Introduction to Brian part 1: Neurons¶
All Brian scripts start with the following. If you’re trying this notebook out in IPython, you should start by running this cell.
from brian2 import *
Later we’ll do some plotting in the notebook, so we activate inline plotting in the IPython notebook by doing this:
Brian has a system for using quantities with physical dimensions:
All of the basic SI units can be used (volt, amp, etc.) along with all
the standard prefixes (m=milli, p=pico, etc.), as well as a few special
mV for millivolt,
pF for picofarad, etc.
Also note that combinations of units with work as expected:
And if you try to do something wrong like adding amps and volts, what happens?
--------------------------------------------------------------------------- DimensionMismatchError Traceback (most recent call last) <ipython-input-8-a44fa670700d> in <module>() ----> 1 print 5*amp+10*volt /home/marcel/programming/brian2/brian2/units/fundamentalunits.pyc in __add__(self, other) 1301 return self._binary_operation(other, operator.add, 1302 fail_for_mismatch=True, -> 1303 message='Addition') 1304 1305 def __radd__(self, other): /home/marcel/programming/brian2/brian2/units/fundamentalunits.pyc in _binary_operation(self, other, operation, dim_operation, fail_for_mismatch, message, inplace) 1249 1250 if fail_for_mismatch: -> 1251 fail_for_dimension_mismatch(self, other, message) 1252 1253 if inplace: /home/marcel/programming/brian2/brian2/units/fundamentalunits.pyc in fail_for_dimension_mismatch(obj1, obj2, error_message) 147 if error_message is None: 148 error_message = 'Dimension mismatch' --> 149 raise DimensionMismatchError(error_message, dim1, dim2) 150 151 DimensionMismatchError: Addition, dimensions were (A) (m^2 kg s^-3 A^-1)
If you haven’t see an error message in Python before that can look a bit overwhelming, but it’s actually quite simple and it’s important to know how to read these because you’ll probably see them quite often.
You should start at the bottom and work up. The last line gives the
DimensionMismatchError along with a more specific message
(in this case, you were trying to add together two quantities with
different SI units, which is impossible).
Working upwards, each of the sections starts with a filename (e.g.
C:\Users\Dan\...) with possibly the name of a function, and then a
few lines surrounding the line where the error occurred (which is
identified with an arrow).
The last of these sections shows the place in the function where the error actually happened. The section above it shows the function that called that function, and so on until the first section will be the script that you actually run. This sequence of sections is called a traceback, and is helpful in debugging.
If you see a traceback, what you want to do is start at the bottom and scan up the sections until you find your own file because that’s most likely where the problem is. (Of course, your code might be correct and Brian may have a bug in which case, please let us know on the email support list.)
A simple model¶
Let’s start by defining a simple neuron model. In Brian, all models are defined by systems of differential equations. Here’s a simple example of what that looks like:
tau = 10*ms eqs = ''' dv/dt = (1-v)/tau : 1 '''
In Python, the notation
''' is used to begin and end a multi-line
string. So the equations are just a string with one line per equation.
The equations are formatted with standard mathematical notation, with
one addition. At the end of a line you write
: unit where
is the SI unit of that variable.
Now let’s use this definition to create a neuron.
G = NeuronGroup(1, eqs)
In Brian, you only create groups of neurons, using the class
NeuronGroup. The first two arguments when you create one of these
objects are the number of neurons (in this case, 1) and the defining
Let’s see what happens if we didn’t put the variable
tau in the
eqs = ''' dv/dt = 1-v : 1 ''' G = NeuronGroup(1, eqs)
--------------------------------------------------------------------------- DimensionMismatchError Traceback (most recent call last) <ipython-input-11-70d526e22e27> in <module>() 2 dv/dt = 1-v : 1 3 ''' ----> 4 G = NeuronGroup(1, eqs) /home/marcel/programming/brian2/brian2/groups/neurongroup.pyc in __init__(self, N, model, method, threshold, reset, refractory, namespace, dtype, dt, clock, order, name, codeobj_class) 403 # can spot unit errors in the equation already here. 404 try: --> 405 self.before_run(None) 406 except KeyError: 407 pass /home/marcel/programming/brian2/brian2/groups/neurongroup.pyc in before_run(self, run_namespace, level) 643 # Check units 644 self.equations.check_units(self, run_namespace=run_namespace, --> 645 level=level+1) 646 647 def _repr_html_(self): /home/marcel/programming/brian2/brian2/equations/equations.pyc in check_units(self, group, run_namespace, level) 861 '\n%s') % (eq.varname, 862 ex.desc), --> 863 *ex.dims) 864 elif eq.type == SUBEXPRESSION: 865 try: DimensionMismatchError: Inconsistent units in differential equation defining variable v: Expression 1-v does not have the expected units, dimensions were (1) (s^-1)
An error is raised, but why? The reason is that the differential
equation is now dimensionally inconsistent. The left hand side
has units of
1/second but the right hand side
dimensionless. People often find this behaviour of Brian confusing
because this sort of equation is very common in mathematics. However,
for quantities with physical dimensions it is incorrect because the
results would change depending on the unit you measured it in. For time,
if you measured it in seconds the same equation would behave differently
to how it would if you measured time in milliseconds. To avoid this, we
insist that you always specify dimensionally consistent equations.
