Automatic Adaptation to Fast Input Changes in a Time-Invariant Neural Circuit
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{"title"=>"Automatic Adaptation to Fast Input Changes in a Time-Invariant Neural Circuit", "type"=>"journal", "authors"=>[{"first_name"=>"Arjun", "last_name"=>"Bharioke", "scopus_author_id"=>"24528283800"}, {"first_name"=>"Dmitri B.", "last_name"=>"Chklovskii", "scopus_author_id"=>"6701366975"}], "year"=>2015, "source"=>"PLoS Computational Biology", "identifiers"=>{"doi"=>"10.1371/journal.pcbi.1004315", "sgr"=>"84940741378", "pmid"=>"26247884", "issn"=>"15537358", "scopus"=>"2-s2.0-84940741378", "pui"=>"605889659"}, "id"=>"69d87456-587a-31c6-a9b3-62a031b7134b", "abstract"=>"Neurons must faithfully encode signals that can vary over many orders of magnitude despite having only limited dynamic ranges. For a correlated signal, this dynamic range constraint can be relieved by subtracting away components of the signal that can be predicted from the past, a strategy known as predictive coding and relying on learning the input statistics. However, the statistics of input natural signals can also vary over very short time scales e.g. following saccades across a visual scene. To maintain a reduced transmission cost to signals with rapidly varying statistics, neuronal circuits implementing predictive coding must also rapidly adapt their properties. Experimentally, in different sensory modalities, sensory neurons have shown such adaptations within 100 ms of an input change. Here, we show first that linear neurons connected in a feedback inhibitory circuit can implement predictive coding. We then show that adding a rectification nonlinearity to such a feedback inhibitory circuit allows it to automatically adapt and approximate the performance of an optimal linear predictive coding network, over a wide range of inputs, while keeping its underlying temporal and synaptic properties unchanged. We demonstrate that the resulting changes to the linearized temporal filters of this nonlinear network match the fast adaptations observed experimentally in different sensory modalities, in different vertebrate species. Therefore, the nonlinear feedback inhibitory network can provide automatic adaptation to fast varying signals, maintaining the dynamic range necessary for accurate neuronal transmission of natural inputs.", "link"=>"http://www.mendeley.com/research/automatic-adaptation-fast-input-changes-timeinvariant-neural-circuit", "reader_count"=>22, "reader_count_by_academic_status"=>{"Researcher"=>8, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>1, "Student > Master"=>1, "Student > Bachelor"=>3, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Researcher"=>8, "Student > Ph. D. Student"=>7, "Student > Postgraduate"=>1, "Student > Master"=>1, "Student > Bachelor"=>3, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Biochemistry, Genetics and Molecular Biology"=>2, "Mathematics"=>1, "Agricultural and Biological Sciences"=>7, "Medicine and Dentistry"=>1, "Neuroscience"=>7, "Physics and Astronomy"=>1, "Computer Science"=>1}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Neuroscience"=>{"Neuroscience"=>7}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Mathematics"=>{"Mathematics"=>1}}, "reader_count_by_country"=>{"United States"=>1, "Germany"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2204104"], "description"=>"<p>Feedforward (a) and feedback (b) predictive coding circuits (as in Eq (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004315#pcbi.1004315.e001\" target=\"_blank\">1</a>)), showing the circuit structure necessary for reconstructing the input. (c) Lossless reconstruction (black dots) of an input (blue) from the transmitted output of a predictive coding circuit (red). The mean power up to each point in time (solid bars) for the input (blue) and transmission (red) demonstrates the reduction in the mean transmitted power via predictive coding.</p>", "links"=>[], "tags"=>["input statistics", "encode signals", "adaptation", "rectification nonlinearity", "transmission cost", "Automatic Adaptation", "100 ms", "nonlinear feedback", "coding network", "Fast Input Changes", "range constraint", "synaptic properties", "circuit", "input change", "nonlinear network"], "article_id"=>1504721, "categories"=>["Biological Sciences"], "users"=>["Arjun Bharioke", "Dmitri B. Chklovskii"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004315.g001", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Lossless_compression_via_predictive_coding_/1504721", "title"=>"Lossless compression via predictive coding.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-06 03:51:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2204105"], "description"=>"<p>(a) <i>β</i> = <i>β</i>(<i>τ</i><sub><i>s</i></sub>) (b) Λ<sup>*</sup> = Λ<sup>*</sup>(<i>β</i>, <i>σ</i>): Eq (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004315#pcbi.1004315.e006\" target=\"_blank\">6</a>) for two values of <i>β</i>.