Relevant bibliographies by topics / Neuronal coding and decoding

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Neuronal coding and decoding'

Author: Grafiati
Published: 14 March 2023

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Journal articles on the topic "Neuronal coding and decoding"

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Wu, Si, Hiroyuki Nakahara, and Shun-ichi Amari. "Population Coding with Correlation and an Unfaithful Model." Neural Computation 13, no. 4 (April 1, 2001): 775–97. http://dx.doi.org/10.1162/089976601300014349.

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This study investigates a population decoding paradigm in which the maximum likelihood inference is based on an unfaithful decoding model (UMLI). This is usually the case for neural population decoding because the encoding process of the brain is not exactly known or because a simplified decoding model is preferred for saving computational cost. We consider an unfaithful decoding model that neglects the pair-wise correlation between neuronal activities and prove that UMLI is asymptotically efficient when the neuronal correlation is uniform or of limited range. The performance of UMLI is compared with that of the maximum likelihood inference based on the faithful model and that of the center-of-mass decoding method. It turns out that UMLI has advantages of decreasing the computational complexity remarkably and maintaining high-leveldecoding accuracy. Moreover, it can be implemented by a biologically feasible recurrent network (Pouget, Zhang, Deneve, & Latham, 1998). The effect of correlation on the decoding accuracy is also discussed.
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Fang, Huijuan, Yongji Wang, and Jiping He. "Spiking Neural Networks for Cortical Neuronal Spike Train Decoding." Neural Computation 22, no. 4 (April 2010): 1060–85. http://dx.doi.org/10.1162/neco.2009.10-08-885.

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Recent investigation of cortical coding and computation indicates that temporal coding is probably a more biologically plausible scheme used by neurons than the rate coding used commonly in most published work. We propose and demonstrate in this letter that spiking neural networks (SNN), consisting of spiking neurons that propagate information by the timing of spikes, are a better alternative to the coding scheme based on spike frequency (histogram) alone. The SNN model analyzes cortical neural spike trains directly without losing temporal information for generating more reliable motor command for cortically controlled prosthetics. In this letter, we compared the temporal pattern classification result from the SNN approach with results generated from firing-rate-based approaches: conventional artificial neural networks, support vector machines, and linear regression. The results show that the SNN algorithm can achieve higher classification accuracy and identify the spiking activity related to movement control earlier than the other methods. Both are desirable characteristics for fast neural information processing and reliable control command pattern recognition for neuroprosthetic applications.
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Koyama, Shinsuke. "On the Relation Between Encoding and Decoding of Neuronal Spikes." Neural Computation 24, no. 6 (June 2012): 1408–25. http://dx.doi.org/10.1162/neco_a_00279.

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Neural coding is a field of study that concerns how sensory information is represented in the brain by networks of neurons. The link between external stimulus and neural response can be studied from two parallel points of view. The first, neural encoding, refers to the mapping from stimulus to response. It focuses primarily on understanding how neurons respond to a wide variety of stimuli and constructing models that accurately describe the stimulus-response relationship. Neural decoding refers to the reverse mapping, from response to stimulus, where the challenge is to reconstruct a stimulus from the spikes it evokes. Since neuronal response is stochastic, a one-to-one mapping of stimuli into neural responses does not exist, causing a mismatch between the two viewpoints of neural coding. Here we use these two perspectives to investigate the question of what rate coding is, in the simple setting of a single stationary stimulus parameter and a single stationary spike train represented by a renewal process. We show that when rate codes are defined in terms of encoding, that is, the stimulus parameter is mapped onto the mean firing rate, the rate decoder given by spike counts or the sample mean does not always efficiently decode the rate codes, but it can improve efficiency in reading certain rate codes when correlations within a spike train are taken into account.
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Bethge, M., D. Rotermund, and K. Pawelzik. "Optimal Short-Term Population Coding: When Fisher Information Fails." Neural Computation 14, no. 10 (October 1, 2002): 2317–51. http://dx.doi.org/10.1162/08997660260293247.

