Note
Click here to download the full example code
Fit injected current
For our first example, we will look at a very simple dataset: patch-clamp recordings from a single neuron in layer 4 of mouse primary visual cortex. This data is from the Allen Brain Atlas, and experimenters injected current directly into the cell, while recording the neuron's membrane potential and spiking behavior. The experiments varied the shape of the current across many sweeps, mapping the neuron's behavior in response to a wide range of potential inputs.
For our purposes, we will examine only one of these sweeps, "Noise 1", in which the experimentalists injected three pulses of current. The current is a square pulse multiplied by a sinusoid of a fixed frequency, with some random noise riding on top.
In the figure above (from the Allen Brain Atlas website), we see the approximately 22 second sweep, with the input current plotted in the first row, the intracellular voltage in the second, and the recorded spikes in the third. (The grey lines and dots in the second and third rows comes from other sweeps with the same stimulus, which we'll ignore in this exercise.) When fitting the Generalized Linear Model, we are attempting to model the spiking behavior, and we generally do not have access to the intracellular voltage, so for the rest of this notebook, we'll use only the input current and the recorded spikes displayed in the first and third rows.
First, let us see how to load in the data and reproduce the above figure, which we'll do using pynapple, which we'll use throughout this workshop, as it simplifies handling this type of data. After we've explored the data some, we'll introduce the Generalized Linear Model and how to fit it with nemos.
Learning objectives
- Learn how to explore spiking data and do basic analyses using pynapple
- Learn how to structure data for nemos
- Learn how to fit a basic Generalized Linear Model using nemos
- Learn how to retrieve the parameters and predictions from a fit GLM for intrepetation.
# Import everything
import jax
import os
import matplotlib.pyplot as plt
import nemos as nmo
import nemos.glm
import numpy as np
import pynapple as nap
import workshop_utils
# configure plots some
plt.style.use(workshop_utils.STYLE_FILE)
# Set the default precision to float64, which is generally a good idea for
# optimization purposes.
jax.config.update("jax_enable_x64", True)
Data Streaming
While you can download the data directly from the Allen Brain Atlas and interact with it using their AllenSDK, we prefer the burgeoning Neurodata Without Borders (NWB) standard. We have converted this single dataset to NWB and uploaded it to the Open Science Framework. This allows us to easily load the data using pynapple, and it will immediately be in a format that pynapple understands!
Tip
Pynapple can stream any NWB-formatted dataset! See their documentation for more details, and see the DANDI Archive for a repository of compliant datasets.
The first time the following cell is run, it will take a little bit of time to download the data, and a progress bar will show the download's progress. On subsequent runs, the cell gets skipped: we do not need to redownload the data.
path = workshop_utils.data.download_data("allen_478498617.nwb", "https://osf.io/vf2nj/download",
'../data')
Pynapple
Data structures and preparation
Now that we've downloaded the data, let's open it with pynapple and examine its contents.
data = nap.load_file(path)
print(data)
Out:
allen_478498617
┍━━━━━━━━━━┯━━━━━━━━━━━━━┑
│ Keys │ Type │
┝━━━━━━━━━━┿━━━━━━━━━━━━━┥
│ units │ TsGroup │
│ epochs │ IntervalSet │
│ stimulus │ Tsd │
│ response │ Tsd │
┕━━━━━━━━━━┷━━━━━━━━━━━━━┙
The dataset contains several different pynapple objects, which we discussed earlier today. Let's see how these relate to the data we visualized above:
units: dictionary of neurons, holding each neuron's spike timestamps.epochs: start and end times of different intervals, defining the experimental structure, specifying when each stimulation protocol began and ended.stimulus: injected current, in Amperes, sampled at 20k Hz.response: the neuron's intracellular voltage, sampled at 20k Hz. We will not use this info in this example
Now let's go through the relevant variables in some more detail:
trial_interval_set = data["epochs"]
# convert current from Ampere to pico-amperes, to match the above visualization
# and move the values to a more reasonable range.
current = data["stimulus"] * 1e12
spikes = data["units"]
First, let's examine trial_interval_set:
trial_interval_set.keys()
Out:
dict_keys(['Long Square', 'Noise 1', 'Noise 2', 'Ramp', 'Short Square', 'Short Square - Triple', 'Square - 2s Suprathreshold', 'Test'])
trial_interval_set is a dictionary with strings for keys and
IntervalSets
for values. Each key defines the stimulus protocol, with the value defining
the begining and end of that stimulation protocol.
