Journal articles: 'Sensory plasticity'

1

Giasson, Claude J., and Christian Casanova. "Plasticity and Sensory Substitution." Canadian Journal of Optometry 71, no. 4 (August 1, 2009): 39. http://dx.doi.org/10.15353/cjo.71.654.

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Doty, R. W. "Sensory Neurons: Diversity, Development, Plasticity." Archives of Neurology 51, no. 6 (June 1, 1994): 539. http://dx.doi.org/10.1001/archneur.1994.00540180017006.

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Ptito, Maurice, Ron Kupers, Steve Lomber, and Pietro Pietrini. "Sensory Deprivation and Brain Plasticity." Neural Plasticity 2012 (2012): 1–2. http://dx.doi.org/10.1155/2012/810370.

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Calford, M. B. "Dynamic representational plasticity in sensory cortex." Neuroscience 111, no. 4 (June 2002): 709–38. http://dx.doi.org/10.1016/s0306-4522(02)00022-2.

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Ostry, David J., and Paul L. Gribble. "Sensory Plasticity in Human Motor Learning." Trends in Neurosciences 39, no. 2 (February 2016): 114–23. http://dx.doi.org/10.1016/j.tins.2015.12.006.

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Davidoff, R. "Sensory Neurons: Diversity, Development, and Plasticity." Neurology 43, no. 8 (August 1, 1993): 1633. http://dx.doi.org/10.1212/wnl.43.8.1633-d.

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Butko, Nicholas J., and Jochen Triesch. "Learning sensory representations with intrinsic plasticity." Neurocomputing 70, no. 7-9 (March 2007): 1130–38. http://dx.doi.org/10.1016/j.neucom.2006.11.006.

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Frank, Eric. "Sensory Neurons: Diversity, Development and Plasticity." Trends in Neurosciences 16, no. 12 (December 1993): 534–35. http://dx.doi.org/10.1016/0166-2236(93)90201-v.

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Fox, Kevin, Helen Wallace, and Stanislaw Glazewski. "Is there a thalamic component to experience–dependent cortical plasticity?" Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 357, no. 1428 (December 29, 2002): 1709–15. http://dx.doi.org/10.1098/rstb.2002.1169.

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Sensory deprivation and injury to the peripheral nervous system both induce plasticity in the somatosensory system of adult animals, but in different places. While injury induces plasticity at several locations within the ascending somatosensory pathways, sensory deprivation appears only to affect the somatosensory cortex. Experiments have been performed to detect experience–dependent plasticity in thalamic receptive fields, thalamic domain sizes and convergence of thalamic receptive fields onto cortical cells. So far, plasticity has not been detected with sensory deprivation paradigms that cause substantial cortical plasticity. Part of the reason for the lack of thalamic plasticity may lie in the synaptic properties of afferent systems to the thalamus. A second factor may lie in the differences in the organization of cortical and thalamic circuits. Many deprivation paradigms induce plasticity by decreasing phasic lateral inhibition. Since lateral inhibition appears to be far weaker in the thalamus than the cortex, sensory deprivation may not cause large enough imbalances in thalamic activity to induce plasticity in the thalamus.

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Desgent, Sébastien, and Maurice Ptito. "Cortical GABAergic Interneurons in Cross-Modal Plasticity following Early Blindness." Neural Plasticity 2012 (2012): 1–20. http://dx.doi.org/10.1155/2012/590725.

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Early loss of a given sensory input in mammals causes anatomical and functional modifications in the brain via a process called cross-modal plasticity. In the past four decades, several animal models have illuminated our understanding of the biological substrates involved in cross-modal plasticity. Progressively, studies are now starting to emphasise on cell-specific mechanisms that may be responsible for this intermodal sensory plasticity. Inhibitory interneurons expressing γ-aminobutyric acid (GABA) play an important role in maintaining the appropriate dynamic range of cortical excitation, in critical periods of developmental plasticity, in receptive field refinement, and in treatment of sensory information reaching the cerebral cortex. The diverse interneuron population is very sensitive to sensory experience during development. GABAergic neurons are therefore well suited to act as a gate for mediating cross-modal plasticity. This paper attempts to highlight the links between early sensory deprivation, cortical GABAergic interneuron alterations, and cross-modal plasticity, discuss its implications, and further provide insights for future research in the field.

