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Of course, it would be very interesting to investigate the
activity of the single neuron during the decision making process.
This quite difficult task because neurons are quite small objects.
So, the typical size of the neuron is between 10-50 micrometers.
So here, you see on this graph, it's a
very small object compared to the tip of the pencil.
So, how to investigate the activity of this very small neuron.
We can, of course, visualize the structure of
this neuron using, for example, the electronic microscopy.
But we also can put a small sensor, small electrode near this neuron,
or inside of this neuron, and record the activity of the single neuron.
So, as you remember, the neuron is a main computational element of our brain.
So, done right, the accumulating information from other neurons.
The cell body integrates this information
and produces action potential, and action potential
propagates the exon and sends the signal to other neurons, or to our muscles.
So, we can record the activity of one neuron using single cell recording method.
So we put a very small electrode inside of the neuron, or nearby, and
we can record the single action potentials produced by the neuron.
The more active the neuron is, the higher number, the higher
frequency of the action potentials is produced by the neuron.
So, overall, the neuron is a
major computational element of the nervous system.
So a neuron accumulates information.
Neuron is activated by other neurons all by the sensory organs.
And if this activation exceeds a
certain threshold, the action potential is produced.
And the action potential propagates the exon and activate by the
release of the neuromediators, the muscles, or the next neuron.
So, how can we study the neuron activity during the decision making process?
We can implant an electrode to the brain of
the animal and train the animal to perform certain tasks.
Or for example, to pay attention to the screen.
So I just give you a few examples
of the reactions of different neurons to certain stimulus.
So, the presentation of this stimulus is indicated by the black line in this graph.
So you see that different neurons react
very different, differently to the same object.
Each line, vertical line here, represents one spike, one action potential.
So, you clearly see that the first neuron produces a lot of
spikes, a lot of action potential, during the presentation of the stimulus.
The second neuron actually reacts to the offset of the stimulus, and the
third neuron reacts to the onset and to the offset of the neuron.
On the right side of this picture,
we represent the same data slightly differently.
So we make a histogram.
We calculate the firing rate, the number of spikes per second.
And we will use these graphs quite a lot during the next lecture.
So, on these graphs, we clearly see that neurons are activated by the stimulus.
And the first neuron is active during the presentation of the
stimulus, the second neuron is activated by the offset of the stimulus.
And the third neuron is activated most by the onset and offset of the stimulus.
So during the course, you will see this kind of pictures.
So here, monkey has to get food from the food box.
He sees a representation of the activity of
the neuron, and each dot here represents a spike.
So, each line represents a trial.
So what do we see here is that this
neuron is particularly active when monkey touches the reward.
So this neuron reacts to something that has a value.
We can visualize this reaction of the neuron using a histogram.
We can calculate the firing rates, the number of spikes per second.
So you clearly see that when monkey touches
something that has a value, these neurons are activated.
So using this method, we can record the activity of the
single neuron, or the activity of few neurons at the same time.
This is a very important method because
we can directly measure the neuronal activity here.
That's the only method that helps us to do it.
Unfortunately, this method is invasive, so we damage a
brain when we implant the electrode inside of the brain.
Normally, this method is limited by few neurons.
Unfortunately, we cannot measure the activity of
billions of neurons in the same time.
And to be honest, we do not clearly understand the informational code.
So, we don't know all details about the code.
How information is encoded in the neuronal activity.
But, this method brings us unique insights into the decision making process.
We can also use EEG and MEG to study
the activity of large portions of the human brain.
So, electroencephalography is sensitive to the electrical
activity produced by the firing of neurons.
And magnetoencepholography is sensitive to the
magnetic fields produced by active neurons.
So, the neurons in our cortex are highly organized so large groups of neuron can be
seen as dipoles, negatively charged on top and positively charged in the middle.
So this difference induces local electrical fields that can be recorded
even outside of the brain using EEG sensors, EEG electrodes.
So, EEG's method is sensitive to the electrical, local
currents induced by active neurons, by large groups of neurons.
So this local, electrical currents also induce magnetic fields.
And these magnetic fields can be detected by the MEG method.
Some of you perhaps are familiar with the EEG method.
Using EEG cap, we can record EEG signal.
And, it looks a bit noisy.
It's difficult, to be honest, to recognize something
here related, for example, to decision making process.
So we can use some tricks, and we
can use a technique called event-related potentials methods.
So, for example, we can present sound, or
a visual stimulus to the person many times.
So if we average EEG during the presentation
of the same stimulus, we can filter out noise.
And we actually get the signal related to the presentation of the stimulus.
So in this case, you see a volt
response triggered by the presentation of the auditory stimulus.
So, this is activity of the brain related to certain stimulus
and, for example, related to certain stage of decision making process.
So we can use event-related potentials
technique to investigate decision making process.
Imagine that two subjects play the matching penny game.
This game is played between two players, Player A and Player B.
Each player has a penny.
If penny match both heads, or both tails, Player A keep both pennies.
If the pennies do not match, Player B keeps both pennies.
We can record evoked responses to the outcome of this game.
In this case, you see evoked responses to the
losses and to the wins during the matching penny game.
So, pay attention to the loss situation, when the person loose money.
You see a peak around 250 milliseconds, and this peak
is particularly strong when person change decision in the next round.
So, I visualize this effect on the right side of this graph.
So, you see that the peak is particularly strong
when subject changes the decision in the next trial.
So, we can predict the decision based on
the both response, based on the event-related potential.
This method helps us to really investigate the dynamic
of the decision making process, with the millisecond precision.
We can also record our activity of the brain using MEG,
and MEG measures the magnetic fields produced by the active neurons.
Actually, produced by the electrical activity of those neurons.
And MEG uses extremely sensitive
devices called superconducting quantum interface devices.
It's a quite expensive method, but it also helps us to detect activity
of certain cortical neurons during the decision making process.
It is important to remember that MEG and EEG do not reflect the action potentials.
MEG and EEG reflect post-synaptic potentials.
So, with this method, we actually monitor the
activation of the neuronal populations by other neurons.
So overall, MEG and EEG methods are strongly recommended if
you would like to study different stages of decision making.
Both methods have excellent time resolution.
EEG method is quite cheap, but MEG device is relatively expensive.
Unfortunately, both methods do not have the optimal spatial resolutions,
but both methods are becoming more and more popular neuroeconomics.
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