Sensory Adaptation and Short Term Plasticity as Bayesian Correction for a Changing Brain
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{"title"=>"Sensory adaptation and short term plasticity as bayesian correction for a changing brain", "type"=>"journal", "authors"=>[{"first_name"=>"Ian H.", "last_name"=>"Stevenson", "scopus_author_id"=>"35304267900"}, {"first_name"=>"Beau", "last_name"=>"Cronin", "scopus_author_id"=>"7007102641"}, {"first_name"=>"Mriganka", "last_name"=>"Sur", "scopus_author_id"=>"7006960386"}, {"first_name"=>"Konrad P.", "last_name"=>"Kording", "scopus_author_id"=>"6603812799"}], "year"=>2010, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"359754371", "sgr"=>"77957858200", "issn"=>"19326203", "pmid"=>"20865056", "scopus"=>"2-s2.0-77957858200", "doi"=>"10.1371/journal.pone.0012436", "isbn"=>"1932-6203"}, "id"=>"2bb57fb2-c395-3b82-be2a-7a0496f7a3c9", "abstract"=>"Neurons in the sensory system exhibit changes in excitability that unfold over many time scales. These fluctuations produce noise and could potentially lead to perceptual errors. However, to prevent such errors, postsynaptic neurons and synapses can adapt and counteract changes in the excitability of presynaptic neurons. Here we ask how neurons could optimally adapt to minimize the influence of changing presynaptic neural properties on their outputs. The resulting model, based on Bayesian inference, explains a range of physiological results from experiments which have measured the overall properties and detailed time-course of sensory tuning curve adaptation in the early visual cortex. We show how several experimentally measured short term plasticity phenomena can be understood as near-optimal solutions to this adaptation problem. This framework provides a link between high level computational problems, the properties of cortical neurons, and synaptic physiology.", "link"=>"http://www.mendeley.com/research/sensory-adaptation-short-term-plasticity-bayesian-correction-changing-brain", "reader_count"=>88, "reader_count_by_academic_status"=>{"Unspecified"=>4, "Professor > Associate Professor"=>5, "Researcher"=>27, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>28, "Student > Postgraduate"=>2, "Student > Master"=>9, "Other"=>1, "Student > Bachelor"=>2, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>4, "Professor > Associate Professor"=>5, "Researcher"=>27, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>28, "Student > Postgraduate"=>2, "Student > Master"=>9, "Other"=>1, "Student > Bachelor"=>2, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_subject_area"=>{"Engineering"=>8, "Unspecified"=>6, "Mathematics"=>2, "Agricultural and Biological Sciences"=>20, "Medicine and Dentistry"=>5, "Neuroscience"=>11, "Physics and Astronomy"=>1, "Psychology"=>22, "Social Sciences"=>2, "Computer Science"=>11}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>8}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Neuroscience"=>{"Neuroscience"=>11}, "Social Sciences"=>{"Social Sciences"=>2}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>22}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>20}, "Computer Science"=>{"Computer Science"=>11}, "Mathematics"=>{"Mathematics"=>2}, "Unspecified"=>{"Unspecified"=>6}}, "reader_count_by_country"=>{"Canada"=>1, "Netherlands"=>1, "Belgium"=>1, "United States"=>5, "Denmark"=>1, "United Kingdom"=>3, "Israel"=>1, "France"=>1, "Germany"=>3, "Spain"=>1}, "group_count"=>4}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/832658"], "description"=>"<p>A) Examples of fluctuations in excitability on multiple timescales. We assume that excitability fluctuates due to multiple causes – both slowly fluctuating (e.g. oxygen concentration) and quickly fluctuating (e.g. the activities of neighboring neurons). B) Example of sensory drive. We assume that sensory drive is sparse – non-zero values are relatively rare – and independent from one time-step to the next. C) A schematic depicting the relationship between the excitability, sensory drive, and presynaptic activity. At each time presynaptic activity s is observed. We assume that this activity is the product of the two hidden variables: sensory drive d and excitability (gain) g. Here we assume that the excitability at each time depends on the excitability at the previous time-step. Using this model we can estimate excitability given observations of presynaptic activity, and subsequently normalize postsynaptic responses by this excitability to produce a more stable output.</p>", "links"=>[], "tags"=>["excitability", "estimation"], "article_id"=>503026, "categories"=>["Neuroscience", "Medicine"], "users"=>["Ian H. Stevenson", "Beau Cronin", "Mriganka Sur", "Konrad P. Körding"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0012436.g001", "stats"=>{"downloads"=>0, "page_views"=>1, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_An_excitability_estimation_model_/503026", "title"=>"An excitability estimation model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-26 00:50:26"}
  • {"files"=>["https://ndownloader.figshare.com/files/832930"], "description"=>"<p>Control and adapted tuning curves from two real example neurons, adapted from Dragoi et al. (2000) (top). Control and adapted tuning curves from two model neurons whose initial tuning weights were modified to match the electrophysiological data (bottom). In these two cases, the network tuning parameters are slightly different, but the estimation model and parameters are the same.</p>", "links"=>[], "tags"=>["tuning", "adaptation"], "article_id"=>503299, "categories"=>["Neuroscience", "Medicine"], "users"=>["Ian H. Stevenson", "Beau Cronin", "Mriganka Sur", "Konrad P. Körding"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0012436.g005", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_with_tuning_curve_adaptation_data_/503299", "title"=>"Comparison with tuning curve adaptation data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-26 00:54:59"}
