. But at least we can say it.
Are likely to be kind of intrinsic synapses
rather than you know synapses coming
from external stimulation because this is
during working memory during the delay when there's no direct
sensory stimulus right um so
to adjust your question you can do a different set of experiments but not with
monkey so with rodent you can do in vitro slices right and actually we have
done in collaboration with another group by Wenjing Gao in Philadelphia where
you do prefrontal cortical sizes directly.
You can look at, you know, NMDA or AMPA receptor immediately the transmission
between two cells neighboring neurons.
So there's really local connections between two neurons and show how much NMDA
you have at this very local synopsis.
In peripheral neurons, and you can compare that with the same kind of peer recordings
in the primary sensory system. You see a big difference.
Okay. That's also consistent. Right. So with these experiments...
So that would be really cortical, right? Local. Yes.
But with the antiparesis experiment, basically what you're saying is,
well, this makes it plausible possible that the memory state is implemented
by a recurrent or reverberating circuit.
But in some sense, what we still don't know is whether it really can be called an attractor or not.
So would you argue that, for instance, these micro-stimulation experiments that
have been performed in front of light fields that would sort of bias decision-making
of an animal, would you take that as corroborative evidence of having attractor states? Yes.
Or would that be the kind of way to get at that question? Well,
I think in order to go further along the question, I mean, what I suggest,
we should propose something very well defined that's an alternative to the attractive model, right?
And otherwise, it's hard to say, how do you prove or disprove the attractive network paradigm?
So I guess one alternative is slow transients.
Just because of some cellular or whatever, you know, biophysical process with
very slow time constant, right?
And so as a result, in response to transient stimulus, it just keeps going for
a long time on the timescale of many seconds, right?
So that's something we can try to, right, to contrast with a charted network.
And I'm sure slow processes are playing a role The question is whether it's
the workhorse, the main thing, right?
I do see a possible problem with a mechanism that's completely based on very slow process.
Because if you have a system with intrinsically very slow time constant.
As you know, you would have a hard time to change the system with briefing.
Because the time constant is so short and so long, You have to use very long
Z inputs to do anything with the system.
So switching on and off becomes a problem.
Right. Yeah. So maybe people can think about a clever way to really propose
very clearly defined alternatives, right? And then try to design. Right.
So what was very exciting about your proposal is that you also made the argument,
look, I can represent the memory state. I can represent the decision-making
by switching between my different detractors.
But on top of that, I can also capture the kind of ramping functions that have
to do with the reaction times as observed in prefrontal cortex, right?
Where you see that, okay, these neurons that seem to correlate with a decision
ramp more slowly when you have long reaction times and they ramp very fast when
you have a fast reaction time.
So how could that drop out of your model so easily?
What was the trick there? Why does that work?
Well, I guess the main thing is this idea of slow reverberation, I guess.
And that was a surprise to us.
You know, our priority, as I was saying, you know, working memory itself, in principle, right?
If you're an engineer, you think about how to design a working memory device,
you could say that can be done by faster switches.
But when you try to do that with neurons and the realistic synaptic connections,
you know, it turns out that the system is very unstable because of the strong feedback,
you know, machinery in the system.
And one way to solve that is to say positive feedback needs to be slow relative to negative feedback.
And that so was kind of forced on us, you know, on us.
So we say, you know, we have to have a working memory mechanism with
slow reverberation rather than very fast positive feedback
and from there it turns out um
you know uh it becomes easy so so this slow river version turns out to be exactly
what you need to get a slow ramping activity in decision that's i thought it's
quite nice yeah but now one one question i would have there is um.
In some sense in in this manipulation you could have
a confound that um what we are
what we are detecting what we are detecting is based
on moving dots essentially and
it's about the coherence of the moving dots that you
make your decisions so i could
argue look if i have these prefrontal neurons that
are sensitive to the moving dots then that
if i if they have an orientation tuning
then of course i'm driving them more effectively if
i have coherent movement in my scene than if i have if
i drive all these are incoherently so that basically means per unit
time they get less energy and therefore as a result
i'll be ramping up more slowly or faster you see and this then also exactly
correlates with the task condition so imagine i would change the task now that
the animal has to make a decision based on let's say um when when there's incoherent
movement it gets reward and it should ignore coherent movement so i sort of
i change the contingency,
would you predict the model would still work or would you have to add a new feature?
So what we added, we do need to add something in that case that is reward-dependent plasticity.
So in that case, you have to learn what are the potential outcomes from your choice options, right?
And that, we believe, is done through learning that depends on say, reward. Okay?
I think what you are pointing earlier at is the confine that maybe slow RAM
just corresponds to weaker inputs.
But in part it's true. So basically, you probably need to integrate more over
time if your input, your evidence is weaker.
But it's not just that, because, for example, we showed that if you have more
several options, more options to consider, then the reaction times are also slower.
