Download Affiliates Day Poster Joseph Young

Causality Analysis of Neuron Spike Timings
Recorded During Task Learning
Joseph Young1, Valentin Dragoi2, Behnaam Aazhang1
1 Department
of Electrical and Computer Engineering, Rice University, Houston, USA
2 Department of Neurobiology and Anatomy, The University of Texas Medical School at Houston, Houston, USA
Understanding Learning
Experimental Setup - Visual Task
Learning: Well observed phenomenon
•
“Practice makes perfect”
•
Poorly understood in terms of neurophysiology
Directed Information Estimation
Rhesus Monkeys Performed Rotation Recognition Task:
Definition
Time
2
3
4
5
Fixation
Target
…
Test
Decision
500 ms
300 ms
1000 ms
Rotated or not?
300 ms
Low f
Spike
Channels
Neuronal recordings
were taken from
Rhesus monkeys’
visual cortex (V4)
while they performed
a visual task
Field
Potential
Channels
MAP
Multichannel Acquisition
Processor (Plexon)
Contact
Joseph Young
[email protected]
http://www.owlnet.rice.edu/~jy46
Department of Electrical and
Computer Engineering
Rice University, Houston, TX
Single-units
amplified &
filtered
Threshold
i=1
H(Yi |Y i
1
Spike-Field Coherence (SFC) Was Higher During Learning
•
SFC measures synchronization between single neuron and local population as a function of
frequency
•
SFC was greatest in theta band during learning
•
After learning, changes in SFC disappeared for all frequencies
, X i)
•
•
…
Test
200 ms
200 ms
Decision
Analysis will focus on 200 ms window after each image
onset to avoid synchronicity due to stimulus presentation
21 neurons to analyze from 17 channels (electrodes)
Data from 623 trials of task
Contributions
Causality Analysis Will:
•
Quantify influence between neurons
•
Provide insight into how influence changes during
learning
•
Suggest procedure / areas for stimulated learning
;
11
•
Target
{
Neuronal Synchronization During Task Learning
Moving From Synchronization to Causation
High f
n
X
Neural Spike Analysis
For the decision period (5), the monkey must release a bar by the end of the period if the test image
is a rotated version of the target image. Correct decision results in juice for the monkey.
Universal Estimation of Directed Information
•
Feed sequences of neuronal spikes into the context-tree
weighting (CTW) algorithm
CTW
•
M-ary tree with weights
•
Provides probability estimates to plug into directed
information equations (quantifying causality)
Procedure:
•
Slide M-length window across an input sequence of finitealphabet size
•
Match window with context of a bottom level node (e.g.
“01”) and update parameters based on next symbol after
the window
H(Y n ||X n )
1500 ms
Neuronal Data Collection
Neuronal
Signals from
V4
)
Estimation
•
Plug in probabilities from Context-Tree
•
Estimate DI flow between neuron pairs
Fixation
New Tech Allows Ensemble Recording
•
Can observe neuronal network
•
Functional / effective connectivity analysis
•
Research at UTHSC studied neuronal synchronization in
Rhesus monkey brains during task learning
•
We will further analyze this same data set using directed
information (DI) between neurons to determine causality
1
Causally Conditioned Entropy:
H(Y n ||X n ) ,
Levels of Analysis
1990s Focus on Single Neurons
•
Learning thought to involve modifications in the brain on
the order of single cells and single synapses
•
No support - network more promising
= H(Y n )
1
[blank]
i=1
I(X i ; Yi |Y i
{
Objective: Discover how neuronal population connectivity
alters over the time course of learning
•
Quantify causality between recorded spike timings
•
Produce graphical model of connectivity
Preamplifier
I(X n ! Y n ) ,
n
X
1
0
01
10
00
Future Work:
•
Carry out recordings on larger ensembles to account
for effective connectivity between different areas during
learning
•
Stimulate population areas to improve learning
•
Develop model that describes the time course of
learning in the visual cortex
Example context-tree for M=2
Spikes
References
1. Ye Wang and Valentin Dragoi. Rapid learning in visual cortical networks. eLife, 4:e08417, aug 2015.
2. J. Jiao, H. H. Permuter, L. Zhao, Y. H. Kim, and T. Weissman. Universal estimation of directed information. IEEE Transactions on Information Theory, 59(10):6220–6242, Oct
2013.
3. F. M. J. Willems, Y. M. Shtarkov, and T. J. Tjalkens. The context-tree weighting method: basic properties. IEEE Transactions on Information Theory, 41(3):653–664, May 1995.
4.
5.
6.
Sagi, D., & Tanne, D. Perceptual learning: learning to see. Current opinion in neurobiology. 1994.
Karni, A., & Bertini, G. Learning perceptual skills: behavioral probes into adult cortical plasticity. Current opinion in neurobiology. 1997.
Rhesus image: From Einar Fredriksen (http://flickr.com/photos/85198067@N00/4185543087), seen at https://commons.wikimedia.org/wiki/
File:Macaca_mulatta_in_Guiyang.jpg