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Efficient coding has been proposed as a first principle explaining neuronal response properties in the central nervous system. The shape of optimal codes, however, strongly depends on the natural limitations of the particular physical system. Here we investigate how optimal neuronal encoding strategies are influenced by the finite number of neurons N (place constraint), the limited decoding time window length T (time constraint), the maximum neuronal firing rate fmax (power constraint), and the maximal average rate fmax (energy constraint). While Fisher information provides a general lower bound for the mean squared error of unbiased signal reconstruction, its use to characterize the coding precision is limited. Analyzing simple examples, we illustrate some typical pitfalls and thereby show that Fisher information provides a valid measure for the precision of a code only if the dynamic range (fmin T, fmax T) is sufficiently large. In particular, we demonstrate that the optimal width of gaussian tuning curves depends on the available decoding time T. Within the broader class of unimodal tuning functions, it turns out that the shape of a Fisher-optimal coding scheme is not unique. We solve this ambiguity by taking the minimum mean square error into account, which leads to flat tuning curves. The tuning width, however, remains to be determined by energy constraints rather than by the principle of efficient coding.
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Curreli, Sebastiano, Jacopo Bonato, Sara Romanzi, Stefano Panzeri, and Tommaso Fellin. "Complementary encoding of spatial information in hippocampal astrocytes." PLOS Biology 20, no. 3 (March 3, 2022): e3001530. http://dx.doi.org/10.1371/journal.pbio.3001530.

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Calcium dynamics into astrocytes influence the activity of nearby neuronal structures. However, because previous reports show that astrocytic calcium signals largely mirror neighboring neuronal activity, current information coding models neglect astrocytes. Using simultaneous two-photon calcium imaging of astrocytes and neurons in the hippocampus of mice navigating a virtual environment, we demonstrate that astrocytic calcium signals encode (i.e., statistically reflect) spatial information that could not be explained by visual cue information. Calcium events carrying spatial information occurred in topographically organized astrocytic subregions. Importantly, astrocytes encoded spatial information that was complementary and synergistic to that carried by neurons, improving spatial position decoding when astrocytic signals were considered alongside neuronal ones. These results suggest that the complementary place dependence of localized astrocytic calcium signals may regulate clusters of nearby synapses, enabling dynamic, context-dependent variations in population coding within brain circuits.
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Lottem, Eran, Erez Gugig, and Rony Azouz. "Parallel coding schemes of whisker velocity in the rat's somatosensory system." Journal of Neurophysiology 113, no. 6 (March 15, 2015): 1784–99. http://dx.doi.org/10.1152/jn.00485.2014.

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The function of rodents' whisker somatosensory system is to transform tactile cues, in the form of vibrissa vibrations, into neuronal responses. It is well established that rodents can detect numerous tactile stimuli and tell them apart. However, the transformation of tactile stimuli obtained through whisker movements to neuronal responses is not well-understood. Here we examine the role of whisker velocity in tactile information transmission and its coding mechanisms. We show that in anaesthetized rats, whisker velocity is related to the radial distance of the object contacted and its own velocity. Whisker velocity is accurately and reliably coded in first-order neurons in parallel, by both the relative time interval between velocity-independent first spike latency of rapidly adapting neurons and velocity-dependent first spike latency of slowly adapting neurons. At the same time, whisker velocity is also coded, although less robustly, by the firing rates of slowly adapting neurons. Comparing first- and second-order neurons, we find similar decoding efficiencies for whisker velocity using either temporal or rate-based methods. Both coding schemes are sufficiently robust and hardly affected by neuronal noise. Our results suggest that whisker kinematic variables are coded by two parallel coding schemes and are disseminated in a similar way through various brain stem nuclei to multiple brain areas.
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Doron, Guy, and Michael Brecht. "What single-cell stimulation has told us about neural coding." Philosophical Transactions of the Royal Society B: Biological Sciences 370, no. 1677 (September 19, 2015): 20140204. http://dx.doi.org/10.1098/rstb.2014.0204.

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In recent years, single-cell stimulation experiments have resulted in substantial progress towards directly linking single-cell activity to movement and sensation. Recent advances in electrical recording and stimulation techniques have enabled control of single neuron spiking in vivo and have contributed to our understanding of neuronal coding schemes in the brain. Here, we review single neuron stimulation effects in different brain structures and how they vary with artificially inserted spike patterns. We briefly compare single neuron stimulation with other brain stimulation techniques. A key advantage of single neuron stimulation is the precise control of the evoked spiking patterns. Systematically varying spike patterns and measuring evoked movements and sensations enables ‘decoding’ of the single-cell spike patterns and provides insights into the readout mechanisms of sensory and motor cortical spikes.
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Hartig, Renée, David Wolf, Michael J. Schmeisser, and Wolfgang Kelsch. "Genetic influences of autism candidate genes on circuit wiring and olfactory decoding." Cell and Tissue Research 383, no. 1 (January 2021): 581–95. http://dx.doi.org/10.1007/s00441-020-03390-8.

