AHaH Computing–From Metastable Switches to Attractors to Machine Learning
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{"title"=>"AHaH computing-from metastable switches to attractors to machine learning", "type"=>"journal", "authors"=>[{"first_name"=>"Michael Alexander", "last_name"=>"Nugent", "scopus_author_id"=>"24178659900"}, {"first_name"=>"Timothy Wesley", "last_name"=>"Molter", "scopus_author_id"=>"57189096149"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"372537573", "isbn"=>"1932-6203 (Electronic) 1932-6203 (Linking)", "issn"=>"19326203", "sgr"=>"84895511946", "scopus"=>"2-s2.0-84895511946", "pmid"=>"24520315", "doi"=>"10.1371/journal.pone.0085175"}, "id"=>"de6ddc94-a099-3ae8-b36d-6fe159d9caff", "abstract"=>"Modern computing architecture based on the separation of memory and processing leads to a well known problem called the von Neumann bottleneck, a restrictive limit on the data bandwidth between CPU and RAM. This paper introduces a new approach to computing we call AHaH computing where memory and processing are combined. The idea is based on the attractor dynamics of volatile dissipative electronics inspired by biological systems, presenting an attractive alternative architecture that is able to adapt, self-repair, and learn from interactions with the environment. We envision that both von Neumann and AHaH computing architectures will operate together on the same machine, but that the AHaH computing processor may reduce the power consumption and processing time for certain adaptive learning tasks by orders of magnitude. The paper begins by drawing a connection between the properties of volatility, thermodynamics, and Anti-Hebbian and Hebbian (AHaH) plasticity. We show how AHaH synaptic plasticity leads to attractor states that extract the independent components of applied data streams and how they form a computationally complete set of logic functions. After introducing a general memristive device model based on collections of metastable switches, we show how adaptive synaptic weights can be formed from differential pairs of incremental memristors. We also disclose how arrays of synaptic weights can be used to build a neural node circuit operating AHaH plasticity. By configuring the attractor states of the AHaH node in different ways, high level machine learning functions are demonstrated. This includes unsupervised clustering, supervised and unsupervised classification, complex signal prediction, unsupervised robotic actuation and combinatorial optimization of procedures-all key capabilities of biological nervous systems and modern machine learning algorithms with real world application.", "link"=>"http://www.mendeley.com/research/ahah-computingfrom-metastable-switches-attractors-machine-learning", "reader_count"=>65, "reader_count_by_academic_status"=>{"Professor > Associate Professor"=>5, "Researcher"=>17, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>17, "Other"=>7, "Student > Master"=>7, "Student > Bachelor"=>5, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Professor > Associate Professor"=>5, "Researcher"=>17, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>17, "Other"=>7, "Student > Master"=>7, "Student > Bachelor"=>5, "Lecturer"=>1, "Lecturer > Senior Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Agricultural and Biological Sciences"=>4, "Philosophy"=>1, "Chemistry"=>1, "Computer Science"=>16, "Earth and Planetary Sciences"=>2, "Engineering"=>25, "Materials Science"=>2, "Mathematics"=>2, "Medicine and Dentistry"=>4, "Design"=>1, "Sports and Recreations"=>1, "Physics and Astronomy"=>5, "Psychology"=>1}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>4}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>5}, "Psychology"=>{"Psychology"=>1}, "Mathematics"=>{"Mathematics"=>2}, "Design"=>{"Design"=>1}, "Engineering"=>{"Engineering"=>25}, "Chemistry"=>{"Chemistry"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>4}, "Computer Science"=>{"Computer Science"=>16}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"Turkey"=>1, "Belgium"=>1, "United States"=>4, "Luxembourg"=>1, "Finland"=>1, "United Kingdom"=>1, "Germany"=>1, "Russia"=>2}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1380136"], "description"=>"<p>A) Reuters-21578. Using the top ten most frequent labels associated with the news articles in the Reuters-21578 data set, the AHaH classifier’s accuracy, precision, recall, and F1 score was determined as a function of its confidence threshold. As the confidence threshold increases, the precision increases while recall drops. An optimal confidence threshold can be chosen depending on the desired results and can be dynamically changed. The peak F1 score is 0.92. B) Census Income. The peak F1 score is 0.853 C) Breast Cancer. The peak F1 score is 0.997. D) Breast Cancer repeated but using the circuit model rather than the functional model. The peak F1 score and the shape of the curves are similar to functional model results. E) MNIST. The peak F1 score is 0.98–.99, depending on the resolution of the spike encoding. F) The individual F1 classification scores of the hand written digits.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "benchmarks"], "article_id"=>929374, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g016", "stats"=>{"downloads"=>3, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Classification_benchmarks_results_/929374", "title"=>"Classification benchmarks results.