noise_interval = trial_interval_set["Noise 1"]
noise_interval
As described above, we will be examining "Noise 1". We can see it contains three rows, each defining a separate sweep. We'll just grab the first sweep (shown in blue in the pictures above) and ignore the other two (shown in gray).
To select only one epoch from an IntervalSet, use 2 square brackets:
noise_interval = noise_interval.loc[[0]]
noise_interval
Out:
/home/docs/checkouts/readthedocs.org/user_builds/nemos-workshop-feb-2024/envs/latest/lib/python3.10/site-packages/pandas/core/frame.py:706: DeprecationWarning: Passing a BlockManager to DataFrame is deprecated and will raise in a future version. Use public APIs instead.
warnings.warn(
Now let's examine current:
current
Out:
Time (s)
------------- --
0.0 0
5e-05 0
0.0001 0
0.00015 0
0.0002 0
...
897.420749999 0
897.420799999 0
897.420849999 0
897.420899999 0
897.420949999 0
dtype: float64, shape: (11348420,)
current is a Tsd
(TimeSeriesData)
object with 2 columns. Like all Tsd objects, the first column contains the
time index and the second column contains the data; in this case, the current
in pA.
Currently current contains the entire ~900 second experiment but, as
discussed above, we only want one of the "Noise 1" sweeps. Fortunately,
pynapple makes it easy to grab out the relevant time points by making use
of the noise_interval we defined above:
current = current.restrict(noise_interval)
current
Out:
Time (s)
------------- --
460.768 0
460.76805 0
460.7681 0
460.76815 0
460.7682 0
...
488.787749993 0
488.787799993 0
488.787849993 0
488.787899993 0
488.787949993 0
dtype: float64, shape: (560400,)
Notice that the timestamps have changed and our shape is much smaller.
Finally, let's examine the spike times. spikes is a
TsGroup,
a dictionary-like object that holds multiple Ts (timeseries) objects with
potentially different time indices:
spikes
Out:
Index rate location group
------- ------ ---------- -------
0 0.88 v1 0
Typically, this is used to hold onto the spike times for a population of neurons. In this experiment, we only have recordings from a single neuron, so there's only one row.
We can index into the TsGroup to see the timestamps for this neuron's
spikes:
spikes[0]
Out:
Time (s)
1.85082
2.06869
2.20292
2.325815
2.42342
...
878.09765
878.110865
886.75375
886.761465
886.76995
shape: 777
Similar to current, this object originally contains data from the entire
experiment. To get only the data we need, we again use
restrict(noise_interval):
spikes = spikes.restrict(noise_interval)
print(spikes)
spikes[0]
Out:
Index rate location group
------- ------ ---------- -------
0 1.43 v1 0
Time (s)
470.81754
470.85842
470.907235
470.954925
471.0074
471.107175
471.25083
471.82728
471.917655
472.00696
472.71008
473.009605
478.807455
478.825935
478.85967
478.896305
478.92566
478.955415
478.993125
479.017315
479.06825
479.154195
479.196605
479.24137
479.59276
479.669225
479.70719
479.815245
479.89998
479.94611
479.99794
480.196545
480.53543
480.67927
480.81817
480.90529
480.94921
481.002715
481.60008
481.67727
shape: 40
Now, let's visualize the data from this trial, replicating rows 1 and 3 from the Allen Brain Atlas figure at the beginning of this notebook:
fig, ax = plt.subplots(1, 1, figsize=(8, 2))
ax.plot(current, "grey")
ax.plot(spikes.to_tsd([-5]), "|", color="k", ms = 10)
ax.set_ylabel("Current (pA)")
ax.set_xlabel("Time (s)")
Out:
Text(0.5, -6.277777777777782, 'Time (s)')
Basic analyses
Before using the Generalized Linear Model, or any model, it's worth taking some time to examine our data and think about what features are interesting and worth capturing. As we discussed in tutorial 0, the GLM is a model of the neuronal firing rate. However, in our experiments, we do not observe the firing rate, only the spikes! Moreover, neural responses are typically noisy—even in this highly controlled experiment where the same current was injected over multiple trials, the spike times were slightly different from trial-to-trial. No model can perfectly predict spike times on an individual trial, so how do we tell if our model is doing a good job?
