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Modelling Trial-by-Trial Changes in the Mismatch Negativity

PLOS COMPUTATIONAL BIOLOGY 2013年第2期9卷 1-16页

作者： Lieder, Falk Daunizeau, Jean Garrido, Marta I. Friston, Karl J. Stephan, Klaas E. Univ Zurich Inst Biomed Engn TNU Zurich Switzerland ETH Zurich Switzerland Univ Zurich Lab Social & Neural Syst Res SNS Dept Econ Univ Zurich Hosp Zurich Switzerland UCL Wellcome Trust Ctr Neuroimaging Inst Neurol London England Hop La Pitie Salpetriere Brain & Spine Inst ICM Paris France Univ Queensland Queensland Brain Inst St Lucia Qld Australia

The mismatch negativity (MMN) is a differential brain response to violations of learned regularities. It has been used to demonstrate that the brain learns the statistical structure of its environment and predicts future sensory inputs. However, the algorithmic nature of these computations and the underlying neurobiological implementation remain controversial. This article introduces a mathematical framework with which competing ideas about the computational quantities indexed by MMN responses can be formalized and tested against single-trial EEG data. This framework was applied to five major theories of the MMN, comparing their ability to explain trial-by-trial changes in MMN amplitude. Three of these theories (predictive coding, model adjustment, and novelty detection) were formalized by linking the MMN to different manifestations of the same computational mechanism: approximate Bayesian inference according to the free-energy principle. We thereby propose a unifying view on three distinct theories of the MMN. The relative plausibility of each theory was assessed against empirical single-trial MMN amplitudes acquired from eight healthy volunteers in a roving oddball experiment. Models based on the free-energy principle provided more plausible explanations of trial-by-trial changes in MMN amplitude than models representing the two more traditional theories (change detection and adaptation). Our results suggest that the MMN reflects approximate Bayesian learning of sensory regularities, and that the MMN-generating process adjusts a probabilistic model of the environment according to prediction errors.

关键词： Sensory perception Electrode potentials Learning Electroencephalography Perception Perceptual learning coding mechanisms Mismatch negativity

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Rare Variants Association Analysis in Large-Scale Sequencing Studies at the Single Locus Level

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PLOS COMPUTATIONAL BIOLOGY 2016年第6期12卷 1-23页

作者： Jeng, Xinge Jessie Daye, Zhongyin John Lu, Wenbin Tzeng, Jung-Ying N Carolina State Univ Dept Stat Raleigh NC 27695 USA Univ Arizona Epidemiol & Biostat Tucson AZ USA N Carolina State Univ Bioinformat Res Ctr Raleigh NC 27695 USA Natl Cheng Kung Univ Dept Stat Tainan Taiwan

Genetic association analyses of rare variants in next-generation sequencing (NGS) studies are fundamentally challenging due to the presence of a very large number of candidate variants at extremely low minor allele frequencies. Recent developments often focus on pooling multiple variants to provide association analysis at the gene instead of the locus level. Nonetheless, pinpointing individual variants is a critical goal for genomic researches as such information can facilitate the precise delineation of molecular mechanisms and functions of genetic factors on diseases. Due to the extreme rarity of mutations and high-dimensionality, significances of causal variants cannot easily stand out from those of noncausal ones. Consequently, standard false-positive control procedures, such as the Bonferroni and false discovery rate (FDR), are often impractical to apply, as a majority of the causal variants can only be identified along with a few but unknown number of noncausal variants. To provide informative analysis of individual variants in large-scale sequencing studies, we propose the Adaptive False-Negative Control (AFNC) procedure that can include a large proportion of causal variants with high confidence by introducing a novel statistical inquiry to determine those variants that can be confidently dispatched as noncausal. The AFNC provides a general framework that can accommodate for a variety of models and significance tests. The procedure is computationally efficient and can adapt to the underlying proportion of causal variants and quality of significance rankings. Extensive simulation studies across a plethora of scenarios demonstrate that the AFNC is advantageous for identifying individual rare variants, whereas the Bonferroni and FDR are exceedingly over-conservative for rare variants association studies. In the analyses of the CoLaus dataset, AFNC has identified individual variants most responsible for gene-level significances. Moreover, single-variant results u

关键词： Biology and life sciences Cardiovascular diseases Cardiovascular medicine coding mechanisms Computational biology Computational neuroscience Genetic loci Genetics Genetics of disease Genome analysis Genome-wide association studies Genomics Human genetics Mathematical and statistical techniques Mathematics Medicine and health sciences Molecular biology Molecular biology techniques Neuroscience Next-generation sequencing Physical sciences Research and analysis methods Research Article Sequencing techniques Statistical methods Statistics (mathematics) Test statistics Transcriptome analysis

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Functional Roles for Synaptic-Depression within a Model of the Fly Antennal Lobe