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380133"], "description"=>"<p>The AHaH clusterer performs well across a wide range of different 2D spatial cluster types, all without predefining the number of clusters or the expected cluster types. A) Gaussian B) non-Gaussian C) random Gaussian size and placement.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "spatial", "clustering"], "article_id"=>929371, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g015", "stats"=>{"downloads"=>1, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Two_dimensional_spatial_clustering_demonstrations_/929371", "title"=>"Two-dimensional spatial clustering demonstrations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380112"], "description"=>"<p>The circuit produces an analog voltage signal on the output at node y given a spike pattern on its inputs labeled , , . The bias inputs , , are equivalent to the spike pattern inputs except that they are always active when the spike pattern inputs are active. F is a voltage source used to implement supervised and unsupervised learning via the AHaH rule. The polarity of the memristors for the bias synapse(s) is inverted relative to the input memristors. The output voltage, , contains both state (positive/negative) and confidence (magnitude) information.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "2-1", "two-phase", "circuit"], "article_id"=>929351, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g005", "stats"=>{"downloads"=>1, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_AHaH_2_1_two_phase_circuit_diagram_/929351", "title"=>"AHaH 2-1 two-phase circuit diagram.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380122"], "description"=>"<p>Multiple AHaH nodes receive spike patterns from the set while the weight and weight conjugate is measured. Blue = weight conjugate (), Red = weight (). The quantity has a much lower variance than the quantity over multiple trials, justifying the assumption that is a constant factor.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics"], "article_id"=>929361, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g011", "stats"=>{"downloads"=>1, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Justification_of_constant_weight_conjugate_/929361", "title"=>"Justification of constant weight conjugate.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380152"], "description"=>"<p>Both input and bias memristors are updated during one read/write cycle. During the read phase the active input memristors increase in conductance (accumulate) while the bias memristors decrease in conductance (decay). During the write phase the active input memristors decrease in conductance while the bias memristors increase in conductance. The changes in memristor conductances, and , for the memristor pairs are listed for all four cases.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "conductance", "updates"], "article_id"=>929390, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.t002", "stats"=>{"downloads"=>1, "page_views"=>35, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Memristor_conductance_updates_during_the_read_and_write_cycle_/929390", "title"=>"Memristor conductance updates during the read and write cycle.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380149"], "description"=>"<p>The maximum power dissipation of a differential synaptic weight changes depending on whether feedback is present or not. In the absence of feedback, the power is maximized when the conductance of each path is the same and the output descends into randomness. When feedback is present the synapse may converge to one of two possible configurations, and the power dissipation increases by a factor of four.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "corresponding", "synaptic"], "article_id"=>929387, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.t007", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Maximum_power_and_corresponding_synaptic_weights_/929387", "title"=>"Maximum power and corresponding synaptic weights.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380115"], "description"=>"<p>An MSS is an idealized two-state element that switches probabilistically between its two states as a function of applied voltage bias and temperature. The probability that the MSS will transition from the B state to the A state is given by , while the probability that the MSS will transition from the A state to the B state is given by . We model a memristor as a collection of MSSs evolving over discrete time steps.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "metastable"], "article_id"=>929354, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g007", "stats"=>{"downloads"=>2, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Generalized_Metastable_Switch_MSS_/929354", "title"=>"Generalized Metastable Switch (MSS).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380128"], "description"=>"<p>A) Logic state occupation frequency after 5000 time steps for both functional model and circuit model. All logic functions can be attained directly from attractor states except for XOR functions, which can be attained via multi-stage circuits. B) The logic functions are stable over time for both functional model and circuit model, indicating stable attractor dynamics.