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      Lamina specific loss of inhibition may lead to distinct neuropathic manifestations: a computational modeling approach

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          Abstract

          Introduction It has been reported that inhibitory control at the superficial dorsal horn (SDH) can act in a regionally distinct manner, which suggests that regionally specific subpopulations of SDH inhibitory neurons may prevent one specific neuropathic condition. Methods In an attempt to address this issue, we provide an alternative approach by integrating neuroanatomical information provided by different studies to construct a network-model of the SDH. We use Neuroids to simulate each neuron included in that model by adapting available experimental evidence. Results Simulations suggest that the maintenance of the proper level of pain sensitivity may be attributed to lamina II inhibitory neurons and, therefore, hyperalgesia may be elicited by suppression of the inhibitory tone at that lamina. In contrast, lamina III inhibitory neurons are more likely to be responsible for keeping the nociceptive pathway from the mechanoreceptive pathway, so loss of inhibitory control in that region may result in allodynia. The SDH network-model is also able to replicate non-linearities associated to pain processing, such as Aβ-fiber mediated analgesia and frequency-dependent increase of the neural response. Discussion By incorporating biophysical accuracy and newer experimental evidence, the SDH network-model may become a valuable tool for assessing the contribution of specific SDH connectivity patterns to noxious transmission in both physiological and pathological conditions.

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          Most cited references56

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          Neural networks and physical systems with emergent collective computational abilities.

          J Hopfield (1982)
          Computational properties of use of biological organisms or to the construction of computers can emerge as collective properties of systems having a large number of simple equivalent components (or neurons). The physical meaning of content-addressable memory is described by an appropriate phase space flow of the state of a system. A model of such a system is given, based on aspects of neurobiology but readily adapted to integrated circuits. The collective properties of this model produce a content-addressable memory which correctly yields an entire memory from any subpart of sufficient size. The algorithm for the time evolution of the state of the system is based on asynchronous parallel processing. Additional emergent collective properties include some capacity for generalization, familiarity recognition, categorization, error correction, and time sequence retention. The collective properties are only weakly sensitive to details of the modeling or the failure of individual devices.
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            Neurons with graded response have collective computational properties like those of two-state neurons.

            J Hopfield (1984)
            A model for a large network of "neurons" with a graded response (or sigmoid input-output relation) is studied. This deterministic system has collective properties in very close correspondence with the earlier stochastic model based on McCulloch - Pitts neurons. The content- addressable memory and other emergent collective properties of the original model also are present in the graded response model. The idea that such collective properties are used in biological systems is given added credence by the continued presence of such properties for more nearly biological "neurons." Collective analog electrical circuits of the kind described will certainly function. The collective states of the two models have a simple correspondence. The original model will continue to be useful for simulations, because its connection to graded response systems is established. Equations that include the effect of action potentials in the graded response system are also developed.
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              Models and mechanisms of hyperalgesia and allodynia.

              Hyperalgesia and allodynia are frequent symptoms of disease and may be useful adaptations to protect vulnerable tissues. Both may, however, also emerge as diseases in their own right. Considerable progress has been made in developing clinically relevant animal models for identifying the most significant underlying mechanisms. This review deals with experimental models that are currently used to measure (sect. II) or to induce (sect. III) hyperalgesia and allodynia in animals. Induction and expression of hyperalgesia and allodynia are context sensitive. This is discussed in section IV. Neuronal and nonneuronal cell populations have been identified that are indispensable for the induction and/or the expression of hyperalgesia and allodynia as summarized in section V. This review focuses on highly topical spinal mechanisms of hyperalgesia and allodynia including intrinsic and synaptic plasticity, the modulation of inhibitory control (sect. VI), and neuroimmune interactions (sect. VII). The scientific use of language improves also in the field of pain research. Refined definitions of some technical terms including the new definitions of hyperalgesia and allodynia by the International Association for the Study of Pain are illustrated and annotated in section I.
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                Author and article information

                Contributors
                Role: ND
                Role: ND
                Role: ND
                Role: ND
                Journal
                reng
                Research on Biomedical Engineering
                Res. Biomed. Eng.
                Sociedade Brasileira de Engenharia Biomédica (Rio de Janeiro )
                2446-4740
                June 2015
                : 31
                : 2
                : 133-147
                Affiliations
                [1 ] Universidad Simón Bolívar Venezuela
                [2 ] Universidad Politécnica Salesiana de Guayaquil Ecuador
                [3 ] Programa Promeinfo Ecuador
                [4 ] Universidad Politécnica Salesiana de Cuenca Ecuador
                [5 ] Universidad Simón Bolívar Venezuela
                [6 ] Universidad Central de Venezuela Venezuela
                Article
                S2446-47402015000200133
                10.1590/2446-4740.0734
                3b81f816-12b6-46c6-bea8-ebd9816839f4

                http://creativecommons.org/licenses/by/4.0/

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                SciELO Brazil

                Self URI (journal page): http://www.scielo.br/scielo.php?script=sci_serial&pid=2446-4740&lng=en
                Categories
                ENGINEERING, BIOMEDICAL

                Biomedical engineering
                Computational pain modeling,Inhibitory control,Superficial dorsal horn circuit,Network model

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