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Maruska, Karen P., and Julie M. Butler. "Reproductive- and Social-State Plasticity of Multiple Sensory Systems in a Cichlid Fish." Integrative and Comparative Biology 61, no. 1 (May 10, 2021): 249–68. http://dx.doi.org/10.1093/icb/icab062.

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Synopsis Intra- and inter-sexual communications are vital to the survival and reproductive success of animals. In species that cycle in and out of breeding or other physiological condition, sensory function can be modulated to optimize communication at crucial times. Little is known, however, about how widespread this sensory plasticity is across taxa, whether it occurs in multiple senses or both sexes within a species, and what potential modulatory substances and substrates are involved. Thus, studying modulation of sensory communication in a single species can provide valuable insights for understanding how sensory abilities can be altered to optimize detection of salient signals in different sensory channels and social contexts. The African cichlid fish Astatotilapia burtoni uses multimodal communication in social contexts such as courtship, territoriality, and parental care and shows plasticity in sensory abilities. In this review, we synthesize what is known about how visual, acoustic, and chemosensory communication is used in A. burtoni in inter- and intra-specific social contexts, how sensory funtion is modulated by an individual’s reproductive, metabolic, and social state, and discuss evidence for plasticity in potential modulators that may contribute to changes in sensory abilities and behaviors. Sensory plasticity in females is primarily associated with the natural reproductive cycle and functions to improve detection of courtship signals (visual, auditory, chemosensory, and likely mechanosensory) from high-quality males for reproduction. Plasticity in male sensory abilities seems to function in altering their ability to detect the status of other males in the service of territory ownership and future reproductive opportunities. Changes in different classes of potential modulators or their receptors (steroids, neuropeptides, and biogenic amines) occur at both peripheral sensory organs (eye, inner ear, and olfactory epithelium) and central visual, olfactory, and auditory processing regions, suggesting complex mechanisms contributing to plasticity of sensory function. This type of sensory plasticity revealed in males and females of A. burtoni is likely more widespread among diverse animals than currently realized, and future studies should take an integrative and comparative approach to better understand the proximate and ultimate mechanisms modulating communication abilities across taxa.

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Bell, C. C., V. Z. Han, Y. Sugawara, and K. Grant. "Synaptic plasticity in the mormyrid electrosensory lobe." Journal of Experimental Biology 202, no. 10 (May 15, 1999): 1339–47. http://dx.doi.org/10.1242/jeb.202.10.1339.

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The mormyrid electrosensory lateral line lobe (ELL) is one of several different sensory structures in fish that behave as adaptive sensory processors. These structures generate negative images of predictable features in the sensory inflow which are added to the actual inflow to minimize the effects of predictable sensory features. The negative images are generated through a process of association between centrally originating predictive signals and sensory inputs from the periphery. In vitro studies in the mormyrid ELL show that pairing of parallel fiber input with Na+ spikes in postsynaptic cells results in synaptic depression at the parallel fiber synapses. The synaptic plasticity observed at the cellular level and the associative process of generating negative images of predicted sensory input at the systems level share a number of properties. Both are rapidly established, anti-Hebbian, reversible, input-specific and tightly restricted in time. These common properties argue strongly that associative depression at the parallel fiber synapse contributes to the adaptive generation of negative images in the mormyrid ELL.

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Scheyltjens, Isabelle, and Lutgarde Arckens. "The Current Status of Somatostatin-Interneurons in Inhibitory Control of Brain Function and Plasticity." Neural Plasticity 2016 (2016): 1–20. http://dx.doi.org/10.1155/2016/8723623.

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The mammalian neocortex contains many distinct inhibitory neuronal populations to balance excitatory neurotransmission. A correct excitation/inhibition equilibrium is crucial for normal brain development, functioning, and controlling lifelong cortical plasticity. Knowledge about how the inhibitory network contributes to brain plasticity however remains incomplete. Somatostatin- (SST-) interneurons constitute a large neocortical subpopulation of interneurons, next to parvalbumin- (PV-) and vasoactive intestinal peptide- (VIP-) interneurons. Unlike the extensively studied PV-interneurons, acknowledged as key components in guiding ocular dominance plasticity, the contribution of SST-interneurons is less understood. Nevertheless, SST-interneurons are ideally situated within cortical networks to integrate unimodal or cross-modal sensory information processing and therefore likely to be important mediators of experience-dependent plasticity. The lack of knowledge on SST-interneurons partially relates to the wide variety of distinct subpopulations present in the sensory neocortex. This review informs on those SST-subpopulations hitherto described based on anatomical, molecular, or electrophysiological characteristics and whose functional roles can be attributed based on specific cortical wiring patterns. A possible role for these subpopulations in experience-dependent plasticity will be discussed, emphasizing on learning-induced plasticity and on unimodal and cross-modal plasticity upon sensory loss. This knowledge will ultimately contribute to guide brain plasticity into well-defined directions to restore sensory function and promote lifelong learning.

