Adaptation to Changes in Higher-Order Stimulus Statistics in the Salamander Retina
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{"title"=>"Adaptation to changes in higher-order stimulus statistics in the salamander retina", "type"=>"journal", "authors"=>[{"first_name"=>"Gašper", "last_name"=>"Tkačik", "scopus_author_id"=>"15822947100"}, {"first_name"=>"Anandamohan", "last_name"=>"Ghosh", "scopus_author_id"=>"7403964196"}, {"first_name"=>"Elad", "last_name"=>"Schneidman", "scopus_author_id"=>"6602786935"}, {"first_name"=>"Ronen", "last_name"=>"Segev", "scopus_author_id"=>"7003540984"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "pui"=>"372852813", "doi"=>"10.1371/journal.pone.0085841", "sgr"=>"84907408722", "arxiv"=>"1201.3552", "scopus"=>"2-s2.0-84907408722", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "pmid"=>"24465742"}, "id"=>"2c908def-d447-3705-ad57-3ac41b193c34", "abstract"=>"Adaptation in the retina is thought to optimize the encoding of natural light signals into sequences of spikes sent to the brain. However, adaptation also entails computational costs: adaptive code is intrinsically ambiguous, because output symbols cannot be trivially mapped back to the stimuli without the knowledge of the adaptive state of the encoding neuron. It is thus important to learn which statistical changes in the input do, and which do not, invoke adaptive responses, and ask about the reasons for potential limits to adaptation. We measured the ganglion cell responses in the tiger salamander retina to controlled changes in the second (contrast), third (skew) and fourth (kurtosis) moments of the light intensity distribution of spatially uniform temporally independent stimuli. The skew and kurtosis of the stimuli were chosen to cover the range observed in natural scenes. We quantified adaptation in ganglion cells by studying two-dimensional linear-nonlinear models that capture well the retinal encoding properties across all stimuli. We found that the retinal ganglion cells adapt to contrast, but exhibit remarkably invariant behavior to changes in higher-order statistics. Finally, by theoretically analyzing optimal coding in LN-type models, we showed that the neural code can maintain a high information rate without dynamic adaptation despite changes in stimulus skew and kurtosis.", "link"=>"http://www.mendeley.com/research/adaptation-changes-higherorder-stimulus-statistics-salamander-retina", "reader_count"=>42, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Student > Doctoral Student"=>3, "Researcher"=>9, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>1, "Other"=>1, "Student > Master"=>9, "Student > Bachelor"=>2, "Lecturer"=>2, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>2, "Student > Doctoral Student"=>3, "Researcher"=>9, "Student > Ph. D. Student"=>11, "Student > Postgraduate"=>1, "Other"=>1, "Student > Master"=>9, "Student > Bachelor"=>2, "Lecturer"=>2, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>2, "Nursing and Health Professions"=>1, "Mathematics"=>2, "Agricultural and Biological Sciences"=>10, "Medicine and Dentistry"=>1, "Neuroscience"=>10, "Physics and Astronomy"=>7, "Psychology"=>3, "Chemistry"=>1, "Computer Science"=>3, "Decision Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Neuroscience"=>{"Neuroscience"=>10}, "Chemistry"=>{"Chemistry"=>1}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>7}, "Psychology"=>{"Psychology"=>3}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>10}, "Computer Science"=>{"Computer Science"=>3}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>2}}, "reader_count_by_country"=>{"Austria"=>2, "United States"=>1, "United Kingdom"=>1, "Germany"=>1}, "group_count"=>2}

Scopus | Further Information

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1354271"], "description"=>"<p>HOS stimuli are a mixture of two Gaussian distributions G1 and G2, whose parameters are given in the second and third columns, respectively. The displacement of the mean of the second Gaussian vs the first Gaussian is given in the last column. All units are in lux, and all distributions are matched in mean and have a std of 40 lux. All distributions are 0 outside of the range lux, which are the physical limits of the display device.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Nervous system physiology", "Molecular cell biology", "Cellular types", "neuroscience", "computational neuroscience", "Coding mechanisms", "Sensory systems", "Visual system", "Veterinary medicine", "Veterinary opthalmology", "generating", "hos"], "article_id"=>906654, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Gašper Tkačik", "Anandamohan Ghosh", "Elad Schneidman", "Ronen Segev"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085841.t002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Stimulus_generating_parameters_for_HOS_stimuli_/906654", "title"=>"Stimulus generating parameters for HOS stimuli.