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PLOS COMPUTATIONAL BIOLOGY 2012年第8期8卷 e1002622-e1002622页

作者： Rangan, Aaditya V. NYU Courant Inst Math Sci New York NY 10012 USA

Several experiments indicate that there exists substantial synaptic-depression at the synapses between olfactory receptor neurons (ORNs) and neurons within the drosophila antenna lobe (AL). This synaptic-depression may be partly caused by vesicle-depletion, and partly caused by presynaptic-inhibition due to the activity of inhibitory local neurons within the AL. While it has been proposed that this synaptic-depression contributes to the nonlinear relationship between ORN and projection neuron (PN) firing-rates, the precise functional role of synaptic-depression at the ORN synapses is not yet fully understood. In this paper we propose two hypotheses linking the information-coding properties of the fly AL with the network mechanisms responsible for ORN -> AL synaptic-depression. Our first hypothesis is related to variance coding of ORN firing-rate information - once stimulation to the ORNs is sufficiently high to saturate glomerular responses, further stimulation of the ORNs increases the regularity of PN spiking activity while maintaining PN firing-rates. The second hypothesis proposes a tradeoff between spike-time reliability and coding-capacity governed by the relative contribution of vesicle-depletion and presynaptic-inhibition to ORN -> AL synaptic-depression. Synaptic-depression caused primarily by vesicle-depletion will give rise to a very reliable system, whereas an equivalent amount of synaptic-depression caused primarily by presynaptic-inhibition will give rise to a less reliable system that is more sensitive to small shifts in odor stimulation. These two hypotheses are substantiated by several small analyzable toy models of the fly AL, as well as a more physiologically realistic large-scale computational model of the fly AL involving 5 glomerular channels.

关键词： Olfactory receptor neurons Neurons Synapses Neural networks Action potentials Network analysis Membrane potential coding mechanisms

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Cracking the neural code for word recognition in convolutional neural networks

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PLOS COMPUTATIONAL BIOLOGY 2024年第9期20卷 e1012430页

作者： Agrawal, Aakash Dehaene, Stanislas Univ Paris Saclay NeuroSpin Ctr INSERM U 992 Cognit Neuroimaging UnitCEA Gif Sur Yvette France Univ Paris Sci Lettres PSL Coll France Paris France

Learning to read places a strong challenge on the visual system. Years of expertise lead to a remarkable capacity to separate similar letters and encode their relative positions, thus distinguishing words such as FORM and FROM, invariantly over a large range of positions, sizes and fonts. How neural circuits achieve invariant word recognition remains unknown. Here, we address this issue by recycling deep neural network models initially trained for image recognition. We retrain them to recognize written words and then analyze how reading-specialized units emerge and operate across the successive layers. With literacy, a small subset of units becomes specialized for word recognition in the learned script, similar to the visual word form area (VWFA) in the human brain. We show that these units are sensitive to specific letter identities and their ordinal position from the left or the right of a word. The transition from retinotopic to ordinal position coding is achieved by a hierarchy of "space bigram" unit that detect the position of a letter relative to a blank space and that pool across low- and high-frequency-sensitive units from early layers of the network. The proposed scheme provides a plausible neural code for written words in the VWFA, and leads to predictions for reading behavior, error patterns, and the neurophysiology of reading. Reading is a fundamental skill in modern society, yet the neural mechanisms that allow us to quickly recognize words remain poorly understood. Our research aims to unravel how the brain achieves invariant word recognition-the ability to recognize words regardless of their position, size, or font. We studied artificial neural networks trained to recognize words, mirroring human learning. Our findings reveal that these networks develop specialized units for word recognition, similar to the Visual Word Form Area in the human brain. These units are sensitive to specific letters and their positions within a word. Crucially, we discovere

关键词： Visual system Word recognition coding mechanisms Neural networks Literacy Vision Multilingualism Functional magnetic resonance imaging

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Frequency-Invariant Representation of Interaural Time Differences in Mammals

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PLOS COMPUTATIONAL BIOLOGY 2011年第3期7卷 e1002013-e1002013页

作者： Lueling, Hannes Siveke, Ida Grothe, Benedikt Leibold, Christian Univ Munich Dept Biol 2 Planegg Martinsried Germany Bernstein Ctr Computat Neurosci Munich Planegg Martinsried Germany

Interaural time differences (ITDs) are the major cue for localizing low-frequency sounds. The activity of neuronal populations in the brainstem encodes ITDs with an exquisite temporal acuity of about 10 mu s. The response of single neurons, however, also changes with other stimulus properties like the spectral composition of sound. The influence of stimulus frequency is very different across neurons and thus it is unclear how ITDs are encoded independently of stimulus frequency by populations of neurons. Here we fitted a statistical model to single-cell rate responses of the dorsal nucleus of the lateral lemniscus. The model was used to evaluate the impact of single-cell response characteristics on the frequency-invariant mutual information between rate response and ITD. We found a rough correspondence between the measured cell characteristics and those predicted by computing mutual information. Furthermore, we studied two readout mechanisms, a linear classifier and a two-channel rate difference decoder. The latter turned out to be better suited to decode the population patterns obtained from the fitted model.

