10,497 research outputs found
High frequency oscillations as a correlate of visual perception
“NOTICE: this is the author’s version of a work that was accepted for publication in International journal of psychophysiology. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in International journal of psychophysiology , 79, 1, (2011) DOI 10.1016/j.ijpsycho.2010.07.004Peer reviewedPostprin
Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information
Local field potentials (LFPs) reflect subthreshold integrative processes that complement spike train measures. However, little is yet known about the differences between how LFPs and spikes encode rich naturalistic sensory stimuli. We addressed this question by recording LFPs and spikes from the primary visual cortex of anesthetized macaques while presenting a color movie.Wethen determined
how the power of LFPs and spikes at different frequencies represents the visual features in the movie.Wefound that the most informative LFP frequency ranges were 1– 8 and 60 –100 Hz. LFPs in the range of 12– 40 Hz carried little information about the stimulus, and may primarily reflect neuromodulatory inputs. Spike power was informative only at frequencies <12 Hz. We further quantified “signal
correlations” (correlations in the trial-averaged power response to different stimuli) and “noise correlations” (trial-by-trial correlations in the fluctuations around the average) of LFPs and spikes recorded from the same electrode. We found positive signal correlation between high-gamma LFPs (60 –100 Hz) and spikes, as well as strong positive signal correlation within high-gamma LFPs, suggesting that high-gamma LFPs and spikes are generated within the same network. LFPs<24 Hz shared strong positive noise correlations, indicating that they are influenced by a common source, such as a diffuse neuromodulatory input. LFPs<40 Hz showed very little signal and noise correlations with LFPs>40Hzand with spikes, suggesting that low-frequency LFPs reflect neural processes that in natural conditions are fully decoupled from those giving rise to spikes and to high-gamma LFPs
Experience-driven formation of parts-based representations in a model of layered visual memory
Growing neuropsychological and neurophysiological evidence suggests that the
visual cortex uses parts-based representations to encode, store and retrieve
relevant objects. In such a scheme, objects are represented as a set of
spatially distributed local features, or parts, arranged in stereotypical
fashion. To encode the local appearance and to represent the relations between
the constituent parts, there has to be an appropriate memory structure formed
by previous experience with visual objects. Here, we propose a model how a
hierarchical memory structure supporting efficient storage and rapid recall of
parts-based representations can be established by an experience-driven process
of self-organization. The process is based on the collaboration of slow
bidirectional synaptic plasticity and homeostatic unit activity regulation,
both running at the top of fast activity dynamics with winner-take-all
character modulated by an oscillatory rhythm. These neural mechanisms lay down
the basis for cooperation and competition between the distributed units and
their synaptic connections. Choosing human face recognition as a test task, we
show that, under the condition of open-ended, unsupervised incremental
learning, the system is able to form memory traces for individual faces in a
parts-based fashion. On a lower memory layer the synaptic structure is
developed to represent local facial features and their interrelations, while
the identities of different persons are captured explicitly on a higher layer.
An additional property of the resulting representations is the sparseness of
both the activity during the recall and the synaptic patterns comprising the
memory traces.Comment: 34 pages, 12 Figures, 1 Table, published in Frontiers in
Computational Neuroscience (Special Issue on Complex Systems Science and
Brain Dynamics),
http://www.frontiersin.org/neuroscience/computationalneuroscience/paper/10.3389/neuro.10/015.2009
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fMRI correlates of subjective reversals in ambiguous structure-from-motion
We used fMRI to examine the neural correlates of subjective reversals for bistable structure-from-motion. We compared transparent random-dot kinematograms depicting either a cylinder rotating in depth or two flat surfaces translating in opposite directions at apparently different depths. For both such stimuli, the motion of dots on the different apparent depth planes typically appears to reverse direction periodically on prolonged viewing. Yet for cylindrical but not flat stimuli, such subjective reversals also coincide with apparent reversal of 3D rotation direction. We hypothesized that the lateral occipital complex (region LOC), sensitive to 3D form, might show greater event-related activity for subjective reversals of cylindrical than flat stimuli; conversely, motion-sensitive hMT+/V5 should respond in common to subjective reversals for either type of stimuli, as both are perceived as changes in planar motion. We obtained an event-related measure of neural activity associated with subjective reversals after first factoring out block-related differences between cylindrical versus flat stimuli (and thereby the associated low-level blocked stimulus differences). In support of our hypothesis, only the cylindrical stimuli produced reversal-related activity in contralateral human LOC. In contrast, the hMT+/V5 complex was activated alike by subjective reversals for both cylindrical and flat stimuli. Intriguingly, V1 also showed (contralateral) specificity for rotational reversals, suggesting a possible feedback influence from LOC. These results reveal specific neural correlates for subjective switches of 3D rotation versus translation, as distinct from subjective reversals in general
Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience
This essay is presented with two principal objectives in mind: first, to
document the prevalence of fractals at all levels of the nervous system, giving
credence to the notion of their functional relevance; and second, to draw
attention to the as yet still unresolved issues of the detailed relationships
among power law scaling, self-similarity, and self-organized criticality. As
regards criticality, I will document that it has become a pivotal reference
point in Neurodynamics. Furthermore, I will emphasize the not yet fully
appreciated significance of allometric control processes. For dynamic fractals,
I will assemble reasons for attributing to them the capacity to adapt task
execution to contextual changes across a range of scales. The final Section
consists of general reflections on the implications of the reviewed data, and
identifies what appear to be issues of fundamental importance for future
research in the rapidly evolving topic of this review
Feature detection using spikes: the greedy approach
A goal of low-level neural processes is to build an efficient code extracting
the relevant information from the sensory input. It is believed that this is
implemented in cortical areas by elementary inferential computations
dynamically extracting the most likely parameters corresponding to the sensory
signal. We explore here a neuro-mimetic feed-forward model of the primary
visual area (VI) solving this problem in the case where the signal may be
described by a robust linear generative model. This model uses an over-complete
dictionary of primitives which provides a distributed probabilistic
representation of input features. Relying on an efficiency criterion, we derive
an algorithm as an approximate solution which uses incremental greedy inference
processes. This algorithm is similar to 'Matching Pursuit' and mimics the
parallel architecture of neural computations. We propose here a simple
implementation using a network of spiking integrate-and-fire neurons which
communicate using lateral interactions. Numerical simulations show that this
Sparse Spike Coding strategy provides an efficient model for representing
visual data from a set of natural images. Even though it is simplistic, this
transformation of spatial data into a spatio-temporal pattern of binary events
provides an accurate description of some complex neural patterns observed in
the spiking activity of biological neural networks.Comment: This work links Matching Pursuit with bayesian inference by providing
the underlying hypotheses (linear model, uniform prior, gaussian noise
model). A parallel with the parallel and event-based nature of neural
computations is explored and we show application to modelling Primary Visual
Cortex / image processsing.
http://incm.cnrs-mrs.fr/perrinet/dynn/LaurentPerrinet/Publications/Perrinet04tau
Acetylcholine neuromodulation in normal and abnormal learning and memory: vigilance control in waking, sleep, autism, amnesia, and Alzheimer's disease
This article provides a unified mechanistic neural explanation of how learning, recognition, and cognition break down during Alzheimer's disease, medial temporal amnesia, and autism. It also clarifies whey there are often sleep disturbances during these disorders. A key mechanism is how acetylcholine modules vigilance control in cortical layer
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