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "attractor", "states"], "article_id"=>929367, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g013", "stats"=>{"downloads"=>3, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_AHaH_attractor_states_as_logic_functions_/929367", "title"=>"AHaH attractor states as logic functions.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380142"], "description"=>"<p>The average total joint actuation required for the robot arm to capture the target remains constant as the number of arm joints increases for actuation using the AHaH motor controller. For random actuation, the required actuation grows exponentially.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "robotic"], "article_id"=>929380, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g019", "stats"=>{"downloads"=>1, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Unsupervised_robotic_arm_challenge_/929380", "title"=>"Unsupervised robotic arm challenge.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380145"], "description"=>"<p>By using single-input AHaH nodes as nodes in a routing tree to perform a strike search, combinatorial optimization problems such as the traveling salesman problem can be solved. Adjusting the learning rate can control the speed and quality of the solution. A) The distance between the 64 cities versus the convergences time for the AHaH-based and random-based strike search. B) Lower learning rates lead to better solutions. C) Higher learning rates decrease convergence time.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "traveling", "salesman"], "article_id"=>929383, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g020", "stats"=>{"downloads"=>2, "page_views"=>42, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_64_city_traveling_salesman_experiment_/929383", "title"=>"64-city traveling salesman experiment.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380111"], "description"=>"<p>A differential pair of memristors is used to form a synaptic weight, allowing for both a sign and magnitude. The bar on the memristor is used to indicate polarity and corresponds to the lower potential end when driving the memristor into a higher conductance state. and form a voltage divider causing the voltage at node y to be some value between and . When driven correctly in the absence of Hebbian feedback a synapse will evolve to a symmetric state where V, alleviating issues arising from device inhomogeneities.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "differential", "memristors", "forms"], "article_id"=>929350, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g004", "stats"=>{"downloads"=>1, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_A_differential_pair_of_memristors_forms_a_synapse_/929350", "title"=>"A differential pair of memristors forms a synapse.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380108"], "description"=>"<p>The AHaH rule naturally forms decision boundaries that maximize the margin between data distributions (black blobs). This is easily visualized in two dimensions, but it is equally valid for any number of inputs. Attractor states are represented by decision boundaries A, B, C (green dotted lines) and D (red dashed line). Each state has a corresponding anti-state: . State A is the null state and its occupation is inhibited by the bias. State D has not yet been reliably achieved in circuit simulations.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "states", "two-input", "ahah"], "article_id"=>929347, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g002", "stats"=>{"downloads"=>2, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Attractor_states_of_a_two_input_AHaH_node_/929347", "title"=>"Attractor states of a two-input AHaH node.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380148"], "description"=>"<p>AHaH classifier classification scores for the Breast Cancer, Census Income, MNIST Handwritten Digits and Reuters-21578 classification benchmark datasets. The AHaH classifier results compare favorably with other methods. Higher scores on the MNIST dataset are possible by increasing the resolution of the spike encoding.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "classification"], "article_id"=>929386, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.t006", "stats"=>{"downloads"=>10, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Benchmark_classification_results_/929386", "title"=>"Benchmark classification results.