And that in part is because you have this competition between your own pools
selected for say four or five options.
And that involves inhibition because it's a competition mediated by inhibition
that also slows down the ramping activity.
So you can also show nicely, you know, something that people see in psychology,
you know, the more options you have to consider, the slower the reaction time.
Okay. Yeah. Yes. so the other.
Aspect that I was curious about if you look at the model it's
also an issue we discussed earlier the interpretation of
this prefrontal cortical function also in the literature at
large it's in the end very much a labeled line kind
of system I have choice options the choice options depend on certain sensory
cues and if you want by magic they just come together in these units or in my
attractor network and now I can start to make decisions with them but you could
then of course pose the question, and sometimes it's a form of the symbol grounding problem,
okay where do these labeled lines come from and should I really assume that
these are labeled lines, that let's say your prefrontal cortex has all these
labeled lines projecting into it from other areas,
representing all possible cues you can ever encounter or you have ever encountered
in the world combined with all possible actions that you can ever trigger in
response to these cues, but that would be the labeled line view.
So do you think that that's a reasonable assumption, or do we have to get away from it in some way?
Right. So I guess you're totally right.
We have been focusing on certain elementary and fundamental,
machineries about working memory and decision-making without paying too much
attention to real-life stimuli, right?
And how do we really process real-life stimuli?
So I guess in my mind, to go forward in that direction, we have to understand
better how objects are recognized and represented in the brain.
And I could imagine, for example, in the inferior temporal cortex.
There we don't really understand yet how we recognize objects fully.
So it could be gilet l'ail, but it could be something more.
You know people talk about grandmother cells that's more
like data line I guess in your terminology
but or maybe something
more dynamic right we don't know yet what that is but to answer your question
I can envision that you know starting with the building blocks basically elementary
elementary machinery we kind of have some insights into
four working memory systems,
we can envision to connect that with something like a sensory,
visual system, including IT, for example, and see how the interaction between
a working memory system and the rest,
the posterior part of the visual system, together in a larger scale brain system
to carry out more realistic kind of representation and the working memory.
And I do think that's a major challenge in the field. How we have in Pakistan
mostly local circuits, right?
And we have to come up with a theoretical framework, new concepts perhaps perhaps
to understand large-scale brain system with interacting parts. Exactly.
So to go in that direction, if you look at the physiology on prefrontal cortex,
these classic experiments by Assad, Miller, and so on, in the end you look at
populations of neurons that actually have a very broad range of representations,
of cues, actions, and their combinations following different rules.
So, could you then imagine that maybe you have, let's say, more specialization
in these prefrontal circuits, that some neurons, let's say, contribute to cue information,
others to action information, and then in their interaction,
they will build up this, what you would call the attractor, representing this
key state in which you want to make your decisions.
Would that be a reasonable alternative? Yes.
Well, actually, so we have done more recent work with Stefano Fusi and Mattia, his graduate student.
That work suggests, this is a computational work, suggests that,
in fact, the neuron signals in the peripheral cortex probably should have a
lot of mixed selectivity.
So a given neuron would be, you know, activated to different degrees by a combination
of many different things, including, you know, sensory stimuli,
internal representation of behavior rules,
and maybe some control signals altogether.
Together so i my view is
that neurons are probably not dedicated to one thing only especially in the
driven accordance you know maybe in contrast to early sensory systems and there
it's very likely that you know there's a lot of multi a lot of mixed selectivity
and so that you know neuron Neuron groups,
if you like, coding things according to a combinatorial code,
that will be maybe a very good way to be able to combine information, right?
Another thing that's special about peripheral cortex is that it gets inputs
for many, many different areas, right?
And that also speaks to the fact that neural inputs also are kind of coming
from a lot of convergent-divergent pathways. Right.
So then on top of that, where do you see these models go?
What's the next challenge for you in this model? Okay.
So I guess briefly I can see several directions.
Number one is to see if, you know, the kind of models we build can be kind of,
and those are building blocks of cognition.
I like to call this, right, local circuit mechanisms and building blocks for cognition.
So the question is, can you extend this kind of approach, at least,
to more complex behaviors, more complex functions,
such as task switching or rule-based behavior, right?
So that's one. Number two, I just mentioned earlier, you know,
can we develop theory and computational mechanisms and the principles for large-scale
brain systems rather than just local circuits?
And third is real-life situation.
That definitely will challenge our kind of models in a very big way.
So we want to see if the insights from those kind of models are really useful
for our behavior in the face of a natural kind of environment.
Environment yeah so then what what
i also realized is that recently you have been become more interested
in let's say brain rhythms and brain oscillations and
you wrote a rather impressive review article on on these oscillations reason
why why how is this related to the attraction network so this is really a new
chapter so this is a very interesting topic i think more and more people are
interested in this really,
very active field,
you know.