关键词： Neurons Single neuron function Brainstem Olives coding mechanisms Ears Psychophysics Support vector machines

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Optimal Learning Rules for Discrete Synapses

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PLOS COMPUTATIONAL BIOLOGY 2008年第11期4卷 e1000230-e1000230页

作者： Barrett, Adam B. van Rossum, M. C. W. Univ Edinburgh Inst Adapt & Neural Computat Edinburgh Midlothian Scotland

There is evidence that biological synapses have a limited number of discrete weight states. Memory storage with such synapses behaves quite differently from synapses with unbounded, continuous weights, as old memories are automatically overwritten by new memories. Consequently, there has been substantial discussion about how this affects learning and storage capacity. In this paper, we calculate the storage capacity of discrete, bounded synapses in terms of Shannon information. We use this to optimize the learning rules and investigate how the maximum information capacity depends on the number of synapses, the number of synaptic states, and the coding sparseness. Below a certain critical number of synapses per neuron (comparable to numbers found in biology), we find that storage is similar to unbounded, continuous synapses. Hence, discrete synapses do not necessarily have lower storage capacity.

关键词： Synapses Learning Memory Neurons coding mechanisms Cellular neuroscience Neuronal dendrites Synaptic plasticity

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Sensory noise predicts divisive reshaping of receptive fields

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PLOS COMPUTATIONAL BIOLOGY 2017年第6期13卷 e1005582-e1005582页

作者： Chalk, Matthew Masset, Paul Deneve, Sophie Gutkin, Boris IST Austria Klosterneuburg Austria Cold Spring Harbor Lab Dept Neurosci POB 100 Cold Spring Harbor NY 11724 USA Watson Sch Biol Sci Cold Spring Harbor NY 11724 USA Natl Res Univ Higher Sch Econ Ctr Cognit & Decis Making Moscow Russia PSL Univ Ecole Normale Super Dept Etud Cognit Grp Neural TheoryLNC INSERM U960 Paris France

In order to respond reliably to specific features of their environment, sensory neurons need to integrate multiple incoming noisy signals. Crucially, they also need to compete for the interpretation of those signals with other neurons representing similar features. The form that this competition should take depends critically on the noise corrupting these signals. In this study we show that for the type of noise commonly observed in sensory systems, whose variance scales with the mean signal, sensory neurons should selectively divide their input signals by their predictions, suppressing ambiguous cues while amplifying others. Any change in the stimulus context alters which inputs are suppressed, leading to a deep dynamic reshaping of neural receptive fields going far beyond simple surround suppression. Paradoxically, these highly variable receptive fields go alongside and are in fact required for an invariant representation of external sensory features. In addition to offering a normative account of context-dependent changes in sensory responses, perceptual inference in the presence of signal-dependent noise accounts for ubiquitous features of sensory neurons such as divisive normalization, gain control and contrast dependent temporal dynamics.

关键词： Animal cells Biology and life sciences Cell biology Cell signaling Cellular neuroscience Cellular types coding mechanisms Computational biology Computational neuroscience Computer and information sciences Neural networks Neuronal tuning Neurons Neuroscience Psychology Research Article Sensory cues Sensory neurons Sensory perception Sensory receptors Signal inhibition Signal transduction Social sciences

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A model for integrating elementary neural functions into delayed-response behavior

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PLOS COMPUTATIONAL BIOLOGY 2006年第4期2卷 211-227页

作者： Gisiger, Thomas Kerszberg, Michel Univ Paris 06 CNRS Inst Pasteur UMR 7138Recepteurs & Cognit Paris France Univ Paris 06 CNRS Modelisat Dynam Syst Integres UMR 7138 Paris France

It is well established that various cortical regions can implement a wide array of neural processes, yet the mechanisms which integrate these processes into behavior-producing, brain-scale activity remain elusive. We propose that an important role in this respect might be played by executive structures controlling the traffic of information between the cortical regions involved. To illustrate this hypothesis, we present a neural network model comprising a set of interconnected structures harboring stimulus-related activity ( visual representation, working memory, and planning), and a group of executive units with task- related activity patterns that manage the information flowing between them. The resulting dynamics allows the network to perform the dual task of either retaining an image during a delay (delayed-matching to sample task), or recalling from this image another one that has been associated with it during training (delayed-pair association task). The model reproduces behavioral and electrophysiological data gathered on the inferior temporal and prefrontal cortices of primates performing these same tasks. It also makes predictions on how neural activity coding for the recall of the image associated with the sample emerges and becomes prospective during the training phase. The network dynamics proves to be very stable against perturbations, and it exhibits signs of scale-invariant organization and cooperativity. The present network represents a possible neural implementation for active, top-down, prospective memory retrieval in primates. The model suggests that brain activity leading to performance of cognitive tasks might be organized in modular fashion, simple neural functions becoming integrated into more complex behavior by executive structures harbored in prefrontal cortex and/or basal ganglia.