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380161", "https://ndownloader.figshare.com/files/1380163", "https://ndownloader.figshare.com/files/1380164", "https://ndownloader.figshare.com/files/1380165", "https://ndownloader.figshare.com/files/1380166", "https://ndownloader.figshare.com/files/1380167", "https://ndownloader.figshare.com/files/1380168", "https://ndownloader.figshare.com/files/1380169", "https://ndownloader.figshare.com/files/1380170"], "description"=>"<div><p>Modern computing architecture based on the separation of memory and processing leads to a well known problem called the von Neumann bottleneck, a restrictive limit on the data bandwidth between CPU and RAM. This paper introduces a new approach to computing we call AHaH computing where memory and processing are combined. The idea is based on the attractor dynamics of volatile dissipative electronics inspired by biological systems, presenting an attractive alternative architecture that is able to adapt, self-repair, and learn from interactions with the environment. We envision that both von Neumann and AHaH computing architectures will operate together on the same machine, but that the AHaH computing processor may reduce the power consumption and processing time for certain adaptive learning tasks by orders of magnitude. The paper begins by drawing a connection between the properties of volatility, thermodynamics, and Anti-Hebbian and Hebbian (AHaH) plasticity. We show how AHaH synaptic plasticity leads to attractor states that extract the independent components of applied data streams and how they form a computationally complete set of logic functions. After introducing a general memristive device model based on collections of metastable switches, we show how adaptive synaptic weights can be formed from differential pairs of incremental memristors. We also disclose how arrays of synaptic weights can be used to build a neural node circuit operating AHaH plasticity. By configuring the attractor states of the AHaH node in different ways, high level machine learning functions are demonstrated. This includes unsupervised clustering, supervised and unsupervised classification, complex signal prediction, unsupervised robotic actuation and combinatorial optimization of procedures–all key capabilities of biological nervous systems and modern machine learning algorithms with real world application.</p></div>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "metastable", "switches", "attractors"], "article_id"=>929399, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0085175.s001", "https://dx.doi.org/10.1371/journal.pone.0085175.s002", "https://dx.doi.org/10.1371/journal.pone.0085175.s003", "https://dx.doi.org/10.1371/journal.pone.0085175.s004", "https://dx.doi.org/10.1371/journal.pone.0085175.s005", "https://dx.doi.org/10.1371/journal.pone.0085175.s006", "https://dx.doi.org/10.1371/journal.pone.0085175.s007", "https://dx.doi.org/10.1371/journal.pone.0085175.s008", "https://dx.doi.org/10.1371/journal.pone.0085175.s009"], "stats"=>{"downloads"=>25, "page_views"=>54, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_AHaH_Computing_8211_From_Metastable_Switches_to_Attractors_to_Machine_Learning_/929399", "title"=>"AHaH Computing–From Metastable Switches to Attractors to Machine Learning", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380124"], "description"=>"<p>The AHaH rule naturally forms decision boundaries that maximize the margin between data distributions. Weight space plots show the initial weight coordinate (green circle), the final weight coordinate (red circle) and the path between (blue line). Evolution of weights from a random normal initialization to attractor basins can be clearly seen for both the functional model (A) and circuit model (B).</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "states", "two-input", "ahah", "node", "three-pattern"], "article_id"=>929363, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g012", "stats"=>{"downloads"=>4, "page_views"=>14, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Attractor_states_of_a_two_input_AHaH_node_under_the_three_pattern_input_/929363", "title"=>"Attractor states of a two-input AHaH node under the three-pattern input.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380138"], "description"=>"<p>For the first 30% of samples from the Reuters-21578 data set, the AHaH classifier was operated in supervised mode followed by operation in unsupervised mode for the remaining samples. A confidence threshold of 1.0 was set for unsupervised application of a learn signal. The F1 score for the top ten most frequently occurring labels in the Reuters-21578 data set were tracked. These results show that the AHaH classifier is capable of continuously improving its performance without supervised feedback.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "ahah"], "article_id"=>929376, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g017", "stats"=>{"downloads"=>2, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Semi_supervised_operation_of_the_AHaH_classifier_/929376", "title"=>"Semi-supervised operation of the AHaH classifier.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380151"], "description"=>"<p>While sweeping each parameter of the AHaH clusterer and holding the others constant at their default values, the reported range is where the vergence remained greater than 90%.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "clusterer"], "article_id"=>929389, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.t005", "stats"=>{"downloads"=>4, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_AHaH_clusterer_sweep_results_/929389", "title"=>"AHaH clusterer sweep results.