</p>", "links"=>[], "tags"=>["input statistics", "encode signals", "adaptation", "rectification nonlinearity", "transmission cost", "Automatic Adaptation", "100 ms", "nonlinear feedback", "coding network", "Fast Input Changes", "range constraint", "synaptic properties", "circuit", "input change", "nonlinear network"], "article_id"=>1504722, "categories"=>["Biological Sciences"], "users"=>["Arjun Bharioke", "Dmitri B. Chklovskii"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004315.g002", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Parameters_of_the_optimized_linear_predictive_coding_algorithm_/1504722", "title"=>"Parameters of the optimized linear predictive coding algorithm.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-06 03:51:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2204106"], "description"=>"<p>Networks’ parameters are: (a) Feedforward: </p><p></p><p></p><p><mi>α</mi><mo>^</mo></p><p></p><p></p>, <p></p><p></p><p><mi>Γ</mi><mo>^</mo></p><p></p><p></p>. (b) Feedback: <i>α</i>, Γ. In both circuits, the unit that outputs <i>p</i><sub><i>t</i></sub> is termed the <i>principal cell</i>, and the unit that outputs <i>n</i><sub><i>t</i></sub> the <i>interneuron</i>. There are two synapses onto each interneuron: (1) its external input and (2) a representation of its internal dynamics (right of the dashed line). We can describe its internal dynamics in this way because the interneuron is a leaky integrator, i.e. its internal dynamics (Eq (<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004315#pcbi.1004315.e008\" target=\"_blank\">8</a>)) are an infinite sum of past outputs, discounted at each time step by a fixed multiplier. Therefore, they can be represented by a delayed input across a synapse, with a multiplicative discounting factor, as in (a) and (b).<p></p>", "links"=>[], "tags"=>["input statistics", "encode signals", "adaptation", "rectification nonlinearity", "transmission cost", "Automatic Adaptation", "100 ms", "nonlinear feedback", "coding network", "Fast Input Changes", "range constraint", "synaptic properties", "circuit", "input change", "nonlinear network"], "article_id"=>1504723, "categories"=>["Biological Sciences"], "users"=>["Arjun Bharioke", "Dmitri B. Chklovskii"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004315.g003", "stats"=>{"downloads"=>1, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Schematic_neural_networks_implementing_predictive_coding_through_feedforward_a_or_feedback_b_inhibition_/1504723", "title"=>"Schematic neural networks implementing predictive coding, through feedforward (a) or feedback (b) inhibition.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-06 03:51:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2204107"], "description"=>"<p>(a) Rectification nonlinearity (black), with threshold at v = 1. Linearized responses in color (cyan: min; magenta: max). (b) Nonlinear feedback inhibitory network. The nonlinearity (inset) is applied to the interneuron’s output. Nonlinearities with increasing thresholds are colored (green: min; red: max) (Methods). (c) Network gain at different input frequencies (note: the frequency space is in Z-space, i.e. defined with respect to the fixed time step of the network). The three colored curves are the nonlinear network response curves (computed using describing function analysis, colored as in (b)) (Methods). The dotted lines provide extreme parameter values for the linear network: Γ = 0,1.</p>", "links"=>[], "tags"=>["input statistics", "encode signals", "adaptation", "rectification nonlinearity", "transmission cost", "Automatic Adaptation", "100 ms", "nonlinear feedback", "coding network", "Fast Input Changes", "range constraint", "synaptic properties", "circuit", "input change", "nonlinear network"], "article_id"=>1504724, "categories"=>["Biological Sciences"], "users"=>["Arjun Bharioke", "Dmitri B. Chklovskii"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004315.g004", "stats"=>{"downloads"=>2, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Rectification_nonlinearity_and_its_effect_on_the_feedback_inhibitory_circuit_/1504724", "title"=>"Rectification nonlinearity and its effect on the feedback inhibitory circuit.