关键词： Neurons Neural networks Memory recall Working memory Decision making coding mechanisms Behavior Signaling networks

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The effect of inhibition on rate code efficiency indicators

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PLOS COMPUTATIONAL BIOLOGY 2019年第12期15卷 e1007545-e1007545页

作者： Barta, Tomas Kostal, Lubomir Czech Acad Sci Inst Physiol Prague Czech Republic Charles Univ Prague Med Fac 1 Prague Czech Republic INRA Inst Ecol & Environm Sci Versailles France

Author summary Neurons communicate by firing action potentials, which can be considered as all-or-none events. The classical rate coding hypothesis states that neurons communicate the information about stimulus intensity by altering their firing frequency. Cortical neurons typically receive a signal from many different neurons, which, depending on the synapse type, either depolarize (excitatory input) or hyperpolarize (inhibitory input) the neural membrane. We use a neural model with excitatory and inhibitory synaptic conductances to reproduce in-vivo like activity and investigate how the intensity of presynaptic inhibitory activity affects the neuron's ability to transmit information through rate code. We reach a counter-intuitive result that increase in inhibition improves the signal-to-noise ratio of the neural response, despite introducing additional noise to the input signal. On the other hand, inhibition also limits the neuronal output range. However, in the end, the actual amount of information transmitted (in bits per energy expended) is remarkably robust to the inhibition level present in the system. Our approach also yields predictions in the form of post-synaptic firing rate histograms, which can be compared with in-vivo recordings. In this paper we investigate the rate coding capabilities of neurons whose input signal are alterations of the base state of balanced inhibitory and excitatory synaptic currents. We consider different regimes of excitation-inhibition relationship and an established conductance-based leaky integrator model with adaptive threshold and parameter sets recreating biologically relevant spiking regimes. We find that given mean post-synaptic firing rate, counter-intuitively, increased ratio of inhibition to excitation generally leads to higher signal to noise ratio (SNR). On the other hand, the inhibitory input significantly reduces the dynamic coding range of the neuron. We quantify the joint effect of SNR and dynamic coding range by

关键词： Neurons coding mechanisms Action potentials Membrane potential Probability distribution Synapses Signal to noise ratio Signal inhibition

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The primacy model and the structure of olfactory space

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PLOS COMPUTATIONAL BIOLOGY 2024年第9期20卷 e1012379页

作者： Giaffar, Hamza Shuvaev, Sergey Rinberg, Dmitry Koulakov, Alexei A. Cold Spring Harbor Lab Cold Spring Harbor NY 11724 USA NYU Neurosci Inst Langone Hlth New York NY 10012 USA NYU Ctr Neural Sci New York NY 10012 USA

Understanding sensory processing relies on the establishment of a consistent relationship between the stimulus space, its neural representation, and perceptual quality. In olfaction, the difficulty in establishing these links lies partly in the complexity of the underlying odor input space and perceptual responses. Based on the recently proposed primacy model for concentration invariant odor identity representation and a few assumptions, we have developed a theoretical framework for mapping the odor input space to the response properties of olfactory receptors. We analyze a geometrical structure containing odor representations in a multidimensional space of receptor affinities and describe its low-dimensional implementation, the primacy hull. We propose the implications of the primacy hull for the structure of feedforward connectivity in early olfactory networks. We test the predictions of our theory by comparing the existing receptor-ligand affinity and connectivity data obtained in the fruit fly olfactory system. We find that the Kenyon cells of the insect mushroom body integrate inputs from the high-affinity (primacy) sets of olfactory receptors in agreement with the primacy theory. Sensory systems face the problem of computing stimulus identity that is invariant with respect to multiple stimulus features. In the olfactory system, odorant percepts often retain their identity despite substantial variations in the odorant concentration. How can olfactory networks robustly represent odorant identity despite variable stimulus intensity? In the nose, odorants are sensed by the odorant receptors (ORs), the specialized proteins that can be activated by the odorant molecules. In this study, we develop a theory for encoding concentration-invariant odor objects. We propose that the identities of a small group of the most sensitive ORs represent odorant identity in a concentration-invariant manner. We argue that, although absolute values of olfactory receptor responses may

关键词： Odorants Olfactory receptors Olfactory system Neurons coding mechanisms Olfactory receptor neurons Sensory perception Principal component analysis

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