1,721,161 research outputs found
Predictive Coding and Spike Timing Dependent Plasticity in Primary Visual Cortex
Full text linkThe aim of the present thesis is to elucidate whether the brain uses the statistical regularities of its environment to facilitate faster information processing and to investigate the mechanisms it uses to adapt to changes in these regularities. To this end, psychophysical studies in human and non-human primate are combined with electrophysiological single cell recordings from primary visual cortex (V1) of the macaque monkey. In the first part, we show that the flash-lag effect—a visual illusion in which the position of a predictably moving object is misjudged relative to that of a briefly flashed object—can also be observed in monkeys. By studying the responses of single neurons to moving and flashed bars, a potential mechanism put forward to explain the flash-lag effect, the differential latencies hypothesis, is rejected. In the second part, a potential mechanism underlying the dynamic reorganization of neural circuits in response to changing statistical regularities of their environment is examined. This mechanism, spike timing dependent plasticity, has been shown to induce “predictive shifts” of neural population responses after repeated presentation of the same stimulus sequences. We demonstrate that motion conditioning can induce such predictive shifts in the visual system of awake monkeys. In contrast to previous findings, however, they are specific to the exact nature of the stimulus (moving object) rather than being manifested in general receptive field shifts. This could be a sign of a principled mechanism by which circuits in early visual areas can undergo changes as a result of learning without at the same time getting disturbed under conditions
What's the Signal in the Noise?
No full textResponses of cortical neurons are highly variable. Even repeated presentations of the same visual stimulus never elicit the same spike train. Identifying the origins of this variability remains a challenge. There is increasing evidence that it is not just noise arising from stochastic features of neuronal architecture, but at least partly represents meaningful top-down signals. One of the most prominent examples of such top-down modulation in the visual system is covert attention. I will present both theoretical and experimental results showing that trial-totrial fluctuations of attentional state contribute significantly to response variability in primary visual cortex of awake, behaving monkeys. I will argue that much can be learned about information processing in the brain by using latent variable models of neuronal activity to help us identify and account for cognitive variables and make sense of single-trial neural population data
What's the Signal in the Noise?
No full textResponses of cortical neurons are highly variable. Even repeated presentations of the same visual stimulus never elicit the same spike train. Identifying the origins of this variability remains a challenge. There is increasing evidence that it is not just noise arising from stochastic features of neuronal architecture, but at least partly represents meaningful top-down signals. One of the most prominent examples of such top-down modulation in the visual system is covert attention. I will present both theoretical and experimental results showing that trial-totrial fluctuations of attentional state contribute significantly to response variability in primary visual cortex of awake, behaving monkeys. I will argue that much can be learned about information processing in the brain by using latent variable models of neuronal activity to help us identify and account for cognitive variables and make sense of single-trial neural population data
Predictive Coding and Spike Timing Dependent Plasticity in Primary Visual Cortex
No full textThe aim of the present thesis is to elucidate whether the brain uses the statistical regularities of its environment to facilitate faster information processing and to investigate the mechanisms it uses to adapt to changes in these regularities. To this end, psychophysical studies in human and non-human primate are combined with electrophysiological single cell recordings from primary visual cortex (V1) of the macaque monkey. In the first part, we show that the flash-lag effect—a visual illusion in which the position of a predictably moving object is misjudged relative to that of a briefly flashed object—can also be observed in monkeys. By studying the responses of single neurons to moving and flashed bars, a potential mechanism put forward to explain the flash-lag effect, the differential latencies hypothesis, is rejected. In the second part, a potential mechanism underlying the dynamic reorganization of neural circuits in response to changing statistical regularities of their environment is examined. This mechanism, spike timing dependent plasticity, has been shown to induce “predictive shifts” of neural population responses after repeated presentation of the same stimulus sequences. We demonstrate that motion conditioning can induce such predictive shifts in the visual system of awake monkeys. In contrast to previous findings, however, they are specific to the exact nature of the stimulus (moving object) rather than being manifested in general receptive field shifts. This could be a sign of a principled mechanism by which circuits in early visual areas can undergo changes as a result of learning without at the same time getting disturbed under conditions
Mixed latent variable model of attention in V1