Our objective function is the log-likelihood of the observed spikes given the predicted firing rate. That is, we're trying to find the firing rate, as a function of time, for which the observed spikes are likely. Intuitively, this makes sense: the firing rate should be high where there are many spikes, and vice versa. However, it can be difficult to figure out if your model is doing a good job by squinting at the observed spikes and the predicted firing rates plotted together.
One common way to visualize a rough estimate of firing rate is to smooth the spikes by convolving them with a Gaussian filter. See section 1.2 of Theoretical Neuroscience by Dayan and Abbott for a more thorough description.
Info
This is a heuristic for getting the firing rate, and shouldn't be taken as the literal truth (to see why, pass a firing rate through a Poisson process to generate spikes and then smooth the output to approximate the generating firing rate). A model should not be expected to match this approximate firing rate exactly, but visualizing the two firing rates together can help you reason about which phenomena in your data the model is able to adequately capture, and which it is missing.
For more information, see section 1.2 of Theoretical Neuroscience, by Dayan and Abbott.
Pynapple can easily compute this approximate firing rate, and plotting this information will help us pull out some phenomena that we think are interesting and would like a model to capture.
First, we must convert from our spike times to binned spikes:
# bin size in seconds
bin_size = 0.001
count = spikes.count(bin_size)
count
Out:
Time (s) 0
------------- ---
460.7685 0
460.7695 0
460.7705 0
460.7715 0
460.7725 0
...
488.783499999 0
488.784499999 0
488.785499999 0
488.786499999 0
488.787499999 0
dtype: int64, shape: (28020, 1)
Now, let's convert the binned spikes into the firing rate, by smoothing them with a gaussian kernel. Pynapple again provides a convenience function for this:
# the inputs to this function are the standard deviation of the gaussian and
# the full width of the window, given in bins. So std=50 corresponds to a
# standard deviation of 50*.001=.05 seconds
firing_rate = count.smooth(std=50, size=1000)
# convert from spikes per bin to spikes per second (Hz)
firing_rate = firing_rate / bin_size
Note that this changes the object's type to a
TsdFrame!
print(type(firing_rate))
Out:
<class 'pynapple.core.time_series.TsdFrame'>
Now that we've done all this preparation, let's make a plot to more easily visualize the data.
# we're hiding the details of the plotting function for the purposes of this
# tutorial, but you can find it in the associated github repo if you're
# interested:
# https://github.com/flatironinstitute/nemos-workshop-feb-2024/blob/binder/src/workshop_utils/plotting.py
workshop_utils.plotting.current_injection_plot(current, spikes, firing_rate)
Out:
/home/docs/checkouts/readthedocs.org/user_builds/nemos-workshop-feb-2024/envs/latest/lib/python3.10/site-packages/pandas/core/frame.py:706: DeprecationWarning: Passing a BlockManager to DataFrame is deprecated and will raise in a future version. Use public APIs instead.
warnings.warn(
/home/docs/checkouts/readthedocs.org/user_builds/nemos-workshop-feb-2024/envs/latest/lib/python3.10/site-packages/pandas/core/frame.py:706: DeprecationWarning: Passing a BlockManager to DataFrame is deprecated and will raise in a future version. Use public APIs instead.
warnings.warn(
/home/docs/checkouts/readthedocs.org/user_builds/nemos-workshop-feb-2024/envs/latest/lib/python3.10/site-packages/pandas/core/frame.py:706: DeprecationWarning: Passing a BlockManager to DataFrame is deprecated and will raise in a future version. Use public APIs instead.
warnings.warn(
So now that we can view the details of our experiment a little more clearly, what do we see?
