Vidna kognicija IV

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Vidna kognicija IV

• Danko Nikolic

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Neuronal Avalanches

• in Vivo

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European avalanche-size table

• 1 - Slough
• Small snow slide that cannot bury a person.
• length lt50 m
• volume lt100 m³
• 2 - Small
• Stops within the slope.
• length lt100 m
• volume lt1,000 m³
• 3 - Medium
• Runs to the bottom of the slope.
• length lt1,000 m
• volume lt10,000 m³
• 4 Large
• Runs over flat areas, may reach the valley
  bottom.
• length gt1,000 m
• volume gt10,000 m³

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Distribution of Avalanche-Sizes
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• Power law in complex systems
• Earthquakes, forest fires, evolution of species
• Size of US cities, citations of papers

Does the brain generate neuronal avalanches
with power law statistics ?
?How do we imagine such neuronal
avalanches? ?How do we observe them in the brain?
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Neuronal Avalanche of Spikes
Size 24 spikes
Lifetime 14 ms
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Methods

• Recording
• ? Michigan Probes
• Recording sites
• ? 3 cats Area 17
• ? 1 cat Area 17 Area 21

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• Spontaneous activity under anesthesia
• 7 datasets ? Duration 100 500 s
• Units ? Per dataset 105 - 158
• ? Single units 68 - 118
• ? Multi units 26 - 50

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Definition of Avalanches
?t (ms)
6 ms
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Power Law
Cat Col05 Probe1
?t 4 ms
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Power law is independent of ?t

• ?t was varied between 1 and 10 ms

?Number of extracted avalanches decreases with
larger ?t ?Number of larger avalanches increases
relative to smaller ones

• Power law remains stable irrespective of ?t !
• Exponent of power law increases with ?t

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Non - Power Law

• Dependence on ?t ? 2 cases
• Exponential-like distribution remains robust
• With small ?t ? power law / with large ?t
  ? exponential function

Cat Col11 Probe 1
?t 4 ms
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Exponent
Cat Col05 Probe1
?t (ms)
? Exponent increases with larger bin-sizes
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Exponent ?tavg
?tavg mean of time intervals between spikes ?
statistical approach for optimal ?t ? Separation
and concatenation of avalanches is minimized
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Exponent (?tavg) -1.8
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Lifetime
Lifetime distribution of avalanches does not
follow a power law in any of the
probes, irrespective of ?t.
Cat Col05 Probe 1
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Conclusions

• Neuronal avalanches defined by spikes
• ? Power law in some size-distributions
• ? Exponent -1.8
• ? No Power law in other size - distributions
• ? No Power law in lifetime distributions
• Interpretation
• ? Self-organized criticality (SOC)
• ? Critical branching processes

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Teme

• Neurofizioloki kodovi prijenosa i obrade
  informacija u vidnom sustavu
• Dva kôda za percepciju svjetline
• Problem povezivanja dijelova vidne scene u
  cjelinu (tzv. binding problem)
• Uloga panje u pohranjivanju informacija u radno
  pamcenje
• Uloga radnog pamcenja za formiranje dugorocnog
  vidnog pamcenja
• Mehanizmi sinestezijskih asocijacija

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Figure 2. The procedure used in Experiment 1.
Participants detected the target items and
memorized the shapes surrounding them. The
presentation time that was needed to achieve high
WM performance was determined by the participants
themselves. After an interval of 8 s participants
had to judge whether the test shape matched one
of the target shapes. ITI Inter-trial interval.
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Figure 3. Results from Experiment 1. A. Mean
response accuracy at test as a function of WM
load and attentional demand. B. Mean presentation
times as a function of WM load and attentional
demand (PO pop-out NPO non pop-out). Vertical
bars the standard error of the mean.
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Figure 4. The procedure used in Experiment 2.
Participants detected and counted the target
items. After pressing the response button a
question mark appeared prompting the participants
to enter the number of the counted targets. ITI
Inter-trial interval.
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Figure 5. Results from Experiment 2. A. Mean
response accuracy at test as a function of WM
load and attentional demand. B. Mean counting
times as a function of WM load and attentional
demand (PO pop-out NPO non pop-out). Vertical
bars the standard error of the mean.
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Experiment 3 Information about the upcoming
number of targets.
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Figure 6. Results from Experiment 3 compared to
the results from Experiment 1. A. Mean response
accuracy at test as a function of WM load and
attentional demand. B. Mean presentation times as
a function of WM load and attentional demand (PO
pop-out NPO non pop-out). C. Differences in the
presentation times between pop-out and non
pop-out conditions across WM load conditions.
Vertical bars the standard error of the mean.
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Figure 6. C. Differences in the presentation
times between pop-out and non pop-out conditions
across WM load conditions. Vertical bars the
standard error of the mean.
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Figure 7. A. Empirically obtained offset in the
presentation times produced by lack of pop-out in
Experiment 3 and theoretically predicted offset
based on search times from Experiment 2, computed
for five different memory loads. B, Offset in the
presentation times produced by lack of pop-out
that is not explained by the visual search and
that is expressed as a function of the number of
target items. Dashed line linear fit (see text).
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Figure 8. The procedure used in Experiment 4.
Participants detected the target items and
memorized their locations only. After an interval
of 8 s participants judged whether the location
of the missing item in the test array matched one
of the target locations. ITI Inter-trial
interval.
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Figure 9. Results from Experiment 4. A. Mean
response accuracy at test as a function of WM
load and attentional demand. B. Mean presentation
times as a function of WM load and attentional
demand (PO pop-out NPO non pop-out). Vertical
bars the standard error of the mean.
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Experiment 5 same as experiment 4 but with
knowing the upcoming number of targets (as in
experiment 3).
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Figure 10. Results from Experiment 5 compared to
the results from Experiment 3. A. Mean
presentation times as a function of WM load and
attentional demand (PO pop-out NPO non
pop-out). B. Differences in the presentation
times between pop-out and non pop-out conditions
across WM load conditions. Vertical bars the
standard error of the mean. C. Offset in the
presentation times produced by lack of pop-out
that is not explained by the visual search and
that is expressed as a function of the number of
target items. Dashed lines linear fit.
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Figure 10. C. Offset in the presentation times
produced by lack of pop-out that is not explained
by the visual search and that is expressed as a
function of the number of target items. Dashed
lines linear fit.
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Conclusions

• WM and attention interfere and perhaps use the
  same resources.
• Memory for locations prevents interference.

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Funkcionalna magnetska rezonanca
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BOLD signal
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Attentional Demand Influences Strategies for
Encoding into Visual Working Memory Jutta S.
Mayer1, Robert A. Bittner1, David E. J. Linden1,
2 and Danko Nikolic3, 4 (under review)
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Information maintenance
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Fraktalna structura aktivnosti u mozgu
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