Cortical representation of touch <i>in silico</i>

Huang, Chao; Zeldenrust, Fleur; Celikel, Tansu

doi:10.1101/2020.11.06.371252

Cited by 3 publications

(7 citation statements)

References 158 publications

(248 reference statements)

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“…Coarsely, on the level of their firing patterns, inhibitory neurons can be distinguished from excitatory neurons, by more dense responses, based on a greater convergence of connections (reviewed in Harris and Mrsic-Flogel (2013)). The connectivity in the present model was set in precise accordance with the latest results from the literature from identified, pairwise recordings (see Huang et al (2020) for detailed references), and consequently recreates these differences in firing behavior.…”

Section: Predictions For Cell-type Specific Coding Strategiesmentioning

confidence: 99%

“…3) and on bootstrapped populations of the in vitro data (Fig. 4, see Materials and Methods and Huang et al, (2020)).…”

Section: Information Recovery In Local Neural Populations In Silico and In Vitromentioning

confidence: 99%

“…From the perspective of information transmission in single trials, the most important property of the neural response is the reliability across trials. We utilized the barrel column in silico (Huang et al, 2020) to investigate the influence of the encoding strategy in L4 (reliability of spike timing and spike count) on the information transfer to L2/3. We first considered three extreme cases of encoding in L4, with a more systematic exploration in the following section.…”

Section: Contribution Of Timing/rate Reliability For Information Recoverymentioning

confidence: 99%

“…How to convey stimulus information both lossless and efficiently, and how this depends on physical properties such as network connectivity and node (neuron) properties, is an important open question in network science, and neuroscience specifically (Maheswaranathan et al, 2018;Mastrogiuseppe and Ostojic, 2017), in which computational models like the one we present here (Huang et al, 2020) play an invaluable role. Recently, it has been shown that there is a trade-off between the sparsity and the amount of recovered information in neural coding (Billings et al, 2014): lossless coding is only possible if the connectivity in the network is not too sparse.…”

Section: High Information