Now let’s go back to the good equations and actually run the simulation.
start_scope() tau = 10*ms eqs = ''' dv/dt = (1-v)/tau : 1 ''' G = NeuronGroup(1, eqs) run(100*ms)
First off, ignore that
start_scope() at the top of the cell. You’ll
see that in each cell in this tutorial where we run a simulation. All it
does is make sure that any Brian objects created before the function is
called aren’t included in the next run of the simulation.
So, what has happened here? Well, the command
run(100*ms) runs the
simulation for 100 ms. We can see that this has worked by printing the
value of the variable
v before and after the simulation.
start_scope() G = NeuronGroup(1, eqs) print 'Before v =', G.v run(100*ms) print 'After v =', G.v
Before v = 0.0 After v = 0.99995460007
By default, all variables start with the value 0. Since the differential
dv/dt=(1-v)/tau we would expect after a while that
would tend towards the value 1, which is just what we see. Specifically,
v to have the value
1-exp(-t/tau). Let’s see if
print 'Expected value of v =', 1-exp(-100*ms/tau)
Expected value of v = 0.99995460007
Good news, the simulation gives the value we’d expect!
Now let’s take a look at a graph of how the variable
v evolves over
start_scope() G = NeuronGroup(1, eqs) M = StateMonitor(G, 'v', record=True) run(30*ms) plot(M.t/ms, M.v) xlabel('Time (ms)') ylabel('v')
<matplotlib.text.Text at 0x7ff097050790>
This time we only ran the simulation for 30 ms so that we can see the behaviour better. It looks like it’s behaving as expected, but let’s just check that analytically by plotting the expected behaviour on top.
start_scope() G = NeuronGroup(1, eqs) M = StateMonitor(G, 'v', record=0) run(30*ms) plot(M.t/ms, M.v, '-b', lw=2, label='Brian') plot(M.t/ms, 1-exp(-M.t/tau), '--r', lw=2, label='Analytic') xlabel('Time (ms)') ylabel('v') legend(loc='best')
<matplotlib.legend.Legend at 0x7ff095e9c510>
As you can see, the blue (Brian) and dashed red (analytic solution) lines coincide.
In this example, we used the object
StateMonitor object. This is
used to record the values of a neuron variable while the simulation
runs. The first two arguments are the group to record from, and the
variable you want to record from. We also specify
means that we record all values for neuron 0. We have to specify which
neurons we want to record because in large simulations with many neurons
it usually uses up too much RAM to record the values of all neurons.
Now try modifying the equations and parameters and see what happens in the cell below.
start_scope() tau = 10*ms eqs = ''' dv/dt = (sin(2*pi*100*Hz*t)-v)/tau : 1 ''' G = NeuronGroup(1, eqs, method='euler') # TODO: we shouldn't have to specify euler here M = StateMonitor(G, 'v', record=0) G.v = 5 # initial value run(60*ms) plot(M.t/ms, M.v) xlabel('Time (ms)') ylabel('v')
<matplotlib.text.Text at 0x7ff096190cd0>
So far we haven’t done anything neuronal, just played around with differential equations. Now let’s start adding spiking behaviour.
start_scope() tau = 10*ms eqs = ''' dv/dt = (1-v)/tau : 1 ''' G = NeuronGroup(1, eqs, threshold='v>0.8', reset='v = 0') M = StateMonitor(G, 'v', record=0) run(50*ms) plot(M.t/ms, M.v) xlabel('Time (ms)') ylabel('v')
<matplotlib.text.Text at 0x7ff09612bd90>
We’ve added two new keywords to the
reset='v = 0'. What this means is that
v>1 we fire a spike, and immediately reset
v = 0 after the
spike. We can put any expression and series of statements as these
As you can see, at the beginning the behaviour is the same as before
v crosses the threshold
v>0.8 at which point you see it
reset to 0. You can’t see it in this figure, but internally Brian has
registered this event as a spike. Let’s have a look at that.