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Phan, Mimi L., and Kasia M. Bieszczad. "Sensory Cortical Plasticity Participates in the Epigenetic Regulation of Robust Memory Formation." Neural Plasticity 2016 (2016): 1–12. http://dx.doi.org/10.1155/2016/7254297.

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Neuroplasticity remodels sensory cortex across the lifespan. A function of adult sensory cortical plasticity may be capturing available information during perception for memory formation. The degree of experience-dependent remodeling in sensory cortex appears to determine memory strength and specificity for important sensory signals. A key open question is how plasticity is engaged to induce different degrees of sensory cortical remodeling. Neural plasticity for long-term memory requires the expression of genes underlying stable changes in neuronal function, structure, connectivity, and, ultimately, behavior. Lasting changes in transcriptional activity may depend on epigenetic mechanisms; some of the best studied in behavioral neuroscience are DNA methylation and histone acetylation and deacetylation, which, respectively, promote and repress gene expression. One purpose of this review is to propose epigenetic regulation of sensory cortical remodeling as a mechanism enabling the transformation of significant information from experiences into content-rich memories of those experiences. Recent evidence suggests how epigenetic mechanisms regulate highly specific reorganization of sensory cortical representations that establish a widespread network for memory. Thus, epigenetic mechanisms could initiate events to establish exceptionally persistent and robust memories at a systems-wide level by engaging sensory cortical plasticity for gatingwhatandhow muchinformation becomes encoded.

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Zochodne, Douglas W. "Diabetes and the plasticity of sensory neurons." Neuroscience Letters 596 (June 2015): 60–65. http://dx.doi.org/10.1016/j.neulet.2014.11.017.

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Jamann, Nora, Merryn Jordan, and Maren Engelhardt. "Activity-Dependent Axonal Plasticity in Sensory Systems." Neuroscience 368 (January 2018): 268–82. http://dx.doi.org/10.1016/j.neuroscience.2017.07.035.

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17

Gundersen, Brigitta. "Context-dependent plasticity in a sensory circuit." Nature Neuroscience 16, no. 10 (September 25, 2013): 1366. http://dx.doi.org/10.1038/nn1013-1366.

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Das, Aniruddha. "Plasticity in adult sensory cortex: a review." Network: Computation in Neural Systems 8, no. 2 (January 1997): R33—R76. http://dx.doi.org/10.1088/0954-898x_8_2_001.

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Depner, Manfred, Konstantin Tziridis, Andreas Hess, and Holger Schulze. "Sensory cortex lesion triggers compensatory neuronal plasticity." BMC Neuroscience 15, no. 1 (2014): 57. http://dx.doi.org/10.1186/1471-2202-15-57.

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Moore, David R. "Stroke recovery and sensory plasticity: Common mechanisms?" Developmental Psychobiology 54, no. 3 (March 13, 2012): 326–31. http://dx.doi.org/10.1002/dev.20627.

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Chapman, Ben B., Lesley J. Morrell, Colin R. Tosh, and Jens Krause. "Behavioural consequences of sensory plasticity in guppies." Proceedings of the Royal Society B: Biological Sciences 277, no. 1686 (January 6, 2010): 1395–401. http://dx.doi.org/10.1098/rspb.2009.2055.

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22

Edeline, JM. "Does Hebbian synaptic plasticity explain learning-induced sensory plasticity in adult mammals?" Journal of Physiology-Paris 90, no. 3-4 (January 1996): 271–76. http://dx.doi.org/10.1016/s0928-4257(97)81437-4.

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23

Feldman, Daniel E., and Michael Brecht. "Map Plasticity in Somatosensory Cortex." Science 310, no. 5749 (November 3, 2005): 810–15. http://dx.doi.org/10.1126/science.1115807.

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Sensory maps in neocortex are adaptively altered to reflect recent experience and learning. In somatosensory cortex, distinct patterns of sensory use or disuse elicit multiple, functionally distinct forms of map plasticity. Diverse approaches—genetics, synaptic and in vivo physiology, optical imaging, and ultrastructural analysis—suggest a distributed model in which plasticity occurs at multiple sites in the cortical circuit with multiple cellular/synaptic mechanisms and multiple likely learning rules for plasticity. This view contrasts with the classical model in which the map plasticity reflects a single Hebbian process acting at a small set of cortical synapses.