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-21 04:25:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1354253"], "description"=>"<p><b>A,B</b>) Two example images from the Penn Natural Image Database <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085841#pone.0085841-Tkaik2\" target=\"_blank\">[32]</a>. The grayscale images are calibrated into units of cd/m<sup>2</sup>. The yellow circle represents the typical size of the salamander retinal ganglion cell center. Luminance was averaged in patches of this size, and contrast (), skewness () and kurtosis () were computed for the distribution over many patches from each image. The distributions for the two example images are shown as insets, and the corresponding values for are displayed in the two image panels. <b>C,D,E</b>) The distribution of contrast, skewness and kurtosis, respectively, over 501 natural images. Colored squares represent the values of the three parameters used in synthetic stimuli (color coded as in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085841#pone-0085841-g001\" target=\"_blank\">Fig 1</a>). 2 cyan stimuli differ in contrast but have constant and ; 4 magenta stimuli differ in skew but have constant values of and ; and 3 yellow stimuli differ in kurtosis but have constant and (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085841#pone-0085841-t001\" target=\"_blank\">Table 1</a>).</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Nervous system physiology", "Molecular cell biology", "Cellular types", "neuroscience", "computational neuroscience", "Coding mechanisms", "Sensory systems", "Visual system", "Veterinary medicine", "Veterinary opthalmology"], "article_id"=>906636, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Gašper Tkačik", "Anandamohan Ghosh", "Elad Schneidman", "Ronen Segev"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085841.g002", "stats"=>{"downloads"=>0, "page_views"=>18, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Higher_order_statistics_in_natural_scenes_/906636", "title"=>"Higher-order statistics in natural scenes.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-21 04:25:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1354258"], "description"=>"<p><b>A</b>) A 2D nonlinearity globally fit across all HOS stimuli for neuron E1C2; projection of the first (second) filter shown on the horizontal (vertical) axis. Hotter colors indicate increased firing rates (see colorbar, rate in Hz; white  =  regions of space where no spike or prior samples have been observed). <b>B</b>) For the same cell, the depiction of prior ensemble (gray dots, all 7 higher-order statistics stimuli overlaid) and the spike-triggered ensembles (magenta  =  skewed stimuli, yellow  =  kurtotic stimuli); shown are also projections of the data, i.e. the marginal distributions and , on the logarithmic scale, for all 7 stimuli separately. <b>C</b>) The segment of predicted and true firing rate in responses to repeated K− stimulus presentations (red  =  2D LN global model fit to all stimuli for this neuron; black  =  true rate). <b>D</b>) Model performance, measured as the Pearson correlation (PC) between the true and predicted PSTH, across different stimuli (horizontal axis; average and error bars  =  mean and interquartile range across 40 neurons in experiment 2). The performance of 2D LN models fit separately for each stimulus is shown by magenta (skewed stimuli) and yellow (kurtotic stimuli) bars. Global model performance (red squares) matches the performance of models fit separately. Right axis, in green: information fraction captured by the nonlinear combination of the 2 linear projections, , shows no drop compared to the information captured by the linear features themselves (c.f. bars in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085841#pone-0085841-g003\" target=\"_blank\">Fig. 3F</a>), and is between across all stimuli (error bars omitted for clarity, comparable to error bars in information fraction captured by the 2 linear features).</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Nervous system physiology", "Molecular cell biology", "Cellular types", "neuroscience", "computational neuroscience", "Coding mechanisms", "Sensory systems", "Visual system", "Veterinary medicine", "Veterinary opthalmology", "higher-order"], "article_id"=>906641, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Gašper Tkačik", "Anandamohan Ghosh", "Elad Schneidman", "Ronen Segev"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085841.g006", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Nonlinearities_and_rate_prediction_with_higher_order_statistics_stimuli_/906641", "title"=>"Nonlinearities and rate prediction with higher-order statistics stimuli.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-21 04:25:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1354256"], "description"=>"<p>The balance index is a ratio between the total (signed) area under the most significant linear filter of each cell, normalized by the absolute area of the filter; balanced filters have , fully unbalanced . Individual dots represent 40 individual cells from experiment 2, which have been grouped into fast-OFF and slow-OFF classes (blue and red, respectively), and a small group of unclassified cells (green). Black symbols show the averages ( std error bars across the recorded population). Inset shows the dependence of the balance index as a function of skewness drawn to scale; the best linear fit (red) is .</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Nervous system physiology", "Molecular cell biology", "Cellular types", "neuroscience", "computational neuroscience", "Coding mechanisms", "Sensory systems", "Visual system", "Veterinary medicine", "Veterinary opthalmology", "dependence"], "article_id"=>906639, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Gašper Tkačik", "Anandamohan Ghosh", "Elad Schneidman", "Ronen Segev"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085841.g004", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_dependence_of_the_balance_index_on_the_stimulus_type_/906639", "title"=>"The dependence of the balance index on the stimulus type.