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-06 03:51:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2204108"], "description"=>"<p>(a) Two input mixtures, modeling rapid transition from predictable to unpredictable input components. (b) Description of simulations. Inputs constructed as in (a). At each time point, inputs are either pure predictable signal, or pure unpredictable noise, with an instantaneous transition from one type to the other, in the middle of the simulation period. (c-f) Simulation outputs (inputs shown in inset). The amplitude of the unpredictable component of the mixture varies along the x axis. Error bars are 1 std. dev. (c,e) Network gain of the linear network of type 1, optimized to the mixture (blue, non-adapted linear response), is significantly higher than that of the nonlinear network (red). In contrast, the nonlinear network gain is close to the response of the optimal linear network of type 2, which is allowed to adapt to each component of the mixture (dotted black, adapted linear response). (e) Green shading indicates region where the nonlinear response is more than one std. dev. lower than the non-adapted linear response. Diagonal hashing indicates region where the nonlinear response is within one std. dev. of the adapted linear response. (d,f) % improvement of the performance of the nonlinear network over the type 1 linear network at different amplitudes of the unpredictable component. Data taken from (c) and (e) respectively. (f) Green box indicates region where the improvement is more than one std. dev. different from 0.</p>", "links"=>[], "tags"=>["input statistics", "encode signals", "adaptation", "rectification nonlinearity", "transmission cost", "Automatic Adaptation", "100 ms", "nonlinear feedback", "coding network", "Fast Input Changes", "range constraint", "synaptic properties", "circuit", "input change", "nonlinear network"], "article_id"=>1504725, "categories"=>["Biological Sciences"], "users"=>["Arjun Bharioke", "Dmitri B. Chklovskii"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004315.g005", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparing_the_performance_measured_through_network_gain_of_nonlinear_and_linear_feedback_inhibitory_networks_measured_through_network_gain_lower_is_better_details_in_Methods_/1504725", "title"=>"Comparing the performance (measured through network gain) of nonlinear and linear feedback inhibitory networks (measured through network gain: lower is better) (details in Methods).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-06 03:51:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2204109"], "description"=>"<p>(a,b) Adapted from [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004315#pcbi.1004315.ref027\" target=\"_blank\">27</a>]. (a) Timeline of experiment. (b) Experimentally measured filters (low contrast, blue; high contrast, red). High contrast filter is computed only from the response to the first 200 ms of high contrast stimulus (see timeline in (a)). (c) Simulated response filters of the principal cell of the nonlinear predictive coding circuit (Methods), showing oscillatory structure.</p>", "links"=>[], "tags"=>["input statistics", "encode signals", "adaptation", "rectification nonlinearity", "transmission cost", "Automatic Adaptation", "100 ms", "nonlinear feedback", "coding network", "Fast Input Changes", "range constraint", "synaptic properties", "circuit", "input change", "nonlinear network"], "article_id"=>1504726, "categories"=>["Biological Sciences"], "users"=>["Arjun Bharioke", "Dmitri B. Chklovskii"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004315.g006", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Linear_filters_from_spike_triggered_correlation_analyses_estimated_at_different_times_around_a_sudden_shift_from_low_L_to_high_H_contrast_/1504726", "title"=>"Linear filters from spike-triggered correlation analyses, estimated at different times around a sudden shift from low (L) to high (H) contrast.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-06 03:51:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2204110"], "description"=>"<p>(a-c) Adapted from [<a href=\"http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004315#pcbi.1004315.ref028\" target=\"_blank\">28</a>]. (a) Linear response filters for a single neuron stimulated with inputs of different mean sound amplitudes (colored to match (d)). (b) Ratio of the total positive to total negative component of neuronal filters is computed for an input with high mean amplitude, and an input with low mean amplitude. Each circle shows these values for a different recorded neuron (one example being (a)). The colored circle is derived from the simulated response filters of the principal cell of the nonlinear circuit (d). It uses the ratio of the total positive to total negative component of the simulated filters (plotted in (e)) at the highest input amplitude (red), and the lowest input amplitude (cyan). (c) The BMF (freq. of the peak of the Fourier transform of the linear response filters) for a high mean input, and a low mean input (for different neurons, one example being (a)). The colored circle is derived from the simulated responses of the principal cell of the nonlinear network (d). It uses the BMFs (shown in (f)) of the simulated filters at the highest input amplitude (red) and the lowest input amplitude (cyan). (d) Linear filters estimated to best approximate the response of the principal cell of the nonlinear circuit to white noise stimuli of different amplitudes (Methods). Curves colored to match (a). (e) The ratio of the total positive component of the curves in (d) to the total negative component. Points are colored as in (a,d). The ratios derived from inputs with highest (red) and lowest amplitude (cyan) define the colored circle in (b). (f) The BMF of the responses filters from (d). The BMF values derived from inputs with highest (red) and lowest amplitude (cyan) define the colored circle in (c).