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Littérature scientifique sur le sujet « Neuronal coding and decoding »
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Articles de revues sur le sujet "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 (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 (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 (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 (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 (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 (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 (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 (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 (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 (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.