-
We have three intervals of increasing current, and the firing rate increases as the current does.
-
While the neuron is receiving the input, it does not fire continuously or at a steady rate; there appears to be some periodicity in the response. The neuron fires for a while, stops, and then starts again. There's periodicity in the input as well, so this pattern in the response might be reflecting that.
-
There's some decay in firing rate as the input remains on: there are three four "bumps" of neuronal firing in the second and third intervals and they decrease in amplitude, with first being the largest.
These give us some good phenomena to try and predict! But there's something that's not quite obvious from the above plot: what is the relationship between the input and the firing rate? As described in the first bullet point above, it looks to be monotonically increasing: as the current increases, so does the firing rate. But is that exactly true? What form is that relationship?
Pynapple can compute a tuning curve to help us answer this question, by binning our spikes based on the instantaneous input current and computing the firing rate within those bins:
tuning_curve = nap.compute_1d_tuning_curves(spikes, current, nb_bins=15)
tuning_curve
tuning_curve is a pandas DataFrame where each column is a neuron (one
neuron in this case) and each row is a bin over the feature (here, the input
current). We can easily plot the tuning curve of the neuron:
workshop_utils.plotting.tuning_curve_plot(tuning_curve)
Out:
<Figure size 640x480 with 1 Axes>
We can see that, while the firing rate mostly increases with the current, it's definitely not a linear relationship, and it might start decreasing as the current gets too large.
So this gives us three interesting phenomena we'd like our model to help explain: the tuning curve between the firing rate and the current, the firing rate's periodicity, and the gradual reduction in firing rate while the current remains on.
Nemos
Preparing data
Now that we understand our model, we're almost ready to put it together. Before we construct it, however, we need to get the data into the right format.
Nemos requires that the predictors and spike counts it operates on have the following properties:
-
predictors and spike counts must have the same number of time points.
-
predictors must be two-dimensional, with shape
(n_time_bins, n_features). In this example, we have a single feature (the injected current). -
spike counts must be one-dimensional, with shape
(n_time_bins,). As discussed above,n_time_binsmust be the same for both the predictors and spike counts. -
predictors and spike counts must be
jax.numpyarrays. As we'll see, we can easily convert betweenjax.numpyarrays, numpy arrays, and pynapple objects.
What is jax?
jax is a Google-supported python library for automatic differentiation. It has all sorts of neat features, but the most relevant of which for nemos is its GPU-compatibility and just-in-time compilation (both of which make code faster with little overhead!), as well as the collection of optimizers present in jaxopt.
First, we require that our predictors and our spike counts have the same
number of time bins. We can achieve this by down-sampling our current to the
spike counts to the proper resolution using the
bin_average
method from pynapple:
binned_current = current.bin_average(bin_size)
print(f"current shape: {binned_current.shape}")
# rate is in Hz, convert to KHz
print(f"current sampling rate: {binned_current.rate/1000.:.02f} KHz")
print(f"\ncount shape: {count.shape}")
print(f"count sampling rate: {count.rate/1000:.02f} KHz")
Out:
current shape: (28020,)
current sampling rate: 1.00 KHz
count shape: (28020, 1)
count sampling rate: 1.00 KHz
Secondly, we have to reshape our variables so that they are the proper shape:
predictors:(n_time_bins, n_features)count:(n_time_bins, )
Because we only have a single predictor feature, we'll use
np.expand_dims
to handle .
# add singleton dimensions for axis 1.
predictor = np.expand_dims(binned_current, 1)
# grab the spike counts for our one neuron
count = count[:, 0]
# check that the dimensionality matches nemos expectation
print(f"predictor shape: {predictor.shape}")
print(f"count shape: {count.shape}")
Out:
predictor shape: (28020, 1)
count shape: (28020,)
What if I have more than one neuron?
In this example, we're only fitting data for a single neuron, but you might wonder how the data should be shaped if you have more than one neuron -- do you add an extra dimension? or concatenate neurons along one of the existing dimensions?
