Search results for " neurons"
showing 10 items of 290 documents
Salsolinol and ethanol-derived excitation of dopamine mesolimbic neurons: new insights
2013
Evidence supporting the essential role of brain-derived ethanol metabolites in the excitation of dopamine (DA) midbrain neurons has multiplied in the last 10–15 years. The pioneer and influential behavioral studies by CM Aragon and colleagues (see Correa et al., 2012 for a complete review) and more recent data (Sanchez-Catalan et al., 2009; Marti-Prats et al., 2010, 2013) have repeatedly demonstrated the crucial role displayed by acetaldehyde (ACD) in the locomotor and other behavioral responses elicited by ethanol. Although these experiments mainly used an indirect measure (exploratory locomotion) as an index of the excitation of DA neurons in the ventral tegmental area (VTA), results stro…
Mirror neurons : physiological requirement for emotional empathy and social and moral behaviour of human and nonhuman primates
2018
Fil: Claramonte Sanz, Vicente. Universitat de València. Departament de Filosofia. Área de Lògica i Filosofia de la Ciència; España. Claramonte Sanz, V. (2018). Las neuronas espejo: presupuesto fisiológico de la empatía emocional y de las conductas sociales y morales en primates humanos y no humanos. Metatheoria, 8(2), 15-22. A partir de la evidencia científica y los análisis debidos a los estudios de investigadores como Rizzolatti, Sinigaglia, Galle-se, Goldman, Iacoboni, etc., este artículo argumenta que el vínculo entre neuronas espejo y empatía emocional resulta corroborado por los estudios de Neurociencia sobre la actividad perceptiva y las reacciones emocionales. En particular, con las…
Derivatives and inverse of a linear-nonlinear multi-layer spatial vision model
2016
Linear-nonlinear transforms are interesting in vision science because they are key in modeling a number of perceptual experiences such as color, motion or spatial texture. Here we first show that a number of issues in vision may be addressed through an analytic expression of the Jacobian of these linear-nonlinear transforms. The particular model analyzed afterwards (an extension of [Malo & Simoncelli SPIE 2015]) is illustrative because it consists of a cascade of standard linear-nonlinear modules. Each module roughly corresponds to a known psychophysical mechanism: (1) linear spectral integration and nonlinear brightness-from-luminance computation, (2) linear pooling of local brightness…
Appropriate kernels for Divisive Normalization explained by Wilson-Cowan equations
2018
The interaction between wavelet-like sensors in Divisive Normalization is classically described through Gaussian kernels that decay with spatial distance, angular distance and frequency distance. However, simultaneous explanation of (a) distortion perception in natural image databases and (b) contrast perception of artificial stimuli requires very specific modifications in classical Divisive Normalization. First, the wavelet response has to be high-pass filtered before the Gaussian interaction is applied. Then, distinct weights per subband are also required after the Gaussian interaction. In summary, the classical Gaussian kernel has to be left- and right-multiplied by two extra diagonal ma…
A Fly-Inspired Mushroom Bodies Model for Sensory-Motor Control Through Sequence and Subsequence Learning
2016
Classification and sequence learning are relevant capabilities used by living beings to extract complex information from the environment for behavioral control. The insect world is full of examples where the presentation time of specific stimuli shapes the behavioral response. On the basis of previously developed neural models, inspired by Drosophila melanogaster, a new architecture for classification and sequence learning is here presented under the perspective of the Neural Reuse theory. Classification of relevant input stimuli is performed through resonant neurons, activated by the complex dynamics generated in a lattice of recurrent spiking neurons modeling the insect Mushroom Bodies n…
Sequence Learning in a Single Trial: A Spiking Neurons Model Based on Hippocampal Circuitry.
2020
ABSTRACTIn contrast with our everyday experience using brain circuits, it can take a prohibitively long time to train a computational system to produce the correct sequence of outputs in the presence of a series of inputs. This suggests that something important is missing in the way in which models are trying to reproduce basic cognitive functions. In this work, we introduce a new neuronal network architecture that is able to learn, in a single trial, an arbitrary long sequence of any known objects. The key point of the model is the explicit use of mechanisms and circuitry observed in the hippocampus, which allow the model to reach a level of efficiency and accuracy that, to the best of our…
On Contextuality in Behavioral Data
2015
Dzhafarov, Zhang, and Kujala (Phil. Trans. Roy. Soc. A 374, 20150099) reviewed several behavioral data sets imitating the formal design of the quantum-mechanical contextuality experiments. The conclusion was that none of these data sets exhibited contextuality if understood in the generalized sense proposed in Dzhafarov, Kujala, and Larsson (Found. Phys. 7, 762-782, 2015), while the traditional definition of contextuality does not apply to these data because they violate the condition of consistent connectedness (also known as marginal selectivity, no-signaling condition, no-disturbance principle, etc.). In this paper we clarify the relationship between (in)consistent connectedness and (non…
Multiscale Granger causality analysis by à trous wavelet transform
2017
Since interactions in neural systems occur across multiple temporal scales, it is likely that information flow will exhibit a multiscale structure, thus requiring a multiscale generalization of classical temporal precedence causality analysis like Granger's approach. However, the computation of multiscale measures of information dynamics is complicated by theoretical and practical issues such as filtering and undersampling: to overcome these problems, we propose a wavelet-based approach for multiscale Granger causality (GC) analysis, which is characterized by the following properties: (i) only the candidate driver variable is wavelet transformed (ii) the decomposition is performed using the…
Are simple striate cells analysers of visual signals both in spatial position as well as in spatial frequency?
1984
According to a modern view, simple cells of the cat striate cortex are considered to operate as apart of Fourier analysis system thus leading to the idea that the operational mechanism of the visual cortex is concerned with the analysis of spatial frequencies. Nevertheless if simple cells are really concerned only with the analysis of spatial frequencies there should exist a strict relationship between their spatial frequency selectivity and the spatial organization of their receptive fields. This is because it is the spatial organization of the spatial frequency detector i.e. the cell's receptive field that determines the cell's spatial frequency selectivity. Since the quantitative analysi…
An Information-Theoretic Framework to Measure the Dynamic Interaction between Neural Spike Trains
2021
Understanding the interaction patterns among simultaneous recordings of spike trains from multiple neuronal units is a key topic in neuroscience. However, an optimal approach of assessing these interactions has not been established, as existing methods either do not consider the inherent point process nature of spike trains or are based on parametric assumptions that may lead to wrong inferences if not met. This work presents a framework, grounded in the field of information dynamics, for the model-free, continuous-time estimation of both undirected (symmetric) and directed (causal) interactions between pairs of spike trains. The framework decomposes the overall information exchanged dynami…