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AbstractOlfaction supports a multitude of behaviors vital for social communication and interactions between conspecifics. Intact sensory processing is contingent upon proper circuit wiring. Disturbances in genetic factors controlling circuit assembly and synaptic wiring can lead to neurodevelopmental disorders, such as autism spectrum disorder (ASD), where impaired social interactions and communication are core symptoms. The variability in behavioral phenotype expression is also contingent upon the role environmental factors play in defining genetic expression. Considering the prevailing clinical diagnosis of ASD, research on therapeutic targets for autism is essential. Behavioral impairments may be identified along a range of increasingly complex social tasks. Hence, the assessment of social behavior and communication is progressing towards more ethologically relevant tasks. Garnering a more accurate understanding of social processing deficits in the sensory domain may greatly contribute to the development of therapeutic targets. With that framework, studies have found a viable link between social behaviors, circuit wiring, and altered neuronal coding related to the processing of salient social stimuli. Here, the relationship between social odor processing in rodents and humans is examined in the context of health and ASD, with special consideration for how genetic expression and neuronal connectivity may regulate behavioral phenotypes.
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Manwani, Amit, Peter N. Steinmetz, and Christof Koch. "The Impact of Spike Timing Variability on the Signal-Encoding Performance of Neural Spiking Models." Neural Computation 14, no. 2 (February 1, 2002): 347–67. http://dx.doi.org/10.1162/08997660252741158.

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It remains unclear whether the variability of neuronal spike trains in vivo arises due to biological noise sources or represents highly precise encoding of temporally varying synaptic input signals. Determining the variability of spike timing can provide fundamental insights into the nature of strategies used in the brain to represent and transmit information in the form of discrete spike trains. In this study, we employ a signal estimation paradigm to determine how variability in spike timing affects encoding of random time-varying signals. We assess this for two types of spiking models: an integrate-and-fire model with random threshold and a more biophysically realistic stochastic ion channel model. Using the coding fraction and mutual information as information-theoretic measures, we quantify the efficacy of optimal linear decoding of random inputs from the model outputs and study the relationship between efficacy and variability in the output spike train. Our findings suggest that variability does not necessarily hinder signal decoding for the biophysically plausible encoders examined and that the functional role of spiking variability depends intimately on the nature of the encoder and the signal processing task; variability can either enhance or impede decoding performance.
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Zeldenrust, Fleur, Boris Gutkin, and Sophie Denéve. "Efficient and robust coding in heterogeneous recurrent networks." PLOS Computational Biology 17, no. 4 (April 30, 2021): e1008673. http://dx.doi.org/10.1371/journal.pcbi.1008673.

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Cortical networks show a large heterogeneity of neuronal properties. However, traditional coding models have focused on homogeneous populations of excitatory and inhibitory neurons. Here, we analytically derive a class of recurrent networks of spiking neurons that close to optimally track a continuously varying input online, based on two assumptions: 1) every spike is decoded linearly and 2) the network aims to reduce the mean-squared error between the input and the estimate. From this we derive a class of predictive coding networks, that unifies encoding and decoding and in which we can investigate the difference between homogeneous networks and heterogeneous networks, in which each neurons represents different features and has different spike-generating properties. We find that in this framework, ‘type 1’ and ‘type 2’ neurons arise naturally and networks consisting of a heterogeneous population of different neuron types are both more efficient and more robust against correlated noise. We make two experimental predictions: 1) we predict that integrators show strong correlations with other integrators and resonators are correlated with resonators, whereas the correlations are much weaker between neurons with different coding properties and 2) that ‘type 2’ neurons are more coherent with the overall network activity than ‘type 1’ neurons.
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Dissertations / Theses on the topic "Neuronal coding and decoding"

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Tripathy, Shreejoy J. "Understanding the Form and Function of Neuronal Physiological Diversity." Research Showcase @ CMU, 2013. http://repository.cmu.edu/dissertations/318.