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Forster, H. V. "Invited Review: Plasticity in the control of breathing following sensory denervation." Journal of Applied Physiology 94, no. 2 (February 1, 2003): 784–94. http://dx.doi.org/10.1152/japplphysiol.00602.2002.

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The purpose of this manuscript is to review the results of studies on the recovery or plasticity following a denervation- or lesion-induced change in breathing. Carotid body denervation (CBD), lung denervation (LD), cervical (CDR) and thoracic (TDR) dorsal rhizotomy, dorsal spinal column lesions, and lesions at pontine, medullary, and spinal sites all chronically alter breathing. The plasticity after these is highly variable, ranging from near complete recovery of the peripheral chemoreflex in rats after CBD to minimal recovery of the Hering-Breuer inflation reflex in ponies after LD. The degree of plasticity varies among the different functions of each pathway, and plasticity varies with the age of the animal when the lesion was made. In addition, plasticity after some lesions varies between species, and plasticity is greater in the awake than in the anesthetized state. Reinnervation is not a common mechanism of plasticity. There is evidence supporting two mechanisms of plasticity. One is through upregulation of an alternate sensory pathway, such as serotonin-mediated aortic chemoreception after CBD. The second is through upregulation on the efferent limb of a reflex, such as serotonin-mediated increased responsiveness of phrenic motoneurons after CDR, TDR, and spinal cord injury. Accordingly, numerous components of the ventilatory control system exhibit plasticity after denervation or lesion-induced changes in breathing; this plasticity is uniform neither in magnitude nor in underlying mechanisms. A major need in future research is to determine whether “reorganization” within the central nervous system contributes to plasticity following lesion-induced changes in breathing.

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Mihail, Sandra M., Andi Wangzhou, Kumud K. Kunjilwar, Jamie K. Moy, Gregory Dussor, Edgar T. Walters, and Theodore J. Price. "MNK-eIF4E signalling is a highly conserved mechanism for sensory neuron axonal plasticity: evidence from Aplysia californica." Philosophical Transactions of the Royal Society B: Biological Sciences 374, no. 1785 (September 23, 2019): 20190289. http://dx.doi.org/10.1098/rstb.2019.0289.

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Injury to sensory neurons causes an increase in the excitability of these cells leading to enhanced action potential generation and a lowering of spike threshold. This type of sensory neuron plasticity occurs across vertebrate and invertebrate species and has been linked to the development of both acute and persistent pain. Injury-induced plasticity in sensory neurons relies on localized changes in gene expression that occur at the level of mRNA translation. Many different translation regulation signalling events have been defined and these signalling events are thought to selectively target subsets of mRNAs. Recent evidence from mice suggests that the key signalling event for nociceptor plasticity is mitogen-activated protein kinase-interacting kinase (MNK) -mediated phosphorylation of eukaryotic translation initiation factor (eIF) 4E. To test the degree to which this is conserved in other species, we used a previously described sensory neuron plasticity model in Aplysia californica . We find, using a variety of pharmacological tools, that MNK signalling is crucial for axonal hyperexcitability in sensory neurons from Aplysia . We propose that MNK-eIF4E signalling is a core, evolutionarily conserved, signalling module that controls nociceptor plasticity. This finding has important implications for the therapeutic potential of this target, and it provides interesting clues about the evolutionary origins of mechanisms important for pain-related plasticity. This article is part of the Theo Murphy meeting issue ‘Evolution of mechanisms and behaviour important for pain’.

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Maya-Vetencourt, José Fernando, and Nicola Origlia. "Visual Cortex Plasticity: A Complex Interplay of Genetic and Environmental Influences." Neural Plasticity 2012 (2012): 1–14. http://dx.doi.org/10.1155/2012/631965.