  • {"files"=>["https://ndownloader.figshare.com/files/832836"], "description"=>"<p>A) The stimuli presented during the control (upper) and adaptation (lower) epochs of the sensory adaptation simulations; these are meant to replicate the stimuli used in Dragoi et al. B) A schematic of the network model used to simulate orientation adaptation. A population of presynaptic neurons, each with its own orientation tuning curve, reacts to the stimulus. These presynaptic activities are then modulated by the presynaptic excitability, weighted, and summed to produce the postsynaptic response. While the synaptic weights remain constant, the excitability estimates are updated over time according to the model. C) The time course of two sets of gains during a period of adaptation to a single stimulus. The gains correspond to presynaptic inputs whose preferred orientations are on the near (blue) and far (yellow) flanks of the control tuning curve. The dashed lines indicate the time points at which the control tuning curve was measured (black), as well as three successive adapted tuning curves (red, gold, and green). D) The control tuning curve (black), as well as three successive adapted tuning curves corresponding to the time points indicated in A.</p>", "links"=>[], "tags"=>["repulsive", "tuning"], "article_id"=>503208, "categories"=>["Neuroscience", "Medicine"], "users"=>["Ian H. Stevenson", "Beau Cronin", "Mriganka Sur", "Konrad P. Körding"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0012436.g004", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Simulation_of_repulsive_tuning_curve_adaptation_/503208", "title"=>"Simulation of repulsive tuning curve adaptation.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-26 00:53:28"}
  • {"files"=>["https://ndownloader.figshare.com/files/415059", "https://ndownloader.figshare.com/files/415072", "https://ndownloader.figshare.com/files/415087", "https://ndownloader.figshare.com/files/415092"], "description"=>"<div><p>Neurons in the sensory system exhibit changes in excitability that unfold over many time scales. These fluctuations produce noise and could potentially lead to perceptual errors. However, to prevent such errors, postsynaptic neurons and synapses can adapt and counteract changes in the excitability of presynaptic neurons. Here we ask how neurons could optimally adapt to minimize the influence of changing presynaptic neural properties on their outputs. The resulting model, based on Bayesian inference, explains a range of physiological results from experiments which have measured the overall properties and detailed time-course of sensory tuning curve adaptation in the early visual cortex. We show how several experimentally measured short term plasticity phenomena can be understood as near-optimal solutions to this adaptation problem. This framework provides a link between high level computational problems, the properties of cortical neurons, and synaptic physiology.</p></div>", "links"=>[], "tags"=>["sensory", "adaptation", "plasticity", "bayesian", "correction"], "article_id"=>141961, "categories"=>["Neuroscience", "Medicine"], "users"=>["Ian H. Stevenson", "Beau Cronin", "Mriganka Sur", "Konrad P. Körding"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0012436.s001", "https://dx.doi.org/10.1371/journal.pone.0012436.s002", "https://dx.doi.org/10.1371/journal.pone.0012436.s003", "https://dx.doi.org/10.1371/journal.pone.0012436.s004"], "stats"=>{"downloads"=>20, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Sensory_Adaptation_and_Short_Term_Plasticity_as_Bayesian_Correction_for_a_Changing_Brain/141961", "title"=>"Sensory Adaptation and Short Term Plasticity as Bayesian Correction for a Changing Brain", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2010-08-26 00:32:41"}
  • {"files"=>["https://ndownloader.figshare.com/files/832786"], "description"=>"<p>A) Low-pass filtering with short-term synaptic depression. Steady-state EPSC size (as a fraction of control) for real data, adapted from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0012436#pone.0012436-Dittman1\" target=\"_blank\">[16]</a>, (top) and the steady-state gain from the excitability estimation model as the input is varied (bottom). B) A comparison of the EPSC magnitudes predicted by the Tsodyks and Markram model (black +/− SEM, averaged over many spike-train simulations) with the gains predicted by our excitability estimation model (blue). The model proposed by Tsodyks & Markram (1997) allows for a closed-form calculation of successive EPSC magnitudes, given a set of presynaptic spikes. The close match indicates that the changes in average EPSC magnitude are consistent with those that would be produced by a mechanism in service of excitability estimation.</p>", "links"=>[], "tags"=>["synaptic"], "article_id"=>503155, "categories"=>["Neuroscience", "Medicine"], "users"=>["Ian H. Stevenson", "Beau Cronin", "Mriganka Sur", "Konrad P. Körding"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0012436.g003", "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_with_short_term_synaptic_depression_data_and_previous_model_/503155", "title"=>"Comparison with short-term synaptic depression data and previous model.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-26 00:52:35"}
  • {"files"=>["https://ndownloader.figshare.com/files/832720"], "description"=>"<p>A simulated neuron receives input from an orientation tuned neuron whose excitability is fluctuating. A) Estimated pre-synaptic fluctuations on three timescales (slow tau = 5 min, intermediate tau = 500 ms, and fast tau = 50 ms). B) The total pre-synaptic gain in this simulation and the optimal estimate given noisy observations of the pre-synaptic activity. C) The post-synaptic response – presynaptic activity normalized by the estimated gain – to a single orientation with and without adaptation. Boxes denote the inter-quartile range; whiskers denote 1.5 times the inter-quartile range. Outliers have been removed for clarity. By cancelling out fluctuations in pre-synaptic excitability the adaptation model can substantially reduce response variability.</p>", "links"=>[], "tags"=>["computational biology/computational neuroscience", "neuroscience/sensory systems", "neuroscience/theoretical neuroscience"], "article_id"=>503091, "categories"=>["Neuroscience", "Medicine"], "users"=>["Ian H. Stevenson", "Beau Cronin", "Mriganka Sur", "Konrad P. Körding"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0012436.g002", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Reducing_response_variability_/503091", "title"=>"Reducing response variability.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2010-08-26 00:51:31"}

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