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For decades electrophysiologists have recorded and characterized the biophysical properties of a rich diversity of neuron types. This diversity of neuron types is critical for generating functionally important patterns of brain activity and implementing neural computations. In this thesis, I developed computational methods towards quantifying neuron diversity and applied these methods for understanding the functional implications of within-type neuron variability and across-type neuron diversity. First, I developed a means for defining the functional role of differences among neurons of the same type. Namely, I adapted statistical neuron models, termed generalized linear models, to precisely capture how the membranes of individual olfactory bulb mitral cells transform afferent stimuli to spiking responses. I then used computational simulations to construct virtual populations of biophysically variable mitral cells to study the functional implications of within-type neuron variability. I demonstrate that an intermediate amount of intrinsic variability enhances coding of noisy afferent stimuli by groups of biophysically variable mitral cells. These results suggest that within-type neuron variability, long considered to be a disadvantageous consequence of biological imprecision, may serve a functional role in the brain. Second, I developed a methodology for quantifying the rich electrophysiological diversity across the majority of the neuron types throughout the mammalian brain. Using semi-automated text-mining, I built a database, Neuro- Electro, of neuron type specific biophysical properties extracted from the primary research literature. This data is available at http://neuroelectro.org, which provides a publicly accessible interface where this information can be viewed. Though the extracted physiological data is highly variable across studies, I demonstrate that knowledge of article-specific experimental conditions can significantly explain the observed variance. By applying simple analyses to the dataset, I find that there exist 5-7 major neuron super-classes which segregate on the basis of known functional roles. Moreover, by integrating the NeuroElectro dataset with brain-wide gene expression data from the Allen Brain Atlas, I show that biophysically-based neuron classes correlate highly with patterns of gene expression among voltage gated ion channels and neurotransmitters. Furthermore, this work lays the conceptual and methodological foundations for substantially enhanced data sharing in neurophysiological investigations in the future.
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Romero, Arandia Iñigo. "Reading out neural populations: shared variability, global fluctuations and information processing." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/404684.

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Entendre l'origen i la funció de l'activitat de poblacions neuronals, i com aquesta activitat es relaciona amb els estímuls sensorials, les decisions o les accions motores és un gran repte per les neurociències. En aquest treball hem analitzat l'activitat de desenes de neurones enregistrades a l'escorça visual primària de micos mentre se'ls presentaven escletxes sinusoïdals en diferents orientacions. Hem trobat que les fluctuacions globals de la xarxa mesurades mitjançant l'activitat de la població modulen la selectivitat de les neurones de forma multiplicativa i additiva. A més, l'activitat de la població també afecta la informació present en grups petits de neurones, depenent de la modulació que ha provocat a la selectivitat d'aquestes. La informació de la població sencera, però, no canvia amb aquestes fluctuacions. A la segona part hem desenvolupat un mètode per mesurar 'correlacions diferencials' amb dades limitades. En aplicar-ho a les dades experimentals hem aconseguit la primera estimació preliminar de la grandària d'aquestes correlacions que limiten la informació. Els nostres resultats contribueixen a l'avenç en la comprensió de la codi ficació d'informació en poblacions neuronals, i alhora generen noves preguntes sobre com aquestes processen i transmeten informació.
Entender el origen y la función de la actividad de poblaciones neuronales, y cómo esta actividad se relaciona con los estímulos sensoriales, las decisiones o las acciones motoras es un gran desafio en neurociencia. En este trabajo hemos analizado la actividad de decenas de neuronas registradas en la corteza visual primaria de monos mientras rejillas sinusoidales en diferentes orientaciones eran presentadas. Hemos encontrado que las fluctuaciones globales de la red medidas mediante la actividad de la población modulan la selectividad de las neuronas de manera multiplicativa y aditiva. Además, la actividad de la población también afecta a la información presente en grupos pequeños de neuronas, dependiendo de la modulación que ha provocado en la selectividad de estas neuronas. La información en la población completa, sin embargo, no varía con estas fluctuaciones. En la segunda parte hemos desarrollado un método para medir 'correlaciones diferenciales' con datos limitados. Al aplicarlo a los datos experimentales hemos obtenido la primera estimación preliminar del tamaño de estas correlaciones que limitan la información. Nuestros resultados contribuyen al avance del entendimiento sobre la codi ficación de la información en poblaciones neuronales, y al mismo tiempo generan más preguntas sobre cómo éstas procesan y transmiten información.
Understanding the sources and the role of the spiking activity of neural populations, and how this activity is related to sensory stimuli, decisions or motor actions is a crucial challenge in neuroscience. In this work, we analyzed the spiking activity of tens of neurons recorded in the primary visual cortex of macaque monkeys while drifting sinusoidal gratings were presented in di erent orientations. We found that global uctuations of the network measured by the population activity a ect the tuning of individual neurons both multiplicatively and additively. Population activity also has an impact in the information of small ensembles, which depends on the kind of modulation that the tuning of those neurons undergoes. Interestingly, the total information of the network is not altered by these uctuations. In the second part, we developed a method to measure 'di erential correlations' from limited amount of data, and obtained the rst, although preliminary, estimate in experimental data. Our results have important implications for information coding, and they open new questions about the way information is processed and transmitted by the spiking activity of neural populations.
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Iwaza, Lana, and Lana Iwaza. "Joint Source-Network Coding & Decoding." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00855787.