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The central nervous system architecture is highly dynamic and continuously modified by sensory experience through processes of neuronal plasticity. Plasticity is achieved by a complex interplay of environmental influences and physiological mechanisms that ultimately activate intracellular signal transduction pathways regulating gene expression. In addition to the remarkable variety of transcription factors and their combinatorial interaction at specific gene promoters, epigenetic mechanisms that regulate transcription have emerged as conserved processes by which the nervous system accomplishes the induction of plasticity. Experience-dependent changes of DNA methylation patterns and histone posttranslational modifications are, in fact, recruited as targets of plasticity-associated signal transduction mechanisms. Here, we shall concentrate on structural and functional consequences of early sensory deprivation in the visual system and discuss how intracellular signal transduction pathways associated with experience regulate changes of chromatin structure and gene expression patterns that underlie these plastic phenomena. Recent experimental evidence for mechanisms of cross-modal plasticity following congenital or acquired sensory deprivation both in human and animal models will be considered as well. We shall also review different experimental strategies that can be used to achieve the recovery of sensory functions after long-term deprivation in humans.

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Romashchenko, A. V., Р. Е. Kireeva, M. В. Sharapova, Т. A. Zapara, and A. S. Ratushnyak. "Learning-induced sensory plasticity of mouse olfactory epithelium." Vavilov Journal of Genetics and Breeding 22, no. 8 (January 3, 2019): 1070–77. http://dx.doi.org/10.18699/vj18.452.

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Traditionally, studies of the neurobiology of learning and memory focus on the circuitry that interfaces between sensory inputs and behavioral outputs, such as the amygdala and cerebellum. However, evidence is accumulating that some forms of learning can in fact drive stimulusspecifc changes very early in sensory systems, including not only primary sensory cortices but also precortical structures and even the peripheral sensory organs themselves. In this study, we investigated the eﬀect of olfactory associative training on the functional activity of olfactory epithelium neurons in response to an indiﬀerent stimulus (orange oil). It was found that such a peripheral structure of the olfactory system of adult mice as the olfactory epithelium (OE) demonstrates experiencedependent plasticity. In our experiment, associative learning led to changes in the patterns of OE cell activation in response to orange oil in comparison with the control group and animals that were given odor without reinforcement. To interpret the results obtained, we compared the distribution of MRI contrast across the zones of OE in response to a conditioned odor in trained animals and in control animals that were given orange oil at three concentrations: original (used for conditioning), 4fold higher and 4fold lower. Since the OE activation patterns obtained coincided in the group of trained animals and controls, which were stimulated with orange oil at the 4fold higher concentration, it can be concluded that associative conditioning increased the sensitivity of the OE to the conditioned stimulus. The observed increase in OE response to orange oil may be the result of neurogenesis, i. e. the maturation of new olfactory neurons responsive to this stimulus, or the consequence of an increase in individual sensitivity of each OE neuron. Based on data of MRI contrast accumulation in mouse OE, the sensory plasticity way in learninginduced increase in sensitivity of OE to conditioned stimulus is more possible. Thus, the sensory plasticity of the OE plays a signifcant role in the formation of the neuronal response to the provision of an initially indiﬀerent odor and is part of the adaptive responses to the environmental changing.

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Fallon, James B., Dexter R. F. Irvine, and Robert K. Shepherd. "Cochlear implants and brain plasticity." Hearing Research 238, no. 1-2 (April 2008): 110–17. http://dx.doi.org/10.1016/j.heares.2007.08.004.

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Franosch, Jan-Moritz P., Sebastian Urban, and J. Leo van Hemmen. "Supervised Spike-Timing-Dependent Plasticity: A Spatiotemporal Neuronal Learning Rule for Function Approximation and Decisions." Neural Computation 25, no. 12 (December 2013): 3113–30. http://dx.doi.org/10.1162/neco_a_00520.

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How can an animal learn from experience? How can it train sensors, such as the auditory or tactile system, based on other sensory input such as the visual system? Supervised spike-timing-dependent plasticity (supervised STDP) is a possible answer. Supervised STDP trains one modality using input from another one as “supervisor.” Quite complex time-dependent relationships between the senses can be learned. Here we prove that under very general conditions, supervised STDP converges to a stable configuration of synaptic weights leading to a reconstruction of primary sensory input.

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Jiang, Mingchen C., Sherif M. Elbasiouny, William F. Collins, and C. J. Heckman. "The transformation of synaptic to system plasticity in motor output from the sacral cord of the adult mouse." Journal of Neurophysiology 114, no. 3 (September 2015): 1987–2004. http://dx.doi.org/10.1152/jn.00337.2015.