In nemos, we always fit Generalized Linear Models to a single neuron at a time. We'll discuss this more in the following tutorial, but briefly: you get the same answer whether you fit the neurons separately or simultaneously, and fitting them separately can make your life easier.
Our last step is to convert these to jax.numpy arrays.
predictor = jax.numpy.asarray(predictor.values)
count = jax.numpy.asarray(count.values)
Info
In this example, we're being very explicit about this conversion to jax.numpy arrays. However, in general, nemos is able to properly convert from pynapple objects to jax.numpy arrays without any additional steps (it can similarly convert from numpy arrays to jax.numpy arrays). Thus, in later tutorials we will omit this step.
Fitting the model
Now we're ready to fit our model!
First, we need to define our GLM model object. We intend for users
to interact with our models like
scikit-learn
estimators. In a nutshell, a model instance is initialized with
hyperparameters that specify optimization and model details,
and then the user calls the .fit() function to fit the model to data.
We will walk you through the process below by example, but if you
are interested in reading more details see the Getting Started with scikit-learn webpage.
To initialize our model, we need to specify the regularizer and observation model objects, both of which should be one of our custom objects:
- Regularizer: this object specifies both the solver algorithm and the
regularization scheme. They are jointly specified because each
regularization scheme has a list of compatible solvers to choose between.
Regularization modifies the objective function to reflect your prior
beliefs about the parameters, such as sparsity. Regularization becomes more
important as the number of input features, and thus model parameters,
grows. They can be found within
nemos.regularizer.
Warning
With a convex problem like the GLM, in theory it does not matter which solver algorithm you use. In practice, due to numerical issues, it generally does. Thus, it's worth trying a couple to see how their solutions compare. (Different regularization schemes will always give different results.)
- Observation model: this object links the firing rate and the observed
spikes, describing the distribution of neural activity (and thus changing
the log-likelihood). For now, the only possible observation model is the
Poisson, though this may change in future releases. They can be found
within
nemos.observation_models.
For this example, we'll use an un-regularized LBFGS solver. We'll discuss regularization in a later tutorial.
Why LBFGS?
LBFGS is a quasi-Netwon method, that is, it uses the first derivative (the gradient) and approximates the second derivative (the Hessian) in order to solve the problem. This means that LBFGS tends to find a solution faster and is often less sensitive to step-size. Try other solvers to see how they behave!
model = workshop_utils.model.GLM(regularizer=nmo.regularizer.UnRegularized(solver_name="LBFGS"))
Now that we've initialized our model with the optimization parameters, we can
fit our data! In the previous section, we prepared our model matrix
(predictor) and target data (count), so to fit the model we just need to
pass them to the model:
model.fit(predictor, count)
Out:
<workshop_utils.model.GLM object at 0x7fab805e11b0>
Now that we've fit our data, we can retrieve the resulting parameters.
Similar to scikit-learn, these are stored as the coef_ and intercept_
attributes:
print(f"firing_rate(t) = exp({model.coef_} * current(t) + {model.intercept_})")
Out:
firing_rate(t) = exp([0.0532673] * current(t) + -9.757487229089023)
Note that model.coef_ has shape (n_features, ), while model.intercept_
is a scalar:
print(f"coef_ shape: {model.coef_.shape}")
print(f"intercept_ shape: {model.intercept_.shape}")
Out:
coef_ shape: (1,)
intercept_ shape: ()
It's nice to get the parameters above, but we can't tell how well our model is doing by looking at them. So how should we evaluate our model?
