Order in Spontaneous Behavior
Publication Date
May 16, 2007
Journal
PLOS ONE
Authors
Alexander Maye, Chih Hao Hsieh, George Sugihara & Björn Brembs
Volume
2
Issue
5
Pages
e443
DOI
https://dx.plos.org/10.1371/journal.pone.0000443
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0000443
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/17505542
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1865389
Europe PMC
http://europepmc.org/abstract/MED/17505542
Web of Science
000207445900003
Scopus
55849098515
Mendeley
http://www.mendeley.com/research/order-spontaneous-behavior
Events
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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/467289", "https://ndownloader.figshare.com/files/467312", "https://ndownloader.figshare.com/files/467335", "https://ndownloader.figshare.com/files/467351", "https://ndownloader.figshare.com/files/467383", "https://ndownloader.figshare.com/files/467395"], "description"=>"<div><p>Brains are usually described as input/output systems: they transform sensory input into motor output. However, the motor output of brains (behavior) is notoriously variable, even under identical sensory conditions. The question of whether this behavioral variability merely reflects residual deviations due to extrinsic random noise in such otherwise deterministic systems or an intrinsic, adaptive indeterminacy trait is central for the basic understanding of brain function. Instead of random noise, we find a fractal order (resembling Lévy flights) in the temporal structure of spontaneous flight maneuvers in tethered <em>Drosophila</em> fruit flies. Lévy-like probabilistic behavior patterns are evolutionarily conserved, suggesting a general neural mechanism underlying spontaneous behavior. <em>Drosophila</em> can produce these patterns endogenously, without any external cues. The fly's behavior is controlled by brain circuits which operate as a nonlinear system with unstable dynamics far from equilibrium. These findings suggest that both general models of brain function and autonomous agents ought to include biologically relevant nonlinear, endogenous behavior-initiating mechanisms if they strive to realistically simulate biological brains or out-compete other agents.</p></div>", "links"=>[], "tags"=>["spontaneous"], "article_id"=>152134, "categories"=>["Mental Health", "Mathematics", "Neuroscience", "Cell Biology", "Cancer", "Physiology", "Medicine"], "users"=>["Alexander Maye", "Chih-hao Hsieh", "George Sugihara", "Björn Brembs"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0000443.s001", "https://dx.doi.org/10.1371/journal.pone.0000443.s002", "https://dx.doi.org/10.1371/journal.pone.0000443.s003", "https://dx.doi.org/10.1371/journal.pone.0000443.s004", "https://dx.doi.org/10.1371/journal.pone.0000443.s005", "https://dx.doi.org/10.1371/journal.pone.0000443.s006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Order_in_Spontaneous_Behavior/152134", "title"=>"Order in Spontaneous Behavior", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2007-05-16 00:35:34"}
  • {"files"=>["https://ndownloader.figshare.com/files/949613"], "description"=>"<p>A–According to the robot-hypothesis, there is an unambiguous mapping of sensory input to behavioral output. If the behavioral output is not constant in a constant environment, there are a number of possible sources of noise, which would be responsible for the varying output. B–In a competing hypothesis, non-constant output is generated intrinsically by an initiator of behavioral activity. Note that the sources of noise have been omitted in B merely because their contribution may be small, compared to that of the initiator, not because they are thought to be non-existent.</p>", "links"=>[], "tags"=>["conceptualizing", "open-loop"], "article_id"=>619941, "categories"=>["Mental Health", "Mathematics", "Neuroscience", "Plant Biology", "Infectious Diseases", "Physiology", "Medicine"], "users"=>["Alexander Maye", "Chih-hao Hsieh", "George Sugihara", "Björn Brembs"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0000443.g001"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Alternative_models_conceptualizing_the_open_loop_experiment_/619941", "title"=>"Alternative models conceptualizing the open-loop experiment.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2007-05-16 02:45:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/949726"], "description"=>"<p>The fly is flying stationarily in a cylindrical arena homogeneously illuminated from behind. The fly's tendency to perform left or right turns (yaw torque) is measured continuously and fed into the computer. In closed-loop, the computer controls arena rotation (single stripe or uniform texture as patterns on the arena wall). An additional white screen (not shown) covered the arena from above for all groups.</p>", "links"=>[], "tags"=>["simulator"], "article_id"=>620046, "categories"=>["Mental Health", "Mathematics", "Neuroscience", "Plant Biology", "Infectious Diseases", "Physiology", "Medicine"], "users"=>["Alexander Maye", "Chih-hao Hsieh", "George Sugihara", "Björn Brembs"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0000443.g002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Flight_simulator_set_up_/620046", "title"=>"Flight simulator set-up.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2007-05-16 00:00:46"}