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380117"], "description"=>"<p>A) Solid line represents the model simulated at 100 Hz and dots represent the measurements from a physical Ag-chalcogenide device from Boise State University. Physical and predicted device current resulted from driving a sinusoidal voltage of 0.25 V amplitude at 100 Hz across the device. B) Simulation of two series-connected arbitrary devices with differing model parameter values. C) Simulated response to pulse trains of {10 <i>μ</i>s, 0.2 V, −0.5 V}, {10 <i>μ</i>s, 0.8 V, −2.0 V}, and {5 <i>μ</i>s, 0.8 V, −2.0 V} showing the incremental change in resistance in response to small voltage pulses. D) Simulated time response of model from driving a sinusoidal voltage of 0.25 V amplitude at 100 Hz, 150 Hz, and 200 Hz. E) Simulated response to a triangle wave of 0.1 V amplitude at 100 Hz showing the expected incremental behavior of the model. F) Simulated and scaled hysteresis curves for the AIST, GST, and WO<i><sub>x</sub></i> devices (not to scale).</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "memristive"], "article_id"=>929356, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g008", "stats"=>{"downloads"=>3, "page_views"=>10, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Generalized_memristive_device_model_simulations_/929356", "title"=>"Generalized memristive device model simulations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380107"], "description"=>"<p>A) A first replenished pressurized container is allowed to diffuse into two non-pressurized empty containers and though a region of matter M. B) The gradient reduces faster than the gradient due to the conductance differential. C) This causes to grow more than , reducing the conductance differential and leading to anti-Hebbian learning. D) The first detectable signal (work) is available at owing to the differential that favors it. As a response to this signal, events may transpire in the environment that open up new pathways to particle dissipation. The initial conductance differential is reinforced leading to Hebbian learning.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics"], "article_id"=>929346, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g001", "stats"=>{"downloads"=>1, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_AHaH_process_/929346", "title"=>"AHaH process.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380120"], "description"=>"<p>Each data point represents the change in a synaptic weight as a function of AHaH node activation, y. Blue data points correspond to input synapses and red data points to bias inputs. There is good congruence between the A) functional and B) circuit implementations of the AHaH rule.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "ahah", "reconstructed"], "article_id"=>929359, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g010", "stats"=>{"downloads"=>2, "page_views"=>46, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_AHaH_rule_reconstructed_from_simulations_/929359", "title"=>"The AHaH rule reconstructed from simulations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380154"], "description"=>"<p>Digital logic states ‘0’ and ‘1’ across two input lines are converted to a spike encoding across four input lines. A spike encoding consists of either spikes (1) or no spikes (). This encoding insures that the number of spikes at any given time is constant.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics"], "article_id"=>929392, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.t001", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Spike_logic_patterns_/929392", "title"=>"Spike logic patterns.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380131"], "description"=>"<p>Functional (A) and circuit (B) simulation results of an AHaH clusterer formed of twenty AHaH nodes. Spike patterns were encoded over 16 active input lines from a total spike space of 256. The number of noise bits was swept from 1 (6.25%) to 10 (62.5%) while the vergence was measured. The performance is a function of the total number of spike patterns. Blue = 16 (100% load), Orange = 20 (125% load), Purple = 24 (150% load), Green = 32 (200% load), Red = 64 (400% load).</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics"], "article_id"=>929369, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g014", "stats"=>{"downloads"=>1, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_AHaH_clusterer_/929369", "title"=>"AHaH clusterer.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380150"], "description"=>"<p>The table defines all 16 possible logic functions (LF) for the four spike encoded input patterns (SP).</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics"], "article_id"=>929388, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.t004", "stats"=>{"downloads"=>1, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Logic_functions_/929388", "title"=>"Logic functions.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380147"], "description"=>"<p>The applications and benchmarks presented in this paper to demonstrate various machine learning tasks using AHaH plasticity require different AHaH node configurations depending on the type of data being processed and what the desired result is. The sparsity is a function of the incoming data and is defined as the number of coactive spikes divided by the total spike space.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "spike", "sparsity", "ahah", "node"], "article_id"=>929385, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.t008", "stats"=>{"downloads"=>1, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Application_spike_sparsity_and_AHaH_node_count_/929385", "title"=>"Application spike sparsity and AHaH node count.