First, we can use the model to predict the firing rates and compare that to
the smoothed spike train. By calling predict() we can get the model's
predicted firing rate for this data. Note that this is just the output of the
model's linear-nonlinear step, as described earlier!
predicted_fr = model.predict(predictor)
# convert units from spikes/bin to spikes/sec
predicted_fr = predicted_fr / bin_size
# let's reintroduce the time axis by defining a TsdFrame.
# we must convert the firing rate to a numpy array (from jax.numpy) to make it
# pynapple compatible
predicted_fr = nap.TsdFrame(t=binned_current.t, d=np.asarray(predicted_fr))
# and let's smooth the firing rate the same way that we smoothed the smoothed
# spike train
smooth_predicted_fr = predicted_fr.smooth(50, 1000)
# and plot!
workshop_utils.plotting.current_injection_plot(current, spikes, firing_rate,
# plot the predicted firing rate that has
# been smoothed the same way as the
# smoothed spike train
predicted_firing_rate=smooth_predicted_fr)
Out:
/home/docs/checkouts/readthedocs.org/user_builds/nemos-workshop-feb-2024/envs/latest/lib/python3.10/site-packages/pandas/core/frame.py:706: DeprecationWarning: Passing a BlockManager to DataFrame is deprecated and will raise in a future version. Use public APIs instead.
warnings.warn(
/home/docs/checkouts/readthedocs.org/user_builds/nemos-workshop-feb-2024/envs/latest/lib/python3.10/site-packages/pandas/core/frame.py:706: DeprecationWarning: Passing a BlockManager to DataFrame is deprecated and will raise in a future version. Use public APIs instead.
warnings.warn(
/home/docs/checkouts/readthedocs.org/user_builds/nemos-workshop-feb-2024/envs/latest/lib/python3.10/site-packages/pandas/core/frame.py:706: DeprecationWarning: Passing a BlockManager to DataFrame is deprecated and will raise in a future version. Use public APIs instead.
warnings.warn(
What do we see above? Note that the y-axes in the final row are different for each subplot!
-
Predicted firing rate increases as injected current goes up -- Success!
-
The amplitude of the predicted firing rate only matches the observed amplitude in the third interval: it's too high in the first and too low in the second -- Failure!
-
Our predicted firing rate has the periodicity we see in the smoothed spike train -- Success!
-
The predicted firing rate does not decay as the input remains on: the amplitudes are identical for each of the bumps within a given interval -- Failure!
The failure described in the second point may seem particularly confusing -- approximate amplitude feels like it should be very easy to capture, so what's going on?
To get a better sense, let's look at the mean firing rate over the whole period:
# compare observed mean firing rate with the model predicted one
print(f"Observed mean firing rate: {np.mean(count) / bin_size} Hz")
print(f"Predicted mean firing rate: {np.mean(predicted_fr)} Hz")
Out:
Observed mean firing rate: 1.4275517487508924 Hz
Predicted mean firing rate: 1.430816697983436 Hz
We matched the average pretty well! So we've matched the average and the range of inputs from the third interval reasonably well, but overshot at low inputs and undershot in the middle.
We can see this more directly by computing the tuning curve for our predicted firing rate and comparing that against our smoothed spike train from the beginning of this notebook. Pynapple can help us again with this:
tuning_curve_model = nap.compute_1d_tuning_curves_continuous(predicted_fr, current, 15)
fig = workshop_utils.plotting.tuning_curve_plot(tuning_curve)
fig.axes[0].plot(tuning_curve_model, color="tomato", label="glm")
fig.axes[0].legend()
Out:
<matplotlib.legend.Legend object at 0x7fab77742860>
In addition to making that mismatch discussed earlier a little more obvious, this tuning curve comparison also highlights that this model thinks the firing rate will continue to grow as the injected current increases, which is not reflected in the data.
Viewing this plot also makes it clear that the model's tuning curve is approximately exponential. We already knew that! That's what it means to be a LNP model of a single input. But it's nice to see it made explicit.
Finishing up
There are a handful of other operations you might like to do with the GLM.
First, you might be wondering how to simulate spikes -- the GLM is a LNP
model, but the firing rate is just the output of LN, its first two steps.
The firing rate is just the mean of a Poisson process, so we can pass it to
jax.random.poisson:
spikes = jax.random.poisson(jax.random.PRNGKey(0), predicted_fr.values)
Note that this is not actually that informative and, in general, it is recommended that you focus on firing rates when interpreting your model. In particular, if your GLM includes auto-regressive inputs (e.g., neurons are connected to themselves or each other), simulate can behave poorly, see XXX for details.