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While network data transmission was traditionally accomplished via routing, network coding (NC) broke this rule by allowing network nodes to perform linear combinations of the upcoming data packets. Network operations are performed in a specific Galois field of fixed size q. Decoding only involves a Gaussian elimination with the received network-coded packets. However, in practical wireless environments, NC might be susceptible to transmission errors caused by noise, fading, or interference. This drawback is quite problematic for real-time applications, such as multimediacontent delivery, where timing constraints may lead to the reception of an insufficient number of packets and consequently to difficulties in decoding the transmitted sources. At best, some packets can be recovered, while in the worst case, the receiver is unable to recover any of the transmitted packets.In this thesis, we propose joint source-network coding and decoding schemes in the purpose of providing an approximate reconstruction of the source in situations where perfect decoding is not possible. The main motivation comes from the fact that source redundancy can be exploited at the decoder in order to estimate the transmitted packets, even when some of them are missing. The redundancy can be either natural, i.e, already existing, or artificial, i.e, externally introduced.Regarding artificial redundancy, we choose multiple description coding (MDC) as a way of introducing structured correlation among uncorrelated packets. By combining MDC and NC, we aim to ensure a reconstruction quality that improves gradually with the number of received network-coded packets. We consider two different approaches for generating descriptions. The first technique consists in generating multiple descriptions via a real-valued frame expansion applied at the source before quantization. Data recovery is then achieved via the solution of a mixed integerlinear problem. The second technique uses a correlating transform in some Galois field in order to generate descriptions, and decoding involves a simple Gaussian elimination. Such schemes are particularly interesting for multimedia contents delivery, such as video streaming, where quality increases with the number of received descriptions.Another application of such schemes would be multicasting or broadcasting data towards mobile terminals experiencing different channel conditions. The channel is modeled as a binary symmetric channel (BSC) and we study the effect on the decoding quality for both proposed schemes. Performance comparison with a traditional NC scheme is also provided.Concerning natural redundancy, a typical scenario would be a wireless sensor network, where geographically distributed sources capture spatially correlated measures. We propose a scheme that aims at exploiting this spatial redundancy, and provide an estimation of the transmitted measurement samples via the solution of an integer quadratic problem. The obtained reconstruction quality is compared with the one provided by a classical NC scheme.
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4

Iwaza, Lana. "Joint Source-Network Coding & Decoding." Thesis, Paris 11, 2013. http://www.theses.fr/2013PA112048/document.