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380113"], "description"=>"<p>A) Voltages during read phase across spike input memristors. B) Voltages during write phase across spike input memristors. C) Voltages during read phase across bias memristors. D) Voltages during write phase across bias memristors.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "voltages", "memristors"], "article_id"=>929352, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g006", "stats"=>{"downloads"=>1, "page_views"=>44, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Circuit_voltages_across_memristors_during_the_read_and_write_phases_/929352", "title"=>"Circuit voltages across memristors during the read and write phases.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380140"], "description"=>"<p>By posing prediction as a multi-label classification problem, the AHaH classifier can learn complex temporal waveforms and make extended predictions via recursion. Here, the temporal signal (dots) is a summation of five sinusoidal signals with randomly chosen amplitudes, periods, and phases. The classifier is trained for 10,000 time steps (last 100 steps shown, dotted line) and then tested for 300 time steps (solid line).</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "ahah"], "article_id"=>929378, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g018", "stats"=>{"downloads"=>4, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Complex_signal_prediction_with_the_AHaH_classifier_/929378", "title"=>"Complex signal prediction with the AHaH classifier.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380153"], "description"=>"<p>The devices used to test our general memristive device model include the Ag-chalcogenide, AIST, GST, and WO<i><sub>x</sub></i> devices. The parameters in this table were determined by comparing the model response to a simulated sinusoidal or triangle-wave voltage to real I–V data of physical devices.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "memristive"], "article_id"=>929391, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.t003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_General_memristive_device_model_parameters_fit_to_various_devices_/929391", "title"=>"General memristive device model parameters fit to various devices.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380119"], "description"=>"<p>The robotic arm challenge involves a multi-jointed robotic arm that moves to capture a target. Each joint on the arm has 360 degrees of rotation, and the base joint is anchored to the floor. Using only a value signal relating the distance from the head to the target and an AHaH motor controller taking as input sensory stimuli in a closed-loop configuration, the robotic arm autonomously learns to capture stationary and moving targets. New targets are dropped within the arm’s reach radius after each capture, and the number of discrete angular joint actuations required for each catch is recorded to asses capture efficiency.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "robotic"], "article_id"=>929358, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g009", "stats"=>{"downloads"=>1, "page_views"=>21, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Unsupervised_robotic_arm_challenge_/929358", "title"=>"Unsupervised robotic arm challenge.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}
  • {"files"=>["https://ndownloader.figshare.com/files/1380109"], "description"=>"<p>By connecting the output of AHaH nodes (circles) to the input of static NAND gates, one may create a universal reconfigurable logic gate by configuring the AHaH node attractor states (). The structure of the data stream on binary encoded channels and support AHaH attractor states (<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0085175#pone-0085175-g002\" target=\"_blank\">Figure 2</a>). Through configuration of node attractor states the logic function of the circuit can be configured and all logic functions are possible. If inputs are represented as a spike encoding over four channels then AHaH node attractor states can attain all logic functions without the use of NAND gates.</p>", "links"=>[], "tags"=>["Computational biology", "computational neuroscience", "Circuit models", "Coding mechanisms", "neuroscience", "Cognitive neuroscience", "cognition", "Decision making", "Motor reactions", "Sensory systems", "Visual system", "Learning and memory", "Motor systems", "neural networks", "algorithms", "Computer architecture", "Computer hardware", "Computing systems", "Hybrid computing", "text mining", "Electrical engineering", "Electronics engineering", "Solid state physics", "reconfigurable"], "article_id"=>929348, "categories"=>["Physics", "Biological Sciences", "Engineering"], "users"=>["Michael Alexander Nugent", "Timothy Wesley Molter"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0085175.g003", "stats"=>{"downloads"=>5, "page_views"=>5, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Universal_reconfigurable_logic_/929348", "title"=>"Universal reconfigurable logic.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-02-10 03:23:44"}

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