1,720,992 research outputs found
Correlatos neuronales de la percepción y la memoria visual: Registro de neuronas individuales corticales en humanos
No full textEn el 2005, se anunció un descubrimiento de enorme repercusión: neuronas que representan conceptos y participan de la formación de memorias, llamadas “neuronas de Jennifer Aniston”, porque en el experimento se utilizaron imágenes de la actriz. En los pacientes con epilepsia refractaria a fármacos, está indicado el tratamiento quirúrgico, situación que ofrece una excepcional oportunidad para investigar las funciones cognitivas en el cerebro humano y, al mismo tiempo, estudiar cómo influye el proceso epileptógeno sobre dichas funciones.Fil: Gori, María Belén. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; ArgentinaFil: Rey, Hernán. No especifíca;Fil: Collavini, Santiago. Universidad Nacional de La Plata. Facultad de Ingeniería. Departamento de Electrotecnia. Laboratorio de Electrónica Industrial, Control e Instrumentación; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Munera, Claudia. No especifíca;Fil: Chaure, Fernando Julián. No especifíca;Fil: Fernandez Lima, Monica Lorena. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia "Prof. Eduardo de Robertis". Universidad de Buenos Aires. Facultad de Medicina. Instituto de Biología Celular y Neurociencia; ArgentinaFil: Seoane, Pablo. No especifíca;Fil: Seoane, Eduardo. No especifíca;Fil: Quian Quiroga, Rodrigo. No especifíca;Fil: Kochen, Silvia. No especifíca
Looking for a face in the crowd: Fixation-related potentials in an eye-movement visual search task
Get PDFDespite the compelling contribution of the study of event related potentials (ERPs) and eye movements to cognitive neuroscience, these two approaches have largely evolved independently. We designed an eye-movement visual search paradigm that allowed us to concurrently record EEG and eye movements while subjects were asked to find a hidden target face in a crowded scene with distractor faces. Fixation event-related potentials (fERPs) to target and distractor stimuli showed the emergence of robust sensory components associated with the perception of stimuli and cognitive components associated with the detection of target faces. We compared those components with the ones obtained in a control task at fixation: qualitative similarities as well as differences in terms of scalp topography and latency emerged between the two. By using single trial analyses, fixations to target and distractors could be decoded from the EEG signals above chance level in 11 out of 12 subjects. Our results show that EEG signatures related to cognitive behavior develop across spatially unconstrained exploration of natural scenes and provide a first step towards understanding the mechanisms of target detection during natural search.Fil: Kaunitz, Lisandro N.. University of Leicester; Reino UnidoFil: Kamienkowski, Juan Esteban. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Neurociencia Integrativa; Argentina. Universidad Diego Portales; Chile;Fil: Varatharajah, Alexander. University of Leicester; Reino UnidoFil: Sigman, Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Bahía Blanca. Instituto de Física del Sur; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Física. Laboratorio de Neurociencia Integrativa; ArgentinaFil: Quian Quiroga, Rodrigo. University of Leicester; Reino UnidoFil: Ison, Matias Julian. University of Leicester; Reino Unid
Single-trial event-related potentials with wavelet denoising
No full textThe application of a recently proposed denoising implementation for obtaining event-related potentials (ERPs) at the single-trial level is shown. We study its performance in simulated data as well as in visual and auditory ERPs. For the simulated data, the method gives a significantly better reconstruction of the single-trial event-related responses in comparison with the original data and also in comparison with a reconstruction based on conventional Wiener filtering. Moreover, with wavelet denoising we obtain a significantly better estimation of the amplitudes and latencies of the simulated ERPs.
For the real data, the method clearly improves the visualization of both visual and auditory single-trial ERPs. This allows the calculation of better averages as well as the study of systematic or unsystematic variations between trials. Since the method is fast and parameter free, it could complement the conventional analysis of ERPs
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Measuring sparseness in the brain: Comment on Bowers (2009).
No full textBowers (2009) challenged the common view in favor of distributed representations in psychological
modeling and the main arguments given against localist and grandmother cell coding schemes. He
revisited the results of several single-cell studies, arguing that they do not support distributed representations.
We praise the contribution of Bowers (2009) for joining evidence from psychological modeling
and neurophysiological recordings, but we disagree with several of his claims. In this comment, we argue
that distinctions between distributed, localist, and grandmother cell coding can be troublesome with real
data. Moreover, these distinctions seem to be lying within the same continuum, and we argue that it may
be sensible to characterize coding schemes with a sparseness measure. We further argue that there may
not be a unique coding scheme implemented in all brain areas and for all possible functions. In particular,
current evidence suggests that the brain may use distributed codes in primary sensory areas and sparser
and invariant representations in higher areas.Version of Recor
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Extracting information from the shape and spatial distribution of evoked potentials
No full textBackground Over 90 years after its first recording, scalp electroencephalography (EEG) remains one of the most widely used techniques in human neuroscience research, in particular for the study of event-related potentials (ERPs). However, because of its low signal-to-noise ratio, extracting useful information from these signals continues to be a hard-technical challenge. Many studies focus on simple properties of the ERPs such as peaks, latencies, and slopes of signal deflections. New method To overcome these limitations, we developed the Wavelet-Information method which uses wavelet decomposition, information theory, and a quantification based on single-trial decoding performance to extract information from evoked responses. Results Using simulations and real data from four experiments, we show that the proposed approach outperforms standard supervised analyses based on peak amplitude estimation. Moreover, the method can extract information using the raw data from all recorded channels using no a priori knowledge or pre-processing steps. Comparison with existing method(s) We show that traditional approaches often disregard important features of the signal such as the shape of EEG waveforms. Also, other approaches often require some form of a priori knowledge for feature selection and lead to problems of multiple comparisons. Conclusions This approach offers a new and complementary framework to design experiments that go beyond the traditional analyses of ERPs. Potentially, it allows a wide usage beyond basic research; such as for clinical diagnosis, brain-machine interfaces, and neurofeedback applications requiring single-trial analyses.Fil: Lopes-dos-Santos, Vítor. Universidade Federal do Rio Grande do Norte; Brasil. University of Leicester; Reino UnidoFil: Rey, Hernan G.. University of Leicester; Reino UnidoFil: Navajas Ahumada, Joaquin Mariano. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. University of Leicester; Reino Unido. University College London; Estados UnidosFil: Quian Quiroga, Rodrigo. University of Leicester; Reino Unid
Event synchronization: A simple and fast method to measure synchronicity and time delay patterns
Get PDFWe propose a simple method to measure synchronization and time-delay patterns between signals. It is based on the relative timings of events in the time series, defined, e.g., as local maxima. The degree of synchronization is obtained from the number of quasisimultaneous appearances of events, and the delay is calculated from the precedence of events in one signal with respect to the other. Moreover, we can easily visualize the time evolution of the delay and synchronization level with an excellent resolution. We apply the algorithm to short rat electroencephalogram (EEG) signals, some of them containing spikes. We also apply it to an intracranial human EEG recording containing an epileptic seizure, and we propose that the method might be useful for the detection of epileptic foci. It can be easily extended to other types of data and it is very simple and fast, thus being suitable for on-line implementations
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