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Synaptic plasticity is fundamental in shaping the output of neural networks. The transformation of synaptic plasticity at the cellular level into plasticity at the system level involves multiple factors, including behavior of local networks of interneurons. Here we investigate the synaptic to system transformation for plasticity in motor output in an in vitro preparation of the adult mouse spinal cord. System plasticity was assessed from compound action potentials (APs) in spinal ventral roots, which were generated simultaneously by the axons of many motoneurons (MNs). Synaptic plasticity was assessed from intracellular recordings of MNs. A computer model of the MN pool was used to identify the middle steps in the transformation from synaptic to system behavior. Two input systems that converge on the same MN pool were studied: one sensory and one descending. The two synaptic input systems generated very different motor outputs, with sensory stimulation consistently evoking short-term depression (STD) whereas descending stimulation had bimodal plasticity: STD at low frequencies but short-term facilitation (STF) at high frequencies. Intracellular and pharmacological studies revealed contributions from monosynaptic excitation and stimulus time-locked inhibition but also considerable asynchronous excitation sustained from local network activity. The computer simulations showed that STD in the monosynaptic excitatory input was the primary driver of the system STD in the sensory input whereas network excitation underlies the bimodal plasticity in the descending system. These results provide insight on the roles of plasticity in the monosynaptic and polysynaptic inputs converging on the same MN pool to overall motor plasticity.

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Polley, Daniel B., Andrea R. Hillock, Christopher Spankovich, Maria V. Popescu, David W. Royal, and Mark T. Wallace. "Development and Plasticity of Intra- and Intersensory Information Processing." Journal of the American Academy of Audiology 19, no. 10 (November 2008): 780–98. http://dx.doi.org/10.3766/jaaa.19.10.6.

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The functional architecture of sensory brain regions reflects an ingenious biological solution to the competing demands of a continually changing sensory environment. While they are malleable, they have the constancy necessary to support a stable sensory percept. How does the functional organization of sensory brain regions contend with these antithetical demands? Here we describe the functional organization of auditory and multisensory (i.e., auditory-visual) information processing in three sensory brain structures: (1) a low-level unisensory cortical region, the primary auditory cortex (A1); (2) a higher-order multisensory cortical region, the anterior ectosylvian sulcus (AES); and (3) a multisensory subcortical structure, the superior colliculus (SC). We then present a body of work that characterizes the ontogenic expression of experience-dependent influences on the operations performed by the functional circuits contained within these regions. We will present data to support the hypothesis that the competing demands for plasticity and stability are addressed through a developmental transition in operational properties of functional circuits from an initially labile mode in the early stages of postnatal development to a more stable mode in the mature brain that retains the capacity for plasticity under specific experiential conditions. Finally, we discuss parallels between the central tenets of functional organization and plasticity of sensory brain structures drawn from animal studies and a growing literature on human brain plasticity and the potential applicability of these principles to the audiology clinic.

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Feldman, Daniel E. "A New Critical Period for Sensory Map Plasticity." Neuron 31, no. 2 (August 2001): 171–73. http://dx.doi.org/10.1016/s0896-6273(01)00363-4.

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Krupa, D. J., A. A. Ghazanfar, and M. A. L. Nicolelis. "Immediate thalamic sensory plasticity depends on corticothalamic feedback." Proceedings of the National Academy of Sciences 96, no. 14 (July 6, 1999): 8200–8205. http://dx.doi.org/10.1073/pnas.96.14.8200.

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Undem, B. J. "Inflammation-induced sensory nerve plasticity in the airways." Clinical & Experimental Allergy Reviews 1, no. 2 (July 2001): 93–95. http://dx.doi.org/10.1046/j.1472-9725.2001.00015.x.

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Cafferty, W. B. J., E. J. Bradbury, M. Lidierth, M. Jones, P. J. Duffy, S. Pezet, and S. B. McMahon. "Chondroitinase ABC-Mediated Plasticity of Spinal Sensory Function." Journal of Neuroscience 28, no. 46 (November 12, 2008): 11998–2009. http://dx.doi.org/10.1523/jneurosci.3877-08.2008.

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Bach-y-Rita, P. "Brain Plasticity as a Basis of Sensory Substitution." Neurorehabilitation and Neural Repair 1, no. 2 (January 1, 1987): 67–71. http://dx.doi.org/10.1177/136140968700100202.

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GRAZIADEI, P. P. C., and G. A. MONTI GRAZIADEI. "Neurogenesis and Plasticity of the Olfactory Sensory Neurons." Annals of the New York Academy of Sciences 457, no. 1 Hope for a Ne (December 1985): 127–42. http://dx.doi.org/10.1111/j.1749-6632.1985.tb20802.x.