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-21 04:25:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1354257"], "description"=>"<p>Each panel shows the linear filter in four skewed conditions (magenta), three kurtotic conditions (yellow), and the gaussian condition of matched variance (cyan). stimuli are denoted by circles, stimuli by triangles, stimuli by squares, and stimuli by stars. The balance index for 3 selected filters (two extreme skewed distributions and the zero skew condition, G) is reported in each panel.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Nervous system physiology", "Molecular cell biology", "Cellular types", "neuroscience", "computational neuroscience", "Coding mechanisms", "Sensory systems", "Visual system", "Veterinary medicine", "Veterinary opthalmology", "filters", "cells"], "article_id"=>906640, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Gašper Tkačik", "Anandamohan Ghosh", "Elad Schneidman", "Ronen Segev"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085841.g005", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Linear_filters_and_balance_index_for_four_example_cells_in_different_stimulus_conditions_/906640", "title"=>"Linear filters and balance index for four example cells in different stimulus conditions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-21 04:25:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1354255"], "description"=>"<p><b>A</b>) Estimated information rate (see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085841#s2\" target=\"_blank\">Methods</a>) as a function of the mean firing rate. Each dot represents one of the 40 cells in experiment 2 exposed to one of the 7 HOS conditions (skewed stimuli in magenta, kurtotic in yellow). The growth in information is slightly sublinear, with no obvious systematic dependence on the stimulus type. <b>B</b>) A cell whose behavior is captured well by a single linear filter. Shown in light blue are the filters for all 9 (2 Gaussian, 7 HOS) stimuli reconstructed using maximally informative dimensions; in black the spike-triggered average computed on the Gaussian stimulus C++; in red, a single <i>global</i> filter inferred using MID across all stimulus conditions simultaneously. <b>C</b>) Biased STA filter estimates for 5 skewed stimuli (thicker lines mean increasing skewness) for the same cell as in B (note the difference in the time axis). <b>D,E</b>) A cell whose behavior is described well by two linear filters (light blue  =  the most informative dimension; dark blue  =  the second most-informative dimension). Other symbols the same as in B). <b>F</b>) Information captured by two filters (across stimuli, horizontal axis), as a fraction of the total information per spike; mean (bars) and interquantile range error bars across 40 cells in experiment 2. The average performance of global models (the same pair of filters across all stimuli for each cell) is plotted as red squares.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Nervous system physiology", "Molecular cell biology", "Cellular types", "neuroscience", "computational neuroscience", "Coding mechanisms", "Sensory systems", "Visual system", "Veterinary medicine", "Veterinary opthalmology", "filters", "higher-order", "stimuli", "retinal", "ganglion"], "article_id"=>906638, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Gašper Tkačik", "Anandamohan Ghosh", "Elad Schneidman", "Ronen Segev"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085841.g003", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Linear_filters_and_higher_order_statistics_stimuli_in_retinal_ganglion_cells_/906638", "title"=>"Linear filters and higher-order statistics stimuli in retinal ganglion cells.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-21 04:25:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1354252"], "description"=>"<p>The stimuli are spatially uniform with light intensity drawn independently on each stimulus frame from . The probability densities, , for all 9 stimuli used, grouped into 3 categories (cyan  =  Gaussian, magenta  =  skewed, and yellow  =  kurtotic). All stimuli are matched in mean (225 lux), and all except for C+ have the same contrast; for details, see <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085841#pone-0085841-t001\" target=\"_blank\">Table 1</a>.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Nervous system physiology", "Molecular cell biology", "Cellular types", "neuroscience", "computational neuroscience", "Coding mechanisms", "Sensory systems", "Visual system", "Veterinary medicine", "Veterinary opthalmology", "stimuli", "probe", "salamander", "retinal", "ganglion"], "article_id"=>906635, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Gašper Tkačik", "Anandamohan Ghosh", "Elad Schneidman", "Ronen Segev"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085841.g001", "stats"=>{"downloads"=>2, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Synthetic_stimuli_used_to_probe_salamander_retinal_ganglion_cells_/906635", "title"=>"Synthetic stimuli used to probe salamander retinal ganglion cells.