62 research outputs found
Comparison of LFP-Based and Spike-Based Spectro-Temporal Receptive Fields and Cross-Correlation in Cat Primary Auditory Cortex
Multi-electrode array recordings of spike and local field potential (LFP) activity were made from primary auditory cortex of 12 normal hearing, ketamine-anesthetized cats. We evaluated 259 spectro-temporal receptive fields (STRFs) and 492 frequency-tuning curves (FTCs) based on LFPs and spikes simultaneously recorded on the same electrode. We compared their characteristic frequency (CF) gradients and their cross-correlation distances. The CF gradient for spike-based FTCs was about twice that for 2â40 Hz-filtered LFP-based FTCs, indicating greatly reduced frequency selectivity for LFPs. We also present comparisons for LFPs band-pass filtered between 4â8 Hz, 8â16 Hz and 16â40 Hz, with spike-based STRFs, on the basis of their marginal frequency distributions. We find on average a significantly larger correlation between the spike based marginal frequency distributions and those based on the 16â40 Hz filtered LFP, compared to those based on the 4â8 Hz, 8â16 Hz and 2â40 Hz filtered LFP. This suggests greater frequency specificity for the 16â40 Hz LFPs compared to those of lower frequency content. For spontaneous LFP and spike activity we evaluated 1373 pair correlations for pairs with >200 spikes in 900 s per electrode. Peak correlation-coefficient space constants were similar for the 2â40 Hz filtered LFP (5.5 mm) and the 16â40 Hz LFP (7.4 mm), whereas for spike-pair correlations it was about half that, at 3.2 mm. Comparing spike-pairs with 2â40 Hz (and 16â40 Hz) LFP-pair correlations showed that about 16% (9%) of the variance in the spike-pair correlations could be explained from LFP-pair correlations recorded on the same electrodes within the same electrode array. This larger correlation distance combined with the reduced CF gradient and much broader frequency selectivity suggests that LFPs are not a substitute for spike activity in primary auditory cortex
BIOFRAG: A new database for analysing BIOdiversity responses to forest FRAGmentation
Habitat fragmentation studies are producing inconsistent and complex results across which it is nearly impossible to synthesise. Consistent analytical techniques can be applied to primary datasets, if stored in a flexible database that allows simple data retrieval for subsequent analyses. Method: We developed a relational database linking data collected in the field to taxonomic nomenclature, spatial and temporal plot attributes and further environmental variables (e.g. information on biogeographic region. Typical field assessments include measures of biological variables (e.g. presence, abundance, ground cover) of one species or a set of species linked to a set of plots in fragments of a forested landscape. Conclusion: The database currently holds records of 5792 unique species sampled in 52 landscapes in six of eight biogeographic regions: mammals 173, birds 1101, herpetofauna 284, insects 2317, other arthropods: 48, plants 1804, snails 65. Most species are found in one or two landscapes, but some are found in four. Using the huge amount of primary data on biodiversity response to fragmentation becomes increasingly important as anthropogenic pressures from high population growth and land demands are increasing. This database can be queried to extract data for subsequent analyses of the biological response to forest fragmentation with new metrics that can integrate across the components of fragmented landscapes. Meta-analyses of findings based on consistent methods and metrics will be able to generalise over studies allowing inter-comparisons for unified answers. The database can thus help researchers in providing findings for analyses of trade-offs between land use benefits and impacts on biodiversity and to track performance of management for biodiversity conservation in human-modified landscapes.Fil: Pfeifer, Marion. Imperial College London; Reino UnidoFil: Lefebvre, Veronique. Imperial College London; Reino UnidoFil: Gardner, Toby A.. Stockholm Environment Institute; SueciaFil: Arroyo RodrĂguez, VĂctor. Universidad Nacional AutĂłnoma de MĂ©xico; MĂ©xicoFil: Baeten, Lander. University of Ghent; BĂ©lgicaFil: Banks Leite, Cristina. Imperial College London; Reino UnidoFil: Barlow, Jos. Lancaster University; Reino UnidoFil: Betts, Matthew G.. State University of Oregon; Estados UnidosFil: Brunet, Joerg. Swedish University of Agricultural Sciences; SueciaFil: Cerezo BlandĂłn, Alexis Mauricio. Universidad de Buenos Aires. Facultad de AgronomĂa. Departamento de MĂ©todos Cuantitativos y Sistemas de InformaciĂłn; ArgentinaFil: Cisneros, Laura M.. University of Connecticut; Estados UnidosFil: Collard, Stuart. Nature Conservation Society of South Australia; AustraliaFil: DÂŽCruze, Neil. The World Society for the Protection of Animals; Reino UnidoFil: Da Silva Motta, Catarina. MinistĂ©rio da CiĂȘncia, Tecnologia, InovaçÔes. Instituto Nacional de Pesquisas da AmazĂŽnia; BrasilFil: Duguay, Stephanie. Carleton University; CanadĂĄFil: Eggermont, Hilde. University of Ghent; BĂ©lgicaFil: Eigenbrod, FĂ©lix. University of Southampton; Reino UnidoFil: Hadley, Adam S.. State University of Oregon; Estados UnidosFil: Hanson, Thor R.. No especifĂca;Fil: Hawes, Joseph E.. University of East Anglia; Reino UnidoFil: Heartsill Scalley, Tamara. United State Department of Agriculture. Forestry Service; Puerto RicoFil: Klingbeil, Brian T.. University of Connecticut; Estados UnidosFil: Kolb, Annette. Universitat Bremen; AlemaniaFil: Kormann, Urs. UniversitĂ€t Göttingen; AlemaniaFil: Kumar, Sunil. State University of Colorado - Fort Collins; Estados UnidosFil: Lachat, Thibault. Swiss Federal Institute for Forest; SuizaFil: Lakeman Fraser, Poppy. Imperial College London; Reino UnidoFil: Lantschner, MarĂa Victoria. Consejo Nacional de Investigaciones CientĂficas y TĂ©cnicas. Centro CientĂfico TecnolĂłgico Conicet - BahĂa Blanca; Argentina. Instituto Nacional de TecnologĂa Agropecuaria. Centro Regional Patagonia Norte. EstaciĂłn Experimental Agropecuaria San Carlos de Bariloche; ArgentinaFil: Laurance, William F.. James Cook University; AustraliaFil: Leal, Inara R.. Universidade Federal de Pernambuco; BrasilFil: Lens, Luc. University of Ghent; BĂ©lgicaFil: Marsh, Charles J.. University of Leeds; Reino UnidoFil: Medina Rangel, Guido F.. Universidad Nacional de Colombia; ColombiaFil: Melles, Stephanie. University of Toronto; CanadĂĄFil: Mezger, Dirk. Field Museum of Natural History; Estados UnidosFil: Oldekop, Johan A.. University of Sheffield; Reino UnidoFil: Overal , Williams L.. Museu Paraense EmĂlio Goeldi. Departamento de Entomologia; BrasilFil: Owen, Charlotte. Imperial College London; Reino UnidoFil: Peres, Carlos A.. University of East Anglia; Reino UnidoFil: Phalan, Ben. University of Southampton; Reino UnidoFil: Pidgeon, Anna Michle. University of Wisconsin; Estados UnidosFil: Pilia, Oriana. Imperial College London; Reino UnidoFil: Possingham, Hugh P.. Imperial College London; Reino Unido. The University Of Queensland; AustraliaFil: Possingham, Max L.. No especifĂca;Fil: Raheem, Dinarzarde C.. Royal Belgian Institute of Natural Sciences; BĂ©lgica. Natural History Museum; Reino UnidoFil: Ribeiro, Danilo B.. Universidade Federal do Mato Grosso do Sul; BrasilFil: Ribeiro Neto, Jose D.. Universidade Federal de Pernambuco; BrasilFil: Robinson, Douglas W.. State University of Oregon; Estados UnidosFil: Robinson, Richard. Manjimup Research Centre; AustraliaFil: Rytwinski, Trina. Carleton University; CanadĂĄFil: Scherber, Christoph. UniversitĂ€t Göttingen; AlemaniaFil: Slade, Eleanor M.. University of Oxford; Reino UnidoFil: Somarriba, Eduardo. Centro AgronĂłmico Tropical de InvestigaciĂłn y Enseñanza; Costa RicaFil: Stouffer, Philip C.. State University of Louisiana; Estados UnidosFil: Struebig, Matthew J.. University of Kent; Reino UnidoFil: Tylianakis, Jason M.. University College London; Estados Unidos. Imperial College London; Reino UnidoFil: Teja, Tscharntke. UniversitĂ€t Göttingen; AlemaniaFil: Tyre, Andrew J.. Universidad de Nebraska - Lincoln; Estados UnidosFil: Urbina Cardona, Jose N.. Pontificia Universidad Javeriana; ColombiaFil: Vasconcelos, Heraldo L.. Universidade Federal de Uberlandia; BrasilFil: Wearn, Oliver. Imperial College London; Reino Unido. The Zoological Society of London; Reino UnidoFil: Wells, Konstans. University of Adelaide; AustraliaFil: Willig, Michael R.. University of Connecticut; Estados UnidosFil: Wood, Eric. University of Wisconsin; Estados UnidosFil: Young, Richard P.. Durrell Wildlife Conservation Trust; Reino UnidoFil: Bradley, Andrew V.. Imperial College London; Reino UnidoFil: Ewers, Robert M.. Imperial College London; Reino Unid
BIOFRAG - a new database for analyzing BIOdiversity responses to forest FRAGmentation
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BIOFRAG â a new database for analyzing BIOdiversity responses to forest FRAGmentation
Habitat fragmentation studies have produced complex results that are challenging
to synthesize. Inconsistencies among studies may result from variation in
the choice of landscape metrics and response variables, which is often compounded
by a lack of key statistical or methodological information. Collating
primary datasets on biodiversity responses to fragmentation in a consistent and
flexible database permits simple data retrieval for subsequent analyses. We present
a relational database that links such field data to taxonomic nomenclature,
spatial and temporal plot attributes, and environmental characteristics. Field
assessments include measurements of the response(s) (e.g., presence, abundance,
ground cover) of one or more species linked to plots in fragments
within a partially forested landscape. The database currently holds 9830 unique
species recorded in plots of 58 unique landscapes in six of eight realms: mammals
315, birds 1286, herptiles 460, insects 4521, spiders 204, other arthropods
85, gastropods 70, annelids 8, platyhelminthes 4, Onychophora 2, vascular
plants 2112, nonvascular plants and lichens 320, and fungi 449. Three landscapes
were sampled as long-term time series (>10 years). Seven hundred and
eleven species are found in two or more landscapes. Consolidating the substantial
amount of primary data available on biodiversity responses to fragmentation
in the context of land-use change and natural disturbances is an essential
part of understanding the effects of increasing anthropogenic pressures on land.