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Halligan, Peter W., John C. Marshall, and Derick T. Wade. "Sensory disorganization and perceptual plasticity after limb amputation." NeuroReport 5, no. 11 (June 1994): 1341–45. http://dx.doi.org/10.1097/00001756-199406000-00012.

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Cooper, Emily A., and Allyson P. Mackey. "Sensory and cognitive plasticity: implications for academic interventions." Current Opinion in Behavioral Sciences 10 (August 2016): 21–27. http://dx.doi.org/10.1016/j.cobeha.2016.04.008.

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Mamassian, Pascal. "Sensory Plasticity: When Eye Movements Change Visual Appearance." Current Biology 26, no. 1 (January 2016): R24—R26. http://dx.doi.org/10.1016/j.cub.2015.11.008.

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Perez-Borrego, Y., V. Soto-Leon, J. Aguilar, G. Foffani, M. Rotondi, S. Bestmann, and A. Oliviero. "Studying plasticity of sensory function: insight from pregnancy." Experimental Brain Research 209, no. 2 (January 4, 2011): 311–16. http://dx.doi.org/10.1007/s00221-010-2532-8.

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Morrone, Maria Concetta. "Brain Development: Critical Periods for Cross-Sensory Plasticity." Current Biology 20, no. 21 (November 2010): R934—R936. http://dx.doi.org/10.1016/j.cub.2010.09.052.

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LeMessurier, Amy M., and Daniel E. Feldman. "Plasticity of population coding in primary sensory cortex." Current Opinion in Neurobiology 53 (December 2018): 50–56. http://dx.doi.org/10.1016/j.conb.2018.04.029.

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Ebner, F. F., V. Rema, R. Sachdev, and F. J. Symons. "Activity-Dependent Plasticity in Adult Somatic Sensory Cortex." Seminars in Neuroscience 9, no. 1-2 (1997): 47–58. http://dx.doi.org/10.1006/smns.1997.0105.

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Hokland, Jørn, and Beatrix Vereijken. "Can robots without Hebbian plasticity make good models of adaptive behaviour?" Behavioral and Brain Sciences 24, no. 6 (December 2001): 1060–62. http://dx.doi.org/10.1017/s0140525x01330121.

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No. Animals' primary problem is the shaping of movements, guided by and adapting to sensory signals. This requires a narrower class of biorobotic models than that spanned by Webb's dimensions and examples. We claim that all model variables and mechanisms must have real counterparts, input vectors must model known sensor fields, internal state vectors and transformations must model neurophysiological processes, and output vectors must model coordinated muscle signals.

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Witten, Ilana B., Eric I. Knudsen, and Haim Sompolinsky. "A Hebbian Learning Rule Mediates Asymmetric Plasticity in Aligning Sensory Representations." Journal of Neurophysiology 100, no. 2 (August 2008): 1067–79. http://dx.doi.org/10.1152/jn.00013.2008.

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In the brain, mutual spatial alignment across different sensory representations can be shaped and maintained through plasticity. Here, we use a Hebbian model to account for the synaptic plasticity that results from a displacement of the space representation for one input channel relative to that of another, when the synapses from both channels are equally plastic. Surprisingly, although the synaptic weights for the two channels obeyed the same Hebbian learning rule, the amount of plasticity exhibited by the respective channels was highly asymmetric and depended on the relative strength and width of the receptive fields (RFs): the channel with the weaker or broader RFs always exhibited most or all of the plasticity. A fundamental difference between our Hebbian model and most previous models is that in our model synaptic weights were normalized separately for each input channel, ensuring that the circuit would respond to both sensory inputs. The model produced three distinct regimes of plasticity dynamics (winner-take-all, mixed-shift, and no-shift), with the transition between the regimes depending on the size of the spatial displacement and the degree of correlation between the sensory channels. In agreement with experimental observations, plasticity was enhanced by the accumulation of incremental adaptive adjustments to a sequence of small displacements. These same principles would apply not only to the maintenance of spatial registry across input channels, but also to the experience-dependent emergence of aligned representations in developing circuits.

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Kraft, Andrew W., Adam Q. Bauer, Joseph P. Culver, and Jin-Moo Lee. "Sensory deprivation after focal ischemia in mice accelerates brain remapping and improves functional recovery through Arc-dependent synaptic plasticity." Science Translational Medicine 10, no. 426 (January 31, 2018): eaag1328. http://dx.doi.org/10.1126/scitranslmed.aag1328.

