# Convolution with matrices#

```
import numpy as np
import matplotlib.pyplot as plt
```

This page follows on directly from the convolution page. It builds on some simple 2D arrays (matrices) to the formal mathematical definition of convolution. This page may be useful to you as more background and deeper understanding. We often do implement convolution with matrices, so the matrix formulation here will be useful for when you see this in code, or you are implementing convolution yourself.

## Neural and hemodynamic prediction#

You will remember, from that page, that we were looking at *neural prediction*
models, with spikes or blocks of “on” activity, and plateaus of “off”
activity.

We were then convolving the neural activity with our estimate of the effect on blood flow of a instantaneous spike or “impulse” of neural activity. This estimate is the Hemodynamic Response Function (HRF).

From that page, we recover our example HRF.

```
def hrf(t):
"A hemodynamic response function"
return t ** 8.6 * np.exp(-t / 0.547)
```

In the main convolution page, we used very short time bins of 0.1 seconds to define whether the neural response was “on” or not. Thus, the shortest impulse we allow for is 0.1 seconds.

For what follows, it is a bit easier to see what is going on with a lower time resolution — say one time point per second. We will make the first event last 3 seconds:

```
times = np.arange(0, 40) # One time point per second
n_time_points = len(times)
neural_signal = np.zeros(n_time_points)
neural_signal[4:7] = 1 # A 3 second event
neural_signal[10] = 1
neural_signal[20] = 3
hrf_times = np.arange(20)
hrf_signal = hrf(hrf_times) # The HRF at one second time resolution
n_hrf_points = len(hrf_signal)
bold_signal = np.convolve(neural_signal, hrf_signal)
times_and_tail = np.arange(n_time_points + n_hrf_points - 1)
fig, axes = plt.subplots(3, 1, figsize=(8, 15))
axes[0].plot(times, neural_signal)
axes[0].set_title('Neural signal, 1 second resolution')
axes[1].plot(hrf_times, hrf_signal)
axes[1].set_title('Hemodynamic impulse response, 1 second resolution')
axes[2].plot(times_and_tail, bold_signal)
axes[2].set_title('Predicted BOLD signal from convolution, 1 second resolution')
```

```
Text(0.5, 1.0, 'Predicted BOLD signal from convolution, 1 second resolution')
```

Our algorithm, which turned out to give convolution, had us add a shifted, scaled version of the HRF to the output, for every index. Here is the algorithm from the convolution page:

Start with an output vector that is a vector of zeros;

For each index \(i\) in the

*input vector*(the neural signal):Prepare a shifted copy of the HRF vector, starting at \(i\). Call this the

*shifted HRF vector*;Multiply the shifted HRF vector by the value in the input at index \(i\), to give the

*shifted, scaled HRF vector*;Add the shifted scaled HRF vector to the output.

Now imagine that, instead of adding the shifted scaled HRF to the output vector, we store each shifted scaled HRF as a row in an array, that has one row for each index in the input vector. Then we can get the same output vector as before by taking the sum across the columns of this array:

```
N = n_time_points
M = n_hrf_points
shifted_scaled_hrfs = np.zeros((N, N + M - 1))
for i in range(N):
input_value = neural_signal[i]
# Storing the shifted, scaled HRF
shifted_scaled_hrfs[i, i : i + n_hrf_points] = hrf_signal * input_value
bold_signal_again = np.sum(shifted_scaled_hrfs, axis=0)
```

```
# We check that the result is almost exactly the same
# (allowing for tiny differences due to the order of +, * operations)
import numpy.testing as npt
npt.assert_almost_equal(bold_signal, bold_signal_again)
```

To visualize this, let’s look at the `shifted_scaled_hrfs`

matrix as an image
where darker colors indicate higher values. We can then look at it as a
collection of time series, and see how it adds to produce our expected BOLD
signal:

```
fig, axes = plt.subplots(3, 1, figsize=(8, 15))
axes[0].imshow(shifted_scaled_hrfs, cmap='Purples')
axes[0].set_title('Convolved events, from "above"')
axes[1].plot(times_and_tail, shifted_scaled_hrfs.T)
axes[1].set_title('Convolved events, as time series')
axes[2].plot(times_and_tail, bold_signal_again)
axes[2].set_title('Predicted BOLD signal from convolution, 1 second resolution')
```

```
Text(0.5, 1.0, 'Predicted BOLD signal from convolution, 1 second resolution')
```

We can also do exactly the same operation by first making an array with the
*shifted* HRFs, without scaling, and then multiplying each row by the
corresponding input value, before doing the sum. Here we are doing the
shifting first, and then the scaling, and then the sum. It all adds up to the
same operation:

```
# First we make the shifted HRFs
shifted_hrfs = np.zeros((N, N + M - 1))
for i in range(N):
# Storing the shifted HRF without scaling
shifted_hrfs[i, i : i + n_hrf_points] = hrf_signal
# Then do the scaling
shifted_scaled_hrfs = np.zeros((N, N + M - 1))
for i in range(N):
input_value = neural_signal[i]
# Scaling the stored HRF by the input value
shifted_scaled_hrfs[i, :] = shifted_hrfs[i, :] * input_value
# Then the sum
bold_signal_again = np.sum(shifted_scaled_hrfs, axis=0)
# This gives the same result, once again
npt.assert_almost_equal(bold_signal, bold_signal_again)
```

The `shifted_hrfs`

array looks like this as an image:

```
plt.imshow(shifted_hrfs, cmap='Purples')
```

```
<matplotlib.image.AxesImage at 0x7fed1ca8b130>
```