The consistent format of this database facilitates testing of generalizations concerning
biologic responses to fragmentation across diverse systems and taxa. It
also allows the re-examination of existing datasets with alternative landscape
metrics and robust statistical methods, for example, helping to address pseudo-replication
problems. The database can thus help researchers in producing
broad syntheses of the effects of land use. The database is dynamic and inclusive,
and contributions from individual and large-scale data-collection efforts
are welcome.Keywords: Species turnover,
Data sharing,
Database,
Global change,
Landscape metrics,
Edge effects,
Forest fragmentation,
Matrix contrast,
Bioinformatic
Maladaptive Neural Synchrony in Tinnitus: Origin and Restoration
Tinnitus is the conscious perception of sound heard in the absence of physical sound sources external or internal to the body, reflected in aberrant neural synchrony of spontaneous or resting-state brain activity. Neural synchrony is generated by the nearly simultaneous firing of individual neurons, of the synchronization of membrane-potential changes in local neural groups as reflected in the local field potentials, resulting in the presence of oscillatory brain waves in the EEG. Noise-induced hearing loss, often resulting in tinnitus, causes a reorganization of the tonotopic map in auditory cortex and increased spontaneous firing rates and neural synchrony. Spontaneous brain rhythms rely on neural synchrony. Abnormal neural synchrony in tinnitus appears to be confined to specific frequency bands of brain rhythms. Increases in delta-band activity are generated by deafferented/deprived neuronal networks resulting from hearing loss. Coordinated reset (CR) stimulation was developed in order to specifically counteract such abnormal neuronal synchrony by desynchronization. The goal of acoustic CR neuromodulation is to desynchronize tinnitus-related abnormal delta-band oscillations. CR neuromodulation does not require permanent stimulus delivery in order to achieve long-lasting desynchronization or even a full-blown anti-kindling but may have cumulative effects, i.e., the effect of different CR epochs separated by pauses may accumulate. Unlike other approaches, acoustic CR neuromodulation does not intend to reduce tinnitus-related neuronal activity by employing lateral inhibition. The potential efficacy of acoustic CR modulation was shown in a clinical proof of concept trial, where effects achieved in 12âweeks of treatment delivered 4-6âh/day persisted through a preplanned 4-week therapy pause and showed sustained long-term effects after 10âmonths of therapy, leading to 75% responders
On the similarities and differences of non-traumatic sound exposure during the critical period and in adulthood.
There is an almost dogmatic view of the different effects of moderate-level sound stimulation in neonatal vs. adult animals. It is often stated that exposure in neonates results in an expansion of the cortical area that responds to the frequencies present in the sound, being either pure tones or frequency modulated sounds. In contrast, recent findings on stimulating adult animals for a sufficiently long time with similar sounds show a contraction of the cortical region responding to those sounds. In this review I will suggest that most neonatal animal results have been wrongly interpreted (albeit generally not by the original authors) and that the changes caused in the critical period and in adulthood are very similar. Thus, the mechanisms leading to the cortical map changes appear to be similar in the critical period and in adulthood. Despite this similarity, the changes induced in the critical period are generally permanent (unless extensive training paradigms to revert the changes are involved), whereas in adults a slow recovery (months) to pre-exposure conditions takes place
Animal models of spontaneous activity in the healthy and impaired auditory system
Spontaneous neural activity in the auditory nerve fibers and in auditory cortex in healthy animals is discussed with respect to the question: Is spontaneous activity noise or information carrier? The studies reviewed suggest strongly that spontaneous activity is a carrier of information. Subsequently, I review the numerous findings in the impaired auditory system, particularly with reference to noise trauma and tinnitus. Here the common assumption is that tinnitus reflects increased noise in the auditory system that among others affects temporal processing and interferes with the gap-startle reflex, which is frequently used as a behavioral assay for tinnitus. It is, however, more likely that the increased spontaneous activity in tinnitus, firing rate as well as neural synchrony, carries information that shapes the activity of downstream structures, including non-auditory ones, and leading to the tinnitus percept. The main drivers of that process are bursting and synchronous firing, which facilitates transfer of activity across synapses, and allows formation of auditory objects, such as tinnitu
Sound-Induced Synchronization of Neural Activity Between and Within Three Auditory Cortical Areas
Representation of Spectral and Temporal Sound Features in Three Cortical Fields of the Cat. Similarities Outweigh Differences
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