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-21 04:25:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1354269"], "description"=>"<p><b>A</b>) The biphasic filter with two parameters determining the amplitudes of the fast and slow lobes, . Each of the amplitudes can be positive or negative. When is positive and is negative, the cell is an OFF cell; when is negative and is positive, the cell is an ON cell. <b>B</b>) Information about the stimulus encoded in the spike train (bits per second, color scale), as a function of the fast lobe amplitude and the slow lobe amplitude . The ON and OFF types have been denoted in the 2 corresponding quadrants of the plot. <b>C</b>) Fraction of entropy lost to noise, , as a function of . Points in the plane that have are shown in white, and lie on a circular locus of points; we are only interested in the models with fixed value of . We parametrize such points by their angle, , going counterclockwise from the vertical (). Magenta dot (here and in B, D) denotes the (OFF) filter that maximizes the transmitted information. <b>D</b>) Information on the locus of points as a function of ; these values are extracted from B) along the contour. Green points correspond to ON cells, cyan points to OFF cells. There are two peaks in information, one (slightly higher) peak for the OFF type cell and one for the ON type cell. For positive skew (S++, not shown for clarity), the results are analogous, with the maximum achieved for ON instead of OFF cells. <b>E</b>) Theoretical prediction for the shape of the optimal biphasic OFF filters for stimuli with different skewness values. As skewness increases from negative (S−−, red) to positive (S++, blue), the negative lobe becomes more prominent and the positive lobe becomes less prominent. For the symmetric Gaussian stimulus (C++, green) the optimal filter is balanced. These changes are quantified by the balance index (see text), which measures the difference in area between the lobes, normalized to the total absolute area under the filter. For the simulations in this figure, the stimulus refresh time is and mean firing rate is held fixed at (results are qualitatively unchanged for rates up to fourfold higher).</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Nervous system physiology", "Molecular cell biology", "Cellular types", "neuroscience", "computational neuroscience", "Coding mechanisms", "Sensory systems", "Visual system", "Veterinary medicine", "Veterinary opthalmology", "optimal", "filters", "ln", "neuron", "stimuli", "skew"], "article_id"=>906652, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Gašper Tkačik", "Anandamohan Ghosh", "Elad Schneidman", "Ronen Segev"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085841.g010", "stats"=>{"downloads"=>0, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Finding_optimal_filters_for_an_LN_model_neuron_for_stimuli_with_negative_skew_S_8722_8722_/906652", "title"=>"Finding optimal filters for an LN model neuron for stimuli with negative skew (S−−).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-21 04:25:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1354264"], "description"=>"<p>Differences between distributions of stimuli before linear filtering, after filtering, and after nonlinear transformation, are quantified by Kullback-Leibler divergence matrix, , measured in bits <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085841#pone.0085841-Cover1\" target=\"_blank\">[56]</a>; while this is not a proper distance metric (since it is not symmetric), a value of 0 indicates identical distributions, and high values signify very different distributions. A) between all 9 pairs of stimulus distributions (cyan  = 2 Gaussian, magenta  = 4 skewed, yellow  = 3 kurtotic distributions). Bimodal K−− distribution is most different from the others. B) The difference between the respective 2D linear projections of the 9 stimulus distributions (shown are the averages over matrices for 19 neurons in experiment 1). Linear filtering of IID stimuli washes out most of the higher-order structure (but not the second order), and the most distinct stimulus type at this stage is C+, since its variance is different from the other distributions of projections. C) (average over 19 neurons) between the nonlinear transformations of the respective linear projections. Since the nonlinearity adapts to contrast, this step equalizes the low contrast (C+) with the other stimuli.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Nervous system physiology", "Molecular cell biology", "Cellular types", "neuroscience", "computational neuroscience", "Coding mechanisms", "Sensory systems", "Visual system", "Veterinary medicine", "Veterinary opthalmology", "adaptation", "distributions", "firing", "rates"], "article_id"=>906647, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Gašper Tkačik", "Anandamohan Ghosh", "Elad Schneidman", "Ronen Segev"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085841.g009", "stats"=>{"downloads"=>0, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Neurons_with_contrast_adaptation_yield_similar_distributions_of_firing_rates_in_response_to_very_different_distributions_of_inputs_/906647", "title"=>"Neurons with contrast adaptation yield similar distributions of firing rates in response to very different distributions of inputs.