Secondly, you may want a number with which to evaluate your model's
performance. As discussed earlier, the model optimizes log-likelihood to find
the best-fitting weights, and we can calculate this number using its score
method:
log_likelihood = model.score(predictor, count, score_type="log-likelihood")
print(f"log-likelihood: {log_likelihood}")
Out:
log-likelihood: -0.007939475843525579
This log-likelihood is un-normalized and thus doesn't mean that much by itself, other than "higher=better". When comparing alternative GLMs fit on the same dataset, whether that's models using different regularizers and solvers or those using different predictors, comparing log-likelihoods is a reasonable thing to do.
Info
Under the hood, nemos is minimizing the negative log-likelihood, as is
typical in many optimization contexts. score returns the real
log-likelihood, however, and thus higher is better.
Because it's un-normalized, however, the log-likelihood should not be compared across datasets (because e.g., it won't account for difference in noise levels). We provide the ability to compute the pseudo-$R^2$ for this purpose:
model.score(predictor, count, score_type='pseudo-r2-Cohen')
Out:
Array(0.30376282, dtype=float64)
Further Exercises
Despite the simplicity of this dataset, there is still more that we can do here. The following sections provide some possible exercises to try yourself!
Other stimulation protocols
We've only fit the model to a single stimulation protocol, but our dataset contains many more! How does the model perform on "Ramp"? On "Noise 2"? Based on the example code above, write new code that fits the model on some other stimulation protocol and evaluate its performance. Which stimulation does it perform best on? Which is the worst?
Train and test sets
In this example, we've used been fitting and evaluating our model on the same data set. That's generally a bad idea! Try splitting the data in to train and test sets, fitting the model to one portion of the data and evaluating on another portion. You could split this stimulation protocol into train and test sets or use different protocols to train and test on.
Model extensions
Our model did not do a good job capturing the onset transience seen in the data, and we could probably improve the match between the amplitudes of the predicted firing rate and smoothed spike train. How would we do that?
We could try adding the following inputs to the model, alone or together:
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Spiking history: we know neurons have a refactory period (they are unable to spike a second time immediately after spiking), so maybe making the model aware of whether the neuron spiked recently could help capture the onset transience.
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Current history: the model's input here is the current at the same moment as the spike, but that information is probably integrated over time. Maybe we can add additional time points.
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More complicated tuning curve: as we saw with the tuning curve plots, this model implicitly assumes that the relationship between current and firing rate is exponential, which is close but not quite right. Maybe we can improve that.
The proper way to add these in nemos makes use of Basis objects, which
we'll explore more in later tutorials. You can try the adding the spiking or
current history inputs without them (though the model won't do as well), or
return to this example after you've learned about Basis objects and how to
use them.
Citation
The data used in this tutorial is from the Allen Brain Map, with the following citation:
Contributors: Agata Budzillo, Bosiljka Tasic, Brian R. Lee, Fahimeh Baftizadeh, Gabe Murphy, Hongkui Zeng, Jim Berg, Nathan Gouwens, Rachel Dalley, Staci A. Sorensen, Tim Jarsky, Uygar Sümbül Zizhen Yao
Dataset: Allen Institute for Brain Science (2020). Allen Cell Types Database -- Mouse Patch-seq [dataset]. Available from brain-map.org/explore/classes/multimodal-characterization.
Primary publication: Gouwens, N.W., Sorensen, S.A., et al. (2020). Integrated morphoelectric and transcriptomic classification of cortical GABAergic cells. Cell, 183(4), 935-953.E19. https://doi.org/10.1016/j.cell.2020.09.057
Patch-seq protocol: Lee, B. R., Budzillo, A., et al. (2021). Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization. eLife, 2021;10:e65482. https://doi.org/10.7554/eLife.65482
Mouse VISp L2/3 glutamatergic neurons: Berg, J., Sorensen, S. A., Miller, J., Ting, J., et al. (2021) Human neocortical expansion involves glutamatergic neuron diversification. Nature, 598(7879):151-158. doi: 10.1038/s41586-021-03813-8
Total running time of the script: ( 0 minutes 27.065 seconds)
Download Python source code: 01_current_injection.py