No full textNeurons show a high degree of variability of spike trains, even in responses to identical stimuli. This variability is often correlated between neurons of one population, however, the sources of the correlation remain unknown. According to one hypothesis, inter-trial fluctuation of an attentional signal can induce noise correlation [Cohen Newsome 2008, Ecker et al. 2016]. To test this hypothesis in the primary visual cortex, we designed a novel cued change detection task in which attentional fluctuations are modulated across trials. We trained two monkeys to maintain fixation and to make a saccade toward coherent gratings among a series of two Gabor patches with randomly changing orientations presented simultaneously in the left and right visual field. The monkeys learned to attend either to the stimulus on one side or to both stimuli (Fig. 1 A, B). To track the attentional state on a single-trial basis, we developed a model that multiplicatively accounts for the stimulus-driven variability of spikes and shared latent fluctuations of an attentional signal. The model describes the neuronal responses as a product of a stimulus response, attentional cue, slow drift, and shared latent variables (Fig. 1 C). The first two components are assumed to capture attentional modulation of the mean neuronal gain («classical» model of attention [Maunsell Treue, 2006]). The slow modulator accounts for potential drift of individual neurons’ firing rates throughout the recording session and is modeled by a Gaussian process across trials [Rabinowitz et al., 2015]. The shared attentional modulators are also assumed to be smooth, but with a faster timescale, and their within-trial
Mixed latent variable model of attention in V1
No full textNeurons show a high degree of variability of spike trains, even in responses to identical stimuli. This variability is often correlated between neurons of one population, however, the sources of the correlation remain unknown. According to one hypothesis, inter-trial fluctuation of an attentional signal can induce noise correlation [Cohen Newsome 2008, Ecker et al. 2016]. To test this hypothesis in the primary visual cortex, we designed a novel cued change detection task in which attentional fluctuations are modulated across trials. We trained two monkeys to maintain fixation and to make a saccade toward coherent gratings among a series of two Gabor patches with randomly changing orientations presented simultaneously in the left and right visual field. The monkeys learned to attend either to the stimulus on one side or to both stimuli (Fig. 1 A, B). To track the attentional state on a single-trial basis, we developed a model that multiplicatively accounts for the stimulus-driven variability of spikes and shared latent fluctuations of an attentional signal. The model describes the neuronal responses as a product of a stimulus response, attentional cue, slow drift, and shared latent variables (Fig. 1 C). The first two components are assumed to capture attentional modulation of the mean neuronal gain («classical» model of attention [Maunsell Treue, 2006]). The slow modulator accounts for potential drift of individual neurons’ firing rates throughout the recording session and is modeled by a Gaussian process across trials [Rabinowitz et al., 2015]. The shared attentional modulators are also assumed to be smooth, but with a faster timescale, and their within-trial
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Information Coding in the Variance of Neural Activity
No full textNeural activity in the cortex appears to be notoriously noisy. A widely accepted explanation for this finding is that excitatory and inhibitory inputs to downstream neurons are balanced in a way that the upstream population activity does not affect the mean but only the variance of the input current. This can be thought of as a multiplicative noise channel. However, the capacity limits imposed by this information channel are not known. Here we develop a general understanding of the encoding process in terms of scale mixture processes and derive information-theoretic bounds on their performance. Our results show that signal transmission via instantaneous changes in the variance can behave quite differently from the common additive noise channel. We perform systematic numerical analyses to maximize the information across the variance channel and thus obtain tight lower bounds to its capacity. Furthermore, we found that additional noise, resembling the unreliable synaptic transmission of spikes, can surprisingly enhance the coding performance of the channel
Tailor-Designed Models for the Turbulent Velocity Gradient through Normalizing Flow
Full text linkSmall-scale turbulence can be comprehensively described in terms of velocity gradients, which makes them an appealing starting point for low-dimensional modeling. Typical models consist of stochastic equations based on closures for nonlocal pressure and viscous contributions. The fidelity of the resulting models depends on the accuracy of the underlying modeling assumptions. Here, we discuss an alternative data-driven approach leveraging machine learning to derive a velocity gradient model which captures its statistics by construction. We use a normalizing flow to learn the velocity gradient probability density function (PDF) from direct numerical simulation (DNS) of incompressible turbulence. Then, by using the equation for the single-time PDF of the velocity gradient, we construct a deterministic, yet chaotic, dynamical system featuring the learned steady-state PDF by design. Finally, utilizing gauge terms for the velocity gradient single-time statistics, we optimize the time correlations as obtained from our model against the DNS data. As a result, the model time realizations resemble the time series from DNS statistically closely. Published by the American Physical Society 2024European Research Council http://dx.doi.org/10.13039/501100000781Horizon 2020 Framework Programme http://dx.doi.org/10.13039/100010661Friedrich-Alexander-Universität Erlangen-Nürnberg http://dx.doi.org/10.13039/501100001652Max-Planck-Gesellschaft http://dx.doi.org/10.13039/501100004189Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/50110000165
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