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Dans les réseaux traditionnels, la transmission de flux de données s'effectuaient par routage des paquets de la source vers le ou les destinataires. Le codage réseau (NC) permet aux nœuds intermédiaires du réseau d'effectuer des combinaisons linéaires des paquets de données qui arrivent à leurs liens entrants. Les opérations de codage ont lieu dans un corps de Galois de taille finie q. Aux destinataires, le décodage se fait par une élimination de Gauss des paquets codés-réseau reçus. Cependant, dans les réseaux sans fils, le codage réseau doit souvent faire face à des erreurs de transmission causées par le bruit, les effacements, et les interférences. Ceci est particulièrement problématique pour les applications temps réel, telle la transmission de contenus multimédia, où les contraintes en termes de délais d'acheminement peuvent aboutir à la réception d'un nombre insuffisant de paquets, et par conséquent à des difficultés à décoder les paquets transmis. Dans le meilleurs des cas, certains paquets arrivent à être décodés. Dans le pire des cas, aucun paquet ne peut être décodé.Dans cette thèse, nous proposons des schémas de codage conjoint source-réseau dont l'objectif est de fournir une reconstruction approximative de la source, dans des situations où un décodage parfait est impossible. L'idée consiste à exploiter la redondance de la source au niveau du décodeur afin d'estimer les paquets émis, même quand certains de ces paquets sont perdus après avoir subi un codage réseau. La redondance peut être soit naturelle, c'est-à-dire déjà existante, ou introduite de manière artificielle.Concernant la redondance artificielle, le codage à descriptions multiples (MDC) est choisi comme moyen d'introduire de la redondance structurée entre les paquets non corrélés. En combinant le codage à descriptions multiples et le codage réseau, nous cherchons à obtenir une qualité de reconstruction qui s'améliore progressivement avec le nombre de paquets codés-réseau reçus.Nous considérons deux approches différentes pour générer les descriptions. La première approche consiste à générer les descriptions par une expansion sur trame appliquée à la source avant la quantification. La reconstruction de données se fait par la résolution d'un problème d' optimisation quadratique mixte. La seconde technique utilise une matrice de transformée dans un corps de Galois donné, afin de générer les descriptions, et le décodage se fait par une simple éliminationde Gauss. Ces schémas sont particulièrement intéressants dans un contexte de transmission de contenus multimédia, comme le streaming vidéo, où la qualité s'améliore avec le nombre de descriptions reçues.Une seconde application de tels schémas consiste en la diffusion de données vers des terminaux mobiles à travers des canaux de transmission dont les conditions sont variables. Dans ce contexte, nous étudions la qualité de décodage obtenue pour chacun des deux schémas de codage proposés, et nous comparons les résultats obtenus avec ceux fournis par un schéma de codage réseau classique.En ce qui concerne la redondance naturelle, un scénario typique est celui d'un réseau de capteurs, où des sources géographiquement distribuées prélèvent des mesures spatialement corrélées. Nous proposons un schéma dont l'objectif est d'exploiter cette redondance spatiale afin de fournir une estimation des échantillons de mesures transmises par la résolution d'un problème d'optimisation quadratique à variables entières. La qualité de reconstruction est comparée à celle obtenue à travers un décodage réseau classique
While network data transmission was traditionally accomplished via routing, network coding (NC) broke this rule by allowing network nodes to perform linear combinations of the upcoming data packets. Network operations are performed in a specific Galois field of fixed size q. Decoding only involves a Gaussian elimination with the received network-coded packets. However, in practical wireless environments, NC might be susceptible to transmission errors caused by noise, fading, or interference. This drawback is quite problematic for real-time applications, such as multimediacontent delivery, where timing constraints may lead to the reception of an insufficient number of packets and consequently to difficulties in decoding the transmitted sources. At best, some packets can be recovered, while in the worst case, the receiver is unable to recover any of the transmitted packets.In this thesis, we propose joint source-network coding and decoding schemes in the purpose of providing an approximate reconstruction of the source in situations where perfect decoding is not possible. The main motivation comes from the fact that source redundancy can be exploited at the decoder in order to estimate the transmitted packets, even when some of them are missing. The redundancy can be either natural, i.e, already existing, or artificial, i.e, externally introduced.Regarding artificial redundancy, we choose multiple description coding (MDC) as a way of introducing structured correlation among uncorrelated packets. By combining MDC and NC, we aim to ensure a reconstruction quality that improves gradually with the number of received network-coded packets. We consider two different approaches for generating descriptions. The first technique consists in generating multiple descriptions via a real-valued frame expansion applied at the source before quantization. Data recovery is then achieved via the solution of a mixed integerlinear problem. The second technique uses a correlating transform in some Galois field in order to generate descriptions, and decoding involves a simple Gaussian elimination. Such schemes are particularly interesting for multimedia contents delivery, such as video streaming, where quality increases with the number of received descriptions.Another application of such schemes would be multicasting or broadcasting data towards mobile terminals experiencing different channel conditions. The channel is modeled as a binary symmetric channel (BSC) and we study the effect on the decoding quality for both proposed schemes. Performance comparison with a traditional NC scheme is also provided.Concerning natural redundancy, a typical scenario would be a wireless sensor network, where geographically distributed sources capture spatially correlated measures. We propose a scheme that aims at exploiting this spatial redundancy, and provide an estimation of the transmitted measurement samples via the solution of an integer quadratic problem. The obtained reconstruction quality is compared with the one provided by a classical NC scheme
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5

Mendl, Christian. "Neuronal coding in the retina." Diss., lmu, 2012. http://nbn-resolving.de/urn:nbn:de:bvb:19-139015.

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Rossoni, Enrico. "Synchronization and decoding in spiking neuronal networks." Thesis, University of Sussex, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.443995.

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姍, 路., and Shan Lu. "Coding and decoding for multiuser communication systems." Thesis, https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB12863897/?lang=0, 2014. https://doors.doshisha.ac.jp/opac/opac_link/bibid/BB12863897/?lang=0.