start_scope() G = NeuronGroup(1, eqs, threshold='v>0.8', reset='v = 0') spikemon = SpikeMonitor(G) run(50*ms) print 'Spike times:', spikemon.t[:]
Spike times: [ 16. 32.1 48.2] ms
SpikeMonitor object takes the group whose spikes you want to
record as its argument and stores the spike times in the variable
Let’s plot those spikes on top of the other figure to see that it’s
getting it right.
start_scope() G = NeuronGroup(1, eqs, threshold='v>0.8', reset='v = 0') statemon = StateMonitor(G, 'v', record=0) spikemon = SpikeMonitor(G) run(50*ms) plot(statemon.t/ms, statemon.v) for t in spikemon.t: axvline(t/ms, ls='--', c='r', lw=3) xlabel('Time (ms)') ylabel('v')
<matplotlib.text.Text at 0x7ff095649a50>
Here we’ve used the
axvline command from
matplotlib to draw a
red, dashed vertical line at the time of each spike recorded by the
Now try changing the strings for
reset in the cell
above to see what happens.
A common feature of neuron models is refractoriness. This means that after the neuron fires a spike it becomes refractory for a certain duration and cannot fire another spike until this period is over. Here’s how we do that in Brian.
start_scope() tau = 10*ms eqs = ''' dv/dt = (1-v)/tau : 1 (unless refractory) ''' G = NeuronGroup(1, eqs, threshold='v>0.8', reset='v = 0', refractory=5*ms) statemon = StateMonitor(G, 'v', record=0) spikemon = SpikeMonitor(G) run(50*ms) plot(statemon.t/ms, statemon.v) for t in spikemon.t: axvline(t/ms, ls='--', c='r', lw=3) xlabel('Time (ms)') ylabel('v')
<matplotlib.text.Text at 0x7ff0956f6290>
As you can see in this figure, after the first spike,
v stays at 0
for around 5 ms before it resumes its normal behaviour. To do this,
we’ve done two things. Firstly, we’ve added the keyword
refractory=5*ms to the
NeuronGroup declaration. On its own, this
only means that the neuron cannot spike in this period (see below), but
doesn’t change how
v behaves. In order to make
v stay constant
during the refractory period, we have to add
(unless refractory) to
the end of the definition of
v in the differential equations. What
this means is that the differential equation determines the behaviour of
v unless it’s refractory in which case it is switched off.
Here’s what would happen if we didn’t include
Note that we’ve also decreased the value of
tau and increased the
length of the refractory period to make the behaviour clearer.
start_scope() tau = 5*ms eqs = ''' dv/dt = (1-v)/tau : 1 ''' G = NeuronGroup(1, eqs, threshold='v>0.8', reset='v = 0', refractory=15*ms) statemon = StateMonitor(G, 'v', record=0) spikemon = SpikeMonitor(G) run(50*ms) plot(statemon.t/ms, statemon.v) for t in spikemon.t: axvline(t/ms, ls='--', c='r', lw=3) axhline(0.8, ls=':', c='g', lw=3) xlabel('Time (ms)') ylabel('v') print "Spike times:", spikemon.t[:]
Spike times: [ 8. 23.1 38.2] ms
So what’s going on here? The behaviour for the first spike is the same:
v rises to 0.8 and then the neuron fires a spike at time 8 ms before
immediately resetting to 0. Since the refractory period is now 15 ms
this means that the neuron won’t be able to spike again until time 8 +
15 = 23 ms. Immediately after the first spike, the value of
instantly starts to rise because we didn’t specify
(unless refractory) in the definition of
dv/dt. However, once it
reaches the value 0.8 (the dashed green line) at time roughly 8 ms it
doesn’t fire a spike even though the threshold is
v>0.8. This is
because the neuron is still refractory until time 23 ms, at which point
it fires a spike.
Note that you can do more complicated and interesting things with refractoriness. See the full documentation for more details about how it works.
So far we’ve only been working with a single neuron. Let’s do something interesting with multiple neurons.
start_scope() N = 100 tau = 10*ms eqs = ''' dv/dt = (2-v)/tau : 1 ''' G = NeuronGroup(N, eqs, threshold='v>1', reset='v=0') G.v = 'rand()' spikemon = SpikeMonitor(G) run(50*ms) plot(spikemon.t/ms, spikemon.i, '.k') xlabel('Time (ms)') ylabel('Neuron index')
<matplotlib.text.Text at 0x7ff0937e8b50>
This shows a few changes. Firstly, we’ve got a new variable
determining the number of neurons. Secondly, we added the statement
G.v = 'rand()' before the run. What this does is initialise each
neuron with a different uniform random value between 0 and 1. We’ve done
this just so each neuron will do something a bit different. The other
big change is how we plot the data in the end.