  • {"files"=>["https://ndownloader.figshare.com/files/949795"], "description"=>"<p>A–GRIP analysis of ISIs. Plotted are the mean standard deviations from the theoretically expected random value for fly ISI series and the random series generated by a Poisson process. The fly deviations are all significantly larger than the values for the computer-generated series. B–Log-log plots of ISIs. The Lévy exponent μ is calculated from the inclination of the linear fit. A Lévy distribution is defined as 1<μ<3. Smaller values indicate a larger proportion of long ISIs. A Cox Process (<i>cox</i>) reveals a similar power-law structure as the flies. Error bars are S.E.M.s throughout. See <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000443#s4\" target=\"_blank\"><i>Methods</i></a> for details and statistics.</p>", "links"=>[], "tags"=>["computational biology/computational neuroscience", "computer science/numerical analysis and theoretical computing", "ecology/behavioral ecology", "evolutionary biology/animal behavior", "mathematics/nonlinear dynamics", "neuroscience/behavioral neuroscience", "neuroscience/motor systems", "physiology/integrative physiology", "physiology/motor systems", "mental health/psychology"], "article_id"=>620107, "categories"=>["Mental Health", "Mathematics", "Neuroscience", "Plant Biology", "Infectious Diseases", "Physiology", "Medicine"], "users"=>["Alexander Maye", "Chih-hao Hsieh", "George Sugihara", "Björn Brembs"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0000443.g003"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spontaneous_behavior_is_not_simply_random_/620107", "title"=>"Spontaneous behavior is not simply random.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2007-05-16 00:01:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/949882"], "description"=>"<p>A–While the correlation dimension converges on a group-specific value with increasing embedding dimension for fly-generated ISIs (<i>openloop, onestripe, uniform</i>), a number sequence generated randomly by a Cox Process (<i>cox</i>) diverges. B–Probability to obtain the computed correlation dimensions in A by random shuffling of the original data. While the <i>cox</i> group exceeds an alpha value of .05, the three fly groups stay well below that threshold.</p>", "links"=>[], "tags"=>["computational biology/computational neuroscience", "computer science/numerical analysis and theoretical computing", "ecology/behavioral ecology", "evolutionary biology/animal behavior", "mathematics/nonlinear dynamics", "neuroscience/behavioral neuroscience", "neuroscience/motor systems", "physiology/integrative physiology", "physiology/motor systems", "mental health/psychology"], "article_id"=>620187, "categories"=>["Mental Health", "Mathematics", "Neuroscience", "Plant Biology", "Infectious Diseases", "Physiology", "Medicine"], "users"=>["Alexander Maye", "Chih-hao Hsieh", "George Sugihara", "Björn Brembs"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0000443.g004"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Correlation_dimension_/620187", "title"=>"Correlation dimension.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2007-05-16 00:03:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/949969"], "description"=>"<p>If the slope of the log-log plots of the r.m.s. fluctuation (exponent α, see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000443#s4\" target=\"_blank\"><i>Methods</i></a>) deviates significantly from ½, long-range correlations exist in the time series. All three fly groups show a significant deviation from 0.5. The deviation of branched Poisson processes (<i>BPP</i>), however, depends on the nonlinearity of the filter function used to drive the Poisson processes and is significantly smaller than that of fly ISI series. *-significant difference from 0.5.</p>", "links"=>[], "tags"=>["correlations"], "article_id"=>620293, "categories"=>["Mental Health", "Mathematics", "Neuroscience", "Plant Biology", "Infectious Diseases", "Physiology", "Medicine"], "users"=>["Alexander Maye", "Chih-hao Hsieh", "George Sugihara", "Björn Brembs"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0000443.g005"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Long_range_correlations_in_fly_ISIs_/620293", "title"=>"Long-range correlations in fly ISIs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2007-05-16 00:04:53"}
  • {"files"=>["https://ndownloader.figshare.com/files/950087"], "description"=>"<p>A–S-Map results. Depicted are the averaged results for fly ISIs and raw yaw torque series (for clarity, only <i>openloop</i> data are shown here), together with two automat simulations. The fly ISI series shows a slightly improved forecast skill with increasingly nonlinear S-map solutions (increasing Θ). Fly yaw torque series yield both a better overall forecast skill as well as increased nonlinear improvement. The automat simulation can be tuned to produce both linear and nonlinear output. B–Sample raw yaw torque data traces from a real fly and the two versions of the simulated agent depicted in A (automat 1, automat 2). S-Map results for the other two groups are depicted in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000443#pone.0000443.s005\" target=\"_blank\">Fig. S5</a>.</p>", "links"=>[], "tags"=>["implies"], "article_id"=>620409, "categories"=>["Mental Health", "Mathematics", "Neuroscience", "Plant Biology", "Infectious Diseases", "Physiology", "Medicine"], "users"=>["Alexander Maye", "Chih-hao Hsieh", "George Sugihara", "Björn Brembs"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0000443.g006"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Nonlinearity_implies_instability_/620409", "title"=>"Nonlinearity implies instability.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2007-05-16 00:06:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/950217"], "description"=>"<p>A–Open-loop model as proposed in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0000443#pone-0000443-g001\" target=\"_blank\">Fig. 1b</a> (for the <i>openloop</i> group). B–Closed-loop model (for the <i>onestripe</i> and <i>uniform</i> groups). Performance in a situation with a closed reafferent feedback loop is commonly modeled with a state estimator, cross-correlating sensory input with recent motor commands via an efference copy (EC). Such an evaluation is required for efficient behavioral control of incoming sensory data.</p>", "links"=>[], "tags"=>["open-and", "closed-loop"], "article_id"=>620535, "categories"=>["Mental Health", "Mathematics", "Neuroscience", "Plant Biology", "Infectious Diseases", "Physiology", "Medicine"], "users"=>["Alexander Maye", "Chih-hao Hsieh", "George Sugihara", "Björn Brembs"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0000443.g007"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Suggested_models_for_open_and_closed_loop_experiments_/620535", "title"=>"Suggested models for open-and closed-loop experiments.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2007-05-16 00:08:55"}

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Relative Metric

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