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Recovery after stroke, a major cause of adult disability, is often unpredictable and incomplete. Behavioral recovery is associated with functional reorganization (remapping) in perilesional regions, suggesting that promoting this process might be an effective strategy to enhance recovery. However, the molecular mechanisms underlying remapping after brain injury and the consequences of its modulation are poorly understood. Focal sensory loss or deprivation has been shown to induce remapping in the corresponding brain areas through activity-regulated cytoskeleton-associated protein (Arc)–mediated synaptic plasticity. We show that targeted sensory deprivation via whisker trimming in mice after induction of ischemic stroke in the somatosensory cortex representing forepaw accelerates remapping into the whisker barrel cortex and improves sensorimotor recovery. These improvements persisted even after focal sensory deprivation ended (whiskers allowed to regrow). Mice deficient in Arc, a gene critical for activity-dependent synaptic plasticity, failed to remap or recover sensorimotor function. These results indicate that post-stroke remapping occurs through Arc-mediated synaptic plasticity and is required for behavioral recovery. Furthermore, our findings suggest that enhancing perilesional cortical plasticity via focal sensory deprivation improves recovery after ischemic stroke in mice.

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Kilgard, M. P., J. L. Vazquez, N. D. Engineer, and P. K. Pandya. "Experience dependent plasticity alters cortical synchronization." Hearing Research 229, no. 1-2 (July 2007): 171–79. http://dx.doi.org/10.1016/j.heares.2007.01.005.

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Beckmann, Daniela, Mirko Feldmann, Olena Shchyglo, and Denise Manahan-Vaughan. "Hippocampal Synaptic Plasticity, Spatial Memory, and Neurotransmitter Receptor Expression Are Profoundly Altered by Gradual Loss of Hearing Ability." Cerebral Cortex 30, no. 8 (March 20, 2020): 4581–96. http://dx.doi.org/10.1093/cercor/bhaa061.

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Abstract Sensory information comprises the substrate from which memories are created. Memories of spatial sensory experience are encoded by means of synaptic plasticity in the hippocampus. Hippocampal dependency on sensory information is highlighted by the fact that sudden and complete loss of a sensory modality results in an impairment of hippocampal function that persists for months. Effects are accompanied by extensive changes in the expression of neurotransmitter receptors in cortex and hippocampus, consistent with a substantial adaptive reorganization of cortical function. Whether gradual sensory loss affects hippocampal function is unclear. Progressive age-dependent hearing loss (presbycusis) is a risk factor for cognitive decline. Here, we scrutinized C57BL/6 mice that experience hereditary and cumulative deafness starting in young adulthood. We observed that 2–4 months postnatally, increases in the cortical and hippocampal expression of GluN2A and GluN2B subunits of the N-methyl-D-aspartate receptor occurred compared to control mice that lack sensory deficits. Furthermore, GABA and metabotropic glutamate receptor expression were significantly altered. Hippocampal synaptic plasticity was profoundly impaired and mice exhibited significant deficits in spatial memory. These data show that during cortical adaptation to cumulative loss of hearing, plasticity-related neurotransmitter expression is extensively altered in the cortex and hippocampus. Furthermore, cumulative sensory loss compromises hippocampal function.

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Gainey, Melanie A., and Daniel E. Feldman. "Multiple shared mechanisms for homeostatic plasticity in rodent somatosensory and visual cortex." Philosophical Transactions of the Royal Society B: Biological Sciences 372, no. 1715 (March 5, 2017): 20160157. http://dx.doi.org/10.1098/rstb.2016.0157.

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We compare the circuit and cellular mechanisms for homeostatic plasticity that have been discovered in rodent somatosensory (S1) and visual (V1) cortex. Both areas use similar mechanisms to restore mean firing rate after sensory deprivation. Two time scales of homeostasis are evident, with distinct mechanisms. Slow homeostasis occurs over several days, and is mediated by homeostatic synaptic scaling in excitatory networks and, in some cases, homeostatic adjustment of pyramidal cell intrinsic excitability. Fast homeostasis occurs within less than 1 day, and is mediated by rapid disinhibition, implemented by activity-dependent plasticity in parvalbumin interneuron circuits. These processes interact with Hebbian synaptic plasticity to maintain cortical firing rates during learned adjustments in sensory representations. This article is part of the themed issue ‘Integrating Hebbian and homeostatic plasticity’.

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Journal articles on the topic 'Sensory plasticity'

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