As well as the variable
spikemon.t with the times of all the spikes,
we’ve also used the variable
spikemon.i which gives the
corresponding neuron index for each spike, and plotted a single black
dot with time on the x-axis and neuron index on the y-value. This is the
standard “raster plot” used in neuroscience.
To make these multiple neurons do something more interesting, let’s introduce per-neuron parameters that don’t have a differential equation attached to them.
start_scope() N = 100 tau = 10*ms v0_max = 3. duration = 1000*ms eqs = ''' dv/dt = (v0-v)/tau : 1 (unless refractory) v0 : 1 ''' G = NeuronGroup(N, eqs, threshold='v>1', reset='v=0', refractory=5*ms) M = SpikeMonitor(G) G.v0 = 'i*v0_max/(N-1)' run(duration) figure(figsize=(12,4)) subplot(121) plot(M.t/ms, M.i, '.k') xlabel('Time (ms)') ylabel('Neuron index') subplot(122) plot(G.v0, M.count/duration) xlabel('v0') ylabel('Firing rate (sp/s)')
<matplotlib.text.Text at 0x7ff092b21290>
v0 : 1 declares a new per-neuron parameter
1 (i.e. dimensionless).
G.v0 = 'i*v0_max/(N-1)' initialises the value of v0 for
each neuron varying from 0 up to
v0_max. The symbol
i when it
appears in strings like this refers to the neuron index.
So in this example, we’re driving the neuron towards the value
exponentially, but we fire spikes when
v>1 it fires a
spike and resets. The effect is that the rate at which it fires spikes
will be related to the value of
v0<1 it will never fire
a spike, and as
v0 gets larger it will fire spikes at a higher rate.
The right hand plot shows the firing rate as a function of the value of
v0. This is the I-f curve of this neuron model.
Note that in the plot we’ve used the
count variable of the
SpikeMonitor: this is an array of the number of spikes each neuron
in the group fired. Dividing this by the duration of the run gives the
Often when making models of neurons, we include a random element to
model the effect of various forms of neural noise. In Brian, we can do
this by using the symbol
xi in differential equations. Strictly
speaking, this symbol is a “stochastic differential” but you can sort of
thinking of it as just a Gaussian random variable with mean 0 and
standard deviation 1. We do have to take into account the way stochastic
differentials scale with time, which is why we multiply it by
tau**-0.5 in the equations below (see a textbook on stochastic
differential equations for more details).
start_scope() N = 100 tau = 10*ms v0_max = 3. duration = 1000*ms sigma = 0.2 eqs = ''' dv/dt = (v0-v)/tau+sigma*xi*tau**-0.5 : 1 (unless refractory) v0 : 1 ''' G = NeuronGroup(N, eqs, threshold='v>1', reset='v=0', refractory=5*ms) M = SpikeMonitor(G) G.v0 = 'i*v0_max/(N-1)' run(duration) figure(figsize=(12,4)) subplot(121) plot(M.t/ms, M.i, '.k') xlabel('Time (ms)') ylabel('Neuron index') subplot(122) plot(G.v0, M.count/duration) xlabel('v0') ylabel('Firing rate (sp/s)')
<matplotlib.text.Text at 0x7ff0929db710>
That’s the same figure as in the previous section but with some noise added. Note how the curve has changed shape: instead of a sharp jump from firing at rate 0 to firing at a positive rate, it now increases in a sigmoidal fashion. This is because no matter how small the driving force the randomness may cause it to fire a spike.
End of tutorial¶
That’s the end of this part of the tutorial. The cell below has another
example. See if you can work out what it is doing and why. Try adding a
StateMonitor to record the values of the variables for one of the
neurons to help you understand it.
You could also try out the things you’ve learned in this cell.
Once you’re done with that you can move on to the next tutorial on Synapses.
start_scope() N = 1000 tau = 10*ms vr = -70*mV vt0 = -50*mV delta_vt0 = 5*mV tau_t = 100*ms sigma = 0.5*(vt0-vr) v_drive = 2*(vt0-vr) duration = 100*ms eqs = ''' dv/dt = (v_drive+vr-v)/tau + sigma*xi*tau**-0.5 : volt dvt/dt = (vt0-vt)/tau_t : volt ''' reset = ''' v = vr vt += delta_vt0 ''' G = NeuronGroup(N, eqs, threshold='v>vt', reset=reset, refractory=5*ms) spikemon = SpikeMonitor(G) G.v = 'rand()*(vt0-vr)+vr' G.vt = vt0 run(duration) _ = hist(spikemon.t/ms, 100, histtype='stepfilled', facecolor='k', weights=ones(len(spikemon))/(N*defaultclock.dt)) xlabel('Time (ms)') ylabel('Instantaneous firing rate (sp/s)')
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