Each new row of `shifted_hrfs`

corresponds to the HRF, shifted by one more
column to the right:

```
fig, axes = plt.subplots(5, 1)
for row_no in range(5):
axes[row_no].plot(shifted_hrfs[row_no, :])
```

Now remember how matrix multiplication works:

Now let us make our input neural vector into a 1 by N row vector. If we *matrix
multiply* this vector onto the `shifted_hrfs`

array (matrix), then we do the
scaling of the HRFs and the sum operation, all in one go. Like this:

```
def as_row_vector(v):
" Convert 1D vector to row vector "
return v.reshape((1, -1))
```

```
neural_vector = as_row_vector(neural_signal)
# The scaling and summing by the magic of matrix multiplication
bold_signal_again = neural_vector @ shifted_hrfs
# This gives the same result as previously, yet one more time
npt.assert_almost_equal(as_row_vector(bold_signal), bold_signal_again)
```

The matrix transpose rule says \((A B)^T = B^T A^T\) where \(A^T\) is the transpose
of matrix \(A\). So we could also do this exact same operation by doing a matrix
multiply of the transpose of `shifted_hrfs`

onto the `neural_signal`

as a
column vector:

```
bold_signal_again = shifted_hrfs.T @ neural_vector.T
# Exactly the same, but transposed
npt.assert_almost_equal(as_row_vector(bold_signal), bold_signal_again.T)
```

In this last formulation, the `shifted_hrfs`

matrix is the *convolution*
matrix, in that (as we have just shown) you can apply the convolution of the
HRF by matrix multiplying onto an input vector.

## Convolution is like cross-correlation with the reversed HRF#

We are now ready to show something slightly odd that arises from the way that convolution works.

Consider index \(i\) in the input (neural) vector. Let’s say \(i = 25\). We want to get value index \(i\) in the output (hemodynamic vector). What do we need to do?

Looking at our non-transposed matrix formulation, we see that value \(i\) in the
output is the matrix multiplication of the neural signal (row vector) by
column \(i\) in `shifted_hrfs`

. Here is a plot of column 25 in
`shifted_hrfs`

:

```
plt.plot(shifted_hrfs[:, 25])
```

```
[<matplotlib.lines.Line2D at 0x7fed1c26cac0>]
```

The column contains a *reversed* copy of the HRF signal, where the first value
from the original HRF signal is at index 25 (\(i\)), the second value is at
index 24 (\(i - 1\)) and so on back to index 25 - 20 = 5. The reversed HRF
follows from the way we constructed the rows of the original matrix. Each new
HRF row was shifted across by one column, therefore, reading up the columns
from the diagonals, will also give you the HRF shape.

Let us rephrase the matrix multiplication that gives us the value at index \(i\)
in the output vector. Call the neural input vector \(\mathbf{n}\) with values
\(n_0, n_1 ... n_{N-1}\). Call the `shifted_hrfs`

array \(\mathbf{S}\) with \(N\)
rows and \(N + M - 1\) columns. \(\mathbf{S}_{:,i}\) is column \(i\) in
\(\mathbf{S}\).

So, the output value \(o_i\) is given by the matrix multiplication of row \(\mathbf{n}\) onto column \(\mathbf{S}_{:,i}\). The matrix multiplication (dot product) gives us the usual sum of products as the output:

The formula above describes what is happening in the matrix multiplication in this piece of code:

```
i = 25
bold_i = neural_vector @ shifted_hrfs[:, i]
```

```
npt.assert_almost_equal(bold_i, bold_signal[i])
```

Can we simplify the formula without using the `shifted_hrfs`

\(\mathbf{S}\)
matrix? We saw above that column \(i\) in `shifted_hrfs`

contains a reversed
HRF, starting at index \(i\) and going backwards towards index 0.

The 1-second resolution HRF is our array `hrf_signal`

.
So `shifted_hrfs[i, i]`

contains `hrf_signal[0]`

, `shifted_hrfs[i-1, i]`

contains
`hrf_signal[1]`

and so on. In general, for any index \(j\) into
`shifted_hrfs[:, i]`

, `shifted_hrfs[j, i] == hrf_signal[i-j]`

(assuming
we return zero for any `hrf_signal[i-j]`

where `i-j`

is outside the
bounds of the vector, with `i-j`

< 0 or \(\geq\) M).

Realizing this, we can replace \(\mathbf{S}_{:,i}\) in our equation above. Call
our `hrf_signal`

vector \(\mathbf{h}\) with values \(h_0, h_1, ... h_{M-1}\).
Then:

This is the sum of the {products of the elements of \(\mathbf{n}\) with the matching elements from the [reversed HRF vector \(\mathbf{h}\), shifted by \(i\) elements]}.

## The mathematical definition for convolution#

This brings us to the abstract definition of convolution for continuous functions.

In general, call the continuous input a function \(f\). In our case the input signal is the neuronal model, that is a function of time. This is the continuous generalization of the vector \(\mathbf{n}\) in our discrete model. The continuous function to convolve with is \(g\). In our case \(g\) is the HRF, also a function of time. \(g\) is the generalized continuous version of the vector \(\mathbf{h}\) in the previous section. The convolution of \(f\) and \(g\) is often written \((f * g)\) and for any given \(t\) is defined as:

As you can see, and as we have already discovered in the discrete case, the convolution is the integral of the product of the two functions as the second function \(g\) is reversed and shifted.

See : the wikipedia convolution definition section for more discussion.