People from very different disciplines, you know, like systems and neuroscientists,
as well as actually many people in the clinical field, people feel like this
synchrony, neuron synchrony, might be a way to look at, for example,
interactions between brain systems, right?
So, you know, related to large-scale dynamics type of issues.
We have worked a lot on, you know, or the mechanisms of synchrony and possible functions.
You know, this emerges naturally in recurrent networks, basically. Okay.
But it's still rather controversial, especially in terms of functional implications.
In part, I think it's because you do see often evidence of synchrony and oscillations
with measurements like EG or local field potential.
On the other hand, single neurons
are very stochastic. Even single neuron populations are very variable.
Okay. And so those two things don't seem to, you know, fit together.
Okay. That's, I think, part of the reason people feel like, you know,
it's hard to understand how do you explain those two things in the same framework.
And also, if the neuron operation is very stochastic,
then how much, right, in terms of degree, synchrony is really there on top of the stochasticity?
And if it's not big, right, if you have some measurement about synchrony,
and if it's still a few percent of the signal you see, why it should be the main focus?
Okay, I'm raising these questions without knowing the exact answers to those questions.
So, you know, in this review, for example, I try to discuss how you can reconcile these views.
Okay, and the general take, I guess we don't definitely know is the final answer.
But the general take is that maybe it's better to think about the role of synchrony
in a framework, in terms of neuron correlations, which we know are very important, right?
Neuron correlations in time, for example, are important for plasticity,
spike time-independent plasticity, right?
It may be even important to generate a stochasticity.
You know, it sounds like a bit of paradox, but we know that because a given
neuron receives a lot of inputs, you can average out noise.
And if, on the other hand, neurons are correlated weakly, they cannot average out noise.
And so maybe correlation itself is important to generate a stochasticity.
And that in turn, of course, can be functionally very important.
So to understand the different aspects of dynamics in the recurrent network
is a big challenge. So I guess that's a big reason that we are interested in signaling.
Very good. So then to finish up, two questions.
So coming from physics, going to neuroscience, attracting networks,
and actually now moving towards brain oscillations, large-scale understanding of the brain.
If you would have to stipulate a law that we should all follow in studying the
brain, so what would be the Shao-Yin-Guang law?
That's a tough one.
Well, let me phrase it this way.
I do think that trying to understand neural circuits of cognition is really
a very exciting, challenging thing.
And that would help to unify cognitive sciences and quote-unquote hardcore neurobiology,
which right now is still kind of separated.
And from what we learned, at least, is the key is slow reverberation balanced by inhibition.
And so again, quantitative differences can give you surprisingly new qualitatively
different functions and computations.
And that'll be, I mean, it's probably known from the theory of dynamical systems field,
but it should be a very big part of our efforts to understand cognitive circuits.
Okay, so judging one law is slow reverberation does the trick.
That's right. Very good. What time do you mean?
Okay. And then my last question is, if so, I'm going to go visit you five years from now at Yale.
I'm going to ask you then five years from now, like, look, five years back,
you gave me this one prediction that you would have believed in, so how did it pan out?
Was it false or true? what is one prediction you'll be willing to stick your neck out for today?
Well, I guess still slow recuperation. There are many other smaller predictions
that came out of the model.
You know, just one more example.
This is a very specific example, but I think it's quite impressive.
If it turns out to be correct.
That is, you find some scale invariance of reaction times.
That's at the behavioral level. It's a psychological law, really.
Very quantitative, very beautiful law in psychology that you can explain with
a neuron-circuit model mechanism in terms of stochastic neuron dynamics, right?
And, you know, that can be proved using neurophysiology.
I thought that would be really quite important, really, to relate what you see
in neurons in the recurrent circuit and what you see at the behavioral level.
Yeah, but that prediction has already come out, so I want a new one I can come
and hassle you with five years from now.
That has not been done yet. Of course. The thing is, I want you to now take
this risk that you might be wrong.
So.
I guess, you know, slow reverberation. But I guess how to prove that's wrong
is... It's a safe prediction.
Right. You know, we also want to have a big one, right? Or just not just any...
Whatever you feel comfortable with.
How about the following? Why don't you put some boundaries on slow?
So in what range of frequencies are we talking about for slow reverberation?
Yeah, so that, I mean, actually opened up a whole can of worms or opened a big
door because, you know, when you make decisions, you can integrate information over many timescales.
So, right, so in the brain, supposedly there are machineries that allow you
to integrate information, right?
So you can talk about integration over many seconds or even minutes.
So we don't know on that timescale what really is going on in the brain.
Let's see, what prediction that can be proven to be raw?
Let's make that very specific. I guess
I bet NMDA receptors and recurrent synapses inside cognitive-type neural cortical
circuits are the key for slow integration on a timescale of a second or so. Perfect.
Chai-Hsing Wang, thank you very much for this conversation.
Thank you. It's a pleasure.