1. Analyses of neurons population data
Artur Luczak, Ph.D.
Canadian Centre for Behavioural Neuroscience
University of Lethbridge, AB, Canada

2. Recording neuronal populations
Courtesy of S. Sakata
Recording neuronal populations
Silicon probes:
Gomez Palacio Schjetnan & Luczak. J Vis Exp. 2011

3. spontaneous sensory evoked activity
sec

4. I never came upon any of my discoveries through the process
Deciphering codes
I never came upon any of my discoveries through the process of rational thinking.
I never came upon
any of my discoveries
through the process
of rational thinking.
Albert Einstein

5. Population vectors Number of spikes in e.g. 100 ms window
Firing rate vectors
We saw constrains on temp profile , now we look at constrains at firing rate combinations

6. Space of neuronal responses
Single dot: activity of two neurons at single trial or UPstate
2 things to notice
If i would shuffle activ of neurons between trials , it is if those neurons would be firing independently from eachother ...
It is 2 dim space
For 3 dim .... To plot for 50 dim we used MDS , the objective of this method ....

7. Multidimensional scaling
Y = mdscale(D,p)
Firing rate vectors
Single dot: activity of 45 neurons at single trial or UPstate
Single dot
Spontaneous events define “realm of the possible”
Sensory responses lie within this realm.
Luczak et al. Neuron 2009

8. Approximating firing rate
Dayan & Abbott 2000

9. Smoothing kernels conv( ker, spk ) Discrete convolution
kernel doesn’t have to be symmetric

10. Parameterizing spike train
Number of spike
First spike latency
Position of peak
Center of mass (Latency)
Fitting curves
- gamma distr.
- Gaussian
- exponential
- …
latency.m

11. Distances - examples Corr coef = large Corr coef = large
Corr coef = small

12. JD Victor metric
A diagram of a sequence of elementary steps that transforms spike train A into spike train B. Each elementary step is one of three types: deletion of a spike (deleted spike shown in red), insertion of a spike (inserted spike shown in green), or shifting a spike in time (blue arrows). JD Victor.Current Opinion in Neurob. 2005

13. Correlation matrices

14. Crosscorrelogram
xcorr( x, y )

15. Joint Peristimulus Time Histogram
Vaadia et. al. (1995) Nature
JPSTH gives probability distribution of all possible spike pairs.

16. Quantifying the response of sensory neurons
spike-triggered average stimulus (“reverse correlation”)
(Rieke et al. 1997; Dayan & Abbott 2000)

17. White-noise stimulation
Klein et al. J Comp Neurosci 2000

18. Spectrotemporal responses
Spectrotemporal responses in bird auditory forebrain
[Sen, Theunissen, Doupe]

19. Packets of neuronal activity
Synchronized
(sleepy brain)
Tone
(attentive brain)
Desynchronized
Luczak et al PNAS
Luczak et al Neuron
Luczak et al J Neurosci.
Bermudez et al Neuron
Luczak et al Nature Rev. Neurosci.

20. Neuronal responses to different stimuli have similar structure
Luczak et al Nature Rev. Neurosci.

21. “attentive” brain state (urethane anesth. + amphetamine)
Packet plasticity
Desynchronized
“attentive” brain state
(urethane anesth. + amphetamine)
Bermudez et al. Neuron (2013)

22. Template Matching (TM) analyses
xcorr_TM_demo.m

23. Bermudez et al. Neuron (2013)

24. Memory reactivation detected by template matching
Task
2600 msec
Non-REM sleep
370 msec
Lee & Wilson, Neuron, 2002
Euston et al., Science, 2007

25. Testing significance

26. Poisson process - statistical model of spike train
A Poisson process is a stochastic process which is used for modeling random events in time that occur to a large extent independently of one another
spk = rand( 1, 100 ) < 0.1

27. Shuffling

28. Spike Jitter

29. Sample code
Available at:

30. neuron
time

31. time

32. time

33. Latency
Time diff b/n neurons

34. template
neuron
time
time

37. Questions?
Krakow, Poland

38. Thank you
Discovery Accelerator Supplement

39. Spike patterns search: triplets
Abeles et al.
Luczak et al.