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本論文は、多端子通信路に対するマルチユーザ符号化および復号の研究成果をまとめたものである。多重接続加算通信路による情報伝送において、複数ユーザの稼働状態を識別するための誤り訂正可能なシグネチャ符号の構成を論ず,全伝送率の高いシグネチャ符号の一般的な構成法を解明する.双方向中継通信路では、2ユーザターボ符号に対する復号の演算量を低減させる復号法を提案する。加法性白色ガウス雑音環境下では復号性能を劣化することなく、レイリーフェージング環境下では僅かな劣化にとどめながら、復号の演算量を約半分程度に低減することができる.
Coding and decoding for multiuser communication systems are investigated. For MAAC, we propose a coding scheme of (k + 1)-ary error-correcting signature codes. We give binary and non-binary signature codes. They are the best error-correcting signature codes for MAAC, in the sense that they have highest sum rates known. For TWRC, we propose a low-complexity two-user turbo decoding scheme when turbo codes are applied in two users. The approximate decoding algorithm preserves low decoding complexity over the Gaussian TWRC, without much performance degradation.
博士(工学)
Doctor of Philosophy in Engineering
同志社大学
Doshisha University
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8

Meyer, Linda. "Coding and Decoding of Reed-Muller Codes." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-84009.

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In this thesis some families of linear error correcting codes are presented. The reader will find a general description of binary codes and more specific details about linear codes such as Hamming, repetition codes, Reed-Muller codes, etc. To fully immerse ourselves in the methods of coding and decoding, we will introduce examples in order to contribute to the understanding of the theories.   In these times of much communication through computer technology, our daily lives involve a substantial amount of data transmission. It is essential that these data are transmitted without errors through the communication channels. Therefore, the scientific field of error-correcting codes holds a significant importance in many aspects of todays society.   The main goal of this thesis is to study linear block codes which belong to the class of binary codes. In this case we will attribute a more prominent role to first order Reed-Muller codes.
I den här uppsatsen kommer flera varianter av linjära felrättande koder att presenteras. Läsaren får ta del av en allmän beskrivning av binära koder och en mer detaljerad framställning av linjära koder så som Hamming, repetitionskod, Reed-Muller kod m.m. Tillsammans med en fördjupning i ämnet, avseende metoder för kodning och avkodning, kommer vi att ge exempel för att bidra till förståelsen.   Den digitala eran, som vi lever i, innefattar att datatransmission är en del av vår vardag. Vår frekventa användning av mobila enheter visar på hur viktigt det är att data överförs korrekt via kommunikationskanalerna. Av den anledningen är vetenskapen om felrättande koder högaktuell i dagens samhälle.   Det huvudsakliga syftet med uppsatsen är att studera linjära block-koder som tillhör klassen binära koder. I det här fallet kommer vi att fokusera lite extra på Reed-Muller koder av första ordningen.
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Rice, Mark. "Decoding of cyclic block codes." Thesis, University of Manchester, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.330207.

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Báez, Mendoza Raymundo. "Neuronal coding of reward during social interactions." Thesis, University of Cambridge, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.648675.

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Books on the topic "Neuronal coding and decoding"

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Howard, Eichenbaum, and Davis Joel L. 1942-, eds. Neuronal ensembles: Strategies for recording and decoding. New York: Wiley-Liss, 1998.

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Tomlinson, Martin, Cen Jung Tjhai, Marcel A. Ambroze, Mohammed Ahmed, and Mubarak Jibril. Error-Correction Coding and Decoding. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-51103-0.

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Fan, John L. Constrained Coding and Soft Iterative Decoding. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1525-8.

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Fan, John L. Constrained Coding and Soft Iterative Decoding. Boston, MA: Springer US, 2001.

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Constrained coding and soft iterative decoding. Boston: Kluwer Academic Publishers, 2001.

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Luan, Sheng. Coding and decoding of calcium signals in plants. Heidelberg: Springer, 2011.

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Luan, Sheng, ed. Coding and Decoding of Calcium Signals in Plants. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-20829-4.

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Lin, Shu. Suboptimum decoding of block codes. [Washington, D.C: National Aeronautics and Space Administration, 1991.

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Duursma, Iwan Maynard. Decoding codes from curves and cyclic codes. [Eindhoven: Technische Universiteit Eindhoven, 1993.

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Jiantian, Wu, Lin Shu 1937-, and United States. National Aeronautics and Space Administration., eds. Multi-level trellis coded modulation and multi-stage decoding. Notre Dame, IN: Dept. of Electrical and Computer Engineering, University of Notre Dame, 1990.

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Book chapters on the topic "Neuronal coding and decoding"

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Heegard, Chris, and Stephen B. Wicker. "Turbo Decoding." In Turbo Coding, 121–64. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4757-2999-3_6.

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Blinovsky, Volodia. "List Decoding." In Asymptotic Combinatorial Coding Theory, 7–40. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6193-4_2.

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Blinovsky, Volodia. "Decoding Complexity." In Asymptotic Combinatorial Coding Theory, 63–73. Boston, MA: Springer US, 1997. http://dx.doi.org/10.1007/978-1-4615-6193-4_4.