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-21 04:25:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1354260"], "description"=>"<p><b>A</b>) <i>Inset.</i> The 1D nonlinearity, , for an example neuron (E0C4) inferred at high contrast (C++, light blue), and at low contrast (C+, dark blue). The low contrast nonlinearity can be aligned to the high contrast one by (i) rescaling the stimulus (horizontal) axis by the ratio of the two contrasts, and (ii) rescaling the firing rate (vertical) axis by the ratio of the two average firing rates, yielding the red line. <i>Main panel.</i> Scatter plot of the nonlinearity at high vs nonlinearity at low contrast (black, 19 neurons from experiment 1; the coordinates of each point are the high/low C nonlinearity values at the same value of the projection for a particular neuron). After rescaling, the nonlinearities align (red). The scaling breaks down for rates above (rarely observed at low contrast). <b>B</b>) The information in the spiking pattern of a LN model neuron, normalized by the information captured by the two linear projections of the corresponding stimulus. Cyan circles  =  inferred models for 19 neurons for high and low contrast (C++, C+) stimulus. Green line  =  computational prediction obtained by taking 19 high contrast models and dialing down the stimulus contrast without any adaptation in the model (error bars  =  std across the neurons). <b>C</b>) Analogous analysis for changes in skewness (note the difference in scale); magenta  =  models inferred separately for each skewed stimulus; green  =  invariant (and therefore non-adapting) global models for every cell.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Nervous system physiology", "Molecular cell biology", "Cellular types", "neuroscience", "computational neuroscience", "Coding mechanisms", "Sensory systems", "Visual system", "Veterinary medicine", "Veterinary opthalmology", "benefits", "higher-order"], "article_id"=>906643, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Gašper Tkačik", "Anandamohan Ghosh", "Elad Schneidman", "Ronen Segev"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085841.g008", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_benefits_of_contrast_and_higher_order_statistics_adaptation_/906643", "title"=>"The benefits of contrast and higher-order statistics adaptation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-21 04:25:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1354259"], "description"=>"<p><b>A</b>) The relative change (color scale, 1  =  the firing rate of the cell is equal to the mean rate for that cell across all stimuli) in the mean firing rate for 40 cells of experiment 2, as a function of the stimulus condition (magenta  =  4 skewed, yellow  =  3 kurtotic stimuli). Neurons (rows) were sorted by the projection on the first principal component explaining most of the change across the recorded population; cells close to the top increase the firing rate in response to left-skewed stimuli, while cells at the bottom increase the rate in response to right-skewed and negative kurtosis stimuli. <b>B</b>) Global 2D models for each cell predict the average rate well (each dot is one cell in one of the 7 HOS stimulus conditions).</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Nervous system physiology", "Molecular cell biology", "Cellular types", "neuroscience", "computational neuroscience", "Coding mechanisms", "Sensory systems", "Visual system", "Veterinary medicine", "Veterinary opthalmology", "firing"], "article_id"=>906642, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Gašper Tkačik", "Anandamohan Ghosh", "Elad Schneidman", "Ronen Segev"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085841.g007", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Measurement_and_prediction_of_changes_in_the_mean_firing_rate_with_stimulus_condition_/906642", "title"=>"Measurement and prediction of changes in the mean firing rate with stimulus condition.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-21 04:25:14"}
  • {"files"=>["https://ndownloader.figshare.com/files/1354272"], "description"=>"<p>The shorthand symbol for the stimulus starts with the C/S/K (for contrast, skew, kurtosis) and is followed by −,−−,+,++ (small magnitude and negative, large magnitude and negative, small magnitude and positive, large magnitude and positive); therefore, C+,C++,S−−,S−,S+,S++,K−−,K−,K+. Parameters in the table denoted in bold were varied in each of the three stimulus categories.</p>", "links"=>[], "tags"=>["Anatomy and physiology", "Neurological system", "Nervous system physiology", "Molecular cell biology", "Cellular types", "neuroscience", "computational neuroscience", "Coding mechanisms", "Sensory systems", "Visual system", "Veterinary medicine", "Veterinary opthalmology"], "article_id"=>906655, "categories"=>["Biological Sciences", "Medicine"], "users"=>["Gašper Tkačik", "Anandamohan Ghosh", "Elad Schneidman", "Ronen Segev"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085841.t001", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Stimuli_used_in_the_experiment_see_main_text_for_the_definition_of_the_statistics_/906655", "title"=>"Stimuli used in the experiment (see main text for the definition of the statistics ).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-21 04:25:14"}

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