</p>", "links"=>[], "tags"=>["input statistics", "encode signals", "adaptation", "rectification nonlinearity", "transmission cost", "Automatic Adaptation", "100 ms", "nonlinear feedback", "coding network", "Fast Input Changes", "range constraint", "synaptic properties", "circuit", "input change", "nonlinear network"], "article_id"=>1504727, "categories"=>["Biological Sciences"], "users"=>["Arjun Bharioke", "Dmitri B. Chklovskii"], "doi"=>"https://dx.doi.org/10.1371/journal.pcbi.1004315.g007", "stats"=>{"downloads"=>1, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Response_filters_of_zebra_finch_auditory_neurons_a_adapted_from_28_compared_to_theoretically_simulated_response_filters_of_a_nonlinear_feedback_inhibitory_circuit_d_/1504727", "title"=>"Response filters of zebra finch auditory neurons ((a), adapted from [28]) compared to theoretically simulated response filters of a nonlinear feedback inhibitory circuit (d).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-06 03:51:42"}
  • {"files"=>["https://ndownloader.figshare.com/files/2204111", "https://ndownloader.figshare.com/files/2204112", "https://ndownloader.figshare.com/files/2204113", "https://ndownloader.figshare.com/files/2204114", "https://ndownloader.figshare.com/files/2204115", "https://ndownloader.figshare.com/files/2204116"], "description"=>"<div><p>Neurons must faithfully encode signals that can vary over many orders of magnitude despite having only limited dynamic ranges. For a correlated signal, this dynamic range constraint can be relieved by subtracting away components of the signal that can be predicted from the past, a strategy known as predictive coding, that relies on learning the input statistics. However, the statistics of input natural signals can also vary over very short time scales e.g., following saccades across a visual scene. To maintain a reduced transmission cost to signals with rapidly varying statistics, neuronal circuits implementing predictive coding must also rapidly adapt their properties. Experimentally, in different sensory modalities, sensory neurons have shown such adaptations within 100 ms of an input change. Here, we show first that linear neurons connected in a feedback inhibitory circuit can implement predictive coding. We then show that adding a rectification nonlinearity to such a feedback inhibitory circuit allows it to automatically adapt and approximate the performance of an optimal linear predictive coding network, over a wide range of inputs, while keeping its underlying temporal and synaptic properties unchanged. We demonstrate that the resulting changes to the linearized temporal filters of this nonlinear network match the fast adaptations observed experimentally in different sensory modalities, in different vertebrate species. Therefore, the nonlinear feedback inhibitory network can provide automatic adaptation to fast varying signals, maintaining the dynamic range necessary for accurate neuronal transmission of natural inputs.</p></div>", "links"=>[], "tags"=>["input statistics", "encode signals", "adaptation", "rectification nonlinearity", "transmission cost", "Automatic Adaptation", "100 ms", "nonlinear feedback", "coding network", "Fast Input Changes", "range constraint", "synaptic properties", "circuit", "input change", "nonlinear network"], "article_id"=>1504728, "categories"=>["Biological Sciences"], "users"=>["Arjun Bharioke", "Dmitri B. Chklovskii"], "doi"=>["https://dx.doi.org/10.1371/journal.pcbi.1004315.s001", "https://dx.doi.org/10.1371/journal.pcbi.1004315.s002", "https://dx.doi.org/10.1371/journal.pcbi.1004315.s003", "https://dx.doi.org/10.1371/journal.pcbi.1004315.s004", "https://dx.doi.org/10.1371/journal.pcbi.1004315.s005", "https://dx.doi.org/10.1371/journal.pcbi.1004315.s006"], "stats"=>{"downloads"=>5, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Automatic_Adaptation_to_Fast_Input_Changes_in_a_Time_Invariant_Neural_Circuit_/1504728", "title"=>"Automatic Adaptation to Fast Input Changes in a Time-Invariant Neural Circuit", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-08-06 03:51:42"}

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{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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