<ul>
<li>0: As you suggested, use a temporal filter to get a smoothed rate representation</li>
<li>1.a: Clean up each frame by using (e.g.) a morphological opening then closing step</li>
<li>1.b: Locate the centres of bumps, perhaps using non-maximum suppression.</li>
<li>1.c: Choose a reasonable activity threshold for neurons to still be "inside" the bump: Maybe 80% of the distance between zero and the activity at the bump-centre.</li>
<li>1.d: Assign neurons to bumps by finding connected regions around the bump centres.</li>
<li>2, 2: Once you have the neurons assigned to bumps, this is just simple stats</li>
<li>4: Track bumps between frames by assigning nearest bump centres across frames. Apply some uniqueness criterion to detect bumps appearing and disappearing.</li>
<li>5: This is also easy once you have the bumps tracked.</li>
</ul>

CC BY-SA 3.0
2014-09-09T09:21:26.387Z
2014-09-09T09:21:26.387Z
Dylan Richard Muir
<p>I am currently working with Spiking Neural Networks and multi-(meta)-stable attractor states.</p>

<p>What I observe in my simulations are 'bump' attractors that appear, disappear, and may wander around. Here is a screen shot from a simulation:</p>

<p><img src="https://i.sstatic.net/8MIiD.png" alt="Bump Activity"> </p>

<p>Dark blue dots represent excitatory neurons' spikes (fading into white to leave a trace), and dark red triangles representing inhibitory neurons' spikes.</p>

<p>If neurons are currently part of a 'bump' cluster, they exhibit a high firing rate, otherwise the rate is much lower.</p>

<p>The neurons are arranged on a 2D torus map (periodic boundary conditions).
Thus, for every neuron I have its respective x and y coordinate plus the timing of the spikes.</p>

<p>What I want and need is a algorithm that</p>

<ol>
<li>can detect and distinguish neurons within the bump state from non-bump states</li>
<li>measure the average size of a bump</li>
<li>the average number of bumps per unit time and unit map size</li>
<li>the average lifetime of bump (as I said they are meta-stable)</li>
<li>In case the bumps are "wandering" the average speed or drift of a bump</li>
</ol>

<p>What might be a suitable clustering algorithm in order to perform these steps? Would it be more useful to convolve the spike times with a filter in order to get some rate representation?</p>

<computational-modeling><neural-network><dynamic-systems>
Spiking Neural Network Simulation: Measuring and Classifying Bump Attractor States
SmCaterpillar
# Spiking Neural Network Simulation: Measuring and Classifying Bump Attractor States

(Asked by: username_0 on 2014-08-19)

I am currently working with Spiking Neural Networks and multi-(meta)-stable attractor states.

What I observe in my simulations are 'bump' attractors that appear, disappear, and may wander around. Here is a screen shot from a simulation:

![Bump Activity](https://i.sstatic.net/8MIiD.png)

Dark blue dots represent excitatory neurons' spikes (fading into white to leave a trace), and dark red triangles representing inhibitory neurons' spikes.

If neurons are currently part of a 'bump' cluster, they exhibit a high firing rate, otherwise the rate is much lower.

The neurons are arranged on a 2D torus map (periodic boundary conditions).
Thus, for every neuron I have its respective x and y coordinate plus the timing of the spikes.

What I want and need is a algorithm that

1. can detect and distinguish neurons within the bump state from non-bump states
2. measure the average size of a bump
3. the average number of bumps per unit time and unit map size
4. the average lifetime of bump (as I said they are meta-stable)
5. In case the bumps are "wandering" the average speed or drift of a bump

What might be a suitable clustering algorithm in order to perform these steps? Would it be more useful to convolve the spike times with a filter in order to get some rate representation?

**Tags:** computational-modeling, neural-network, dynamic-systems


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## 1 answers

### Answer by username_1 on 2014-09-09. Score: 1 | Accepted answer

* 0: As you suggested, use a temporal filter to get a smoothed rate representation
* 1.a: Clean up each frame by using (e.g.) a morphological opening then closing step
* 1.b: Locate the centres of bumps, perhaps using non-maximum suppression.
* 1.c: Choose a reasonable activity threshold for neurons to still be "inside" the bump: Maybe 80% of the distance between zero and the activity at the bump-centre.
* 1.d: Assign neurons to bumps by finding connected regions around the bump centres.
* 2, 2: Once you have the neurons assigned to bumps, this is just simple stats
* 4: Track bumps between frames by assigning nearest bump centres across frames. Apply some uniqueness criterion to detect bumps appearing and disappearing.
* 5: This is also easy once you have the bumps tracked.


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