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Ran, Moshe, Carlos De Segovia, and Omer Ran. "Decoding." In Error Control Coding for B3G/4G Wireless Systems, 49–67. Chichester, UK: John Wiley & Sons, Ltd, 2011. http://dx.doi.org/10.1002/9780470975220.ch2.

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Heegard, Chris, and Stephen B. Wicker. "Belief Propagation and Parallel Decoding." In Turbo Coding, 165–98. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4757-2999-3_7.

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Prank, Klaus, Martin Kropp, and Georg Brabant. "Humoral Coding and Decoding." In Novartis Foundation Symposia, 96–110. Chichester, UK: John Wiley & Sons, Ltd, 2008. http://dx.doi.org/10.1002/0470846674.ch9.

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Paire, J. T., P. Coulton, and P. G. Farrell. "Graph Configurations and Decoding Performance." In Cryptography and Coding, 158–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45325-3_15.

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Zehavi, Ephraim, and Jack Salz. "Decoding under integer metric constraints." In Coding and Quantization, 83–94. Providence, Rhode Island: American Mathematical Society, 1993. http://dx.doi.org/10.1090/dimacs/014/09.

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Brown, Andrew, Lorenz Minder, and Amin Shokrollahi. "Improved Decoding of Interleaved AG Codes." In Cryptography and Coding, 37–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11586821_3.

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Norton, Graham. "Some decoding applications of minimal realization." In Cryptography and Coding, 53–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-60693-9_8.

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Conference papers on the topic "Neuronal coding and decoding"

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Tessadori, Jacopo, Daniele Venuta, Sreedhar S. Kumar, Marta Bisio, Valentina Pasquale, and Michela Chiappalone. "Embodied neuronal assemblies: A closed-loop environment for coding and decoding studies." In 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER). IEEE, 2013. http://dx.doi.org/10.1109/ner.2013.6696080.

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Zhe Chen, F. Kloosterman, S. Layton, and M. A. Wilson. "Transductive neural decoding for unsorted neuronal spikes of rat hippocampus." In 2012 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2012. http://dx.doi.org/10.1109/embc.2012.6346178.

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Faruque, Saleh. "Orthogonal coding and iterative decoding improves coding gain." In 2008 IEEE International Conference on Electro/Information Technology (EIT 2008). IEEE, 2008. http://dx.doi.org/10.1109/eit.2008.4554313.

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"Session WA4b: Coding and decoding." In 2014 48th Asilomar Conference on Signals, Systems and Computers. IEEE, 2014. http://dx.doi.org/10.1109/acssc.2014.7094846.

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"Session WA8al: Coding and decoding." In 2015 49th Asilomar Conference on Signals, Systems and Computers. IEEE, 2015. http://dx.doi.org/10.1109/acssc.2015.7421417.

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Sezgin, Aydin. "Session MP2b: Coding and decoding." In 2011 45th Asilomar Conference on Signals, Systems and Computers. IEEE, 2011. http://dx.doi.org/10.1109/acssc.2011.6190084.

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Glasgow, Margalit, and Mary Wootters. "Approximate Gradient Coding with Optimal Decoding." In 2021 IEEE International Symposium on Information Theory (ISIT). IEEE, 2021. http://dx.doi.org/10.1109/isit45174.2021.9517990.

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Azarian, Kambiz, Arul Murugan, and Hesham El Gamal. "On Cooperative Lattice Coding and Decoding." In 2007 Information Theory and Applications Workshop. IEEE, 2007. http://dx.doi.org/10.1109/ita.2007.4357558.

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Radhakrishnan, Janaki, S. Sarayu, K. George Kurian, Deepak Alluri, and R. Gandhiraj. "Huffman coding and decoding using Android." In 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE, 2016. http://dx.doi.org/10.1109/iccsp.2016.7754156.

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Bruns, Volker, Thomas Richter, Bilal Ahmed, Joachim Keinert, and Siegfried Foel. "Decoding JPEG XS on a GPU." In 2018 Picture Coding Symposium (PCS). IEEE, 2018. http://dx.doi.org/10.1109/pcs.2018.8456310.

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Reports on the topic "Neuronal coding and decoding"

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Wasserman, David. Polar Coding with CRC-Aided List Decoding. Fort Belvoir, VA: Defense Technical Information Center, August 2015. http://dx.doi.org/10.21236/ada625869.

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Neifeld, Mark A. Parallel Error Coding Decoding for Highly Parallel Memories. Fort Belvoir, VA: Defense Technical Information Center, August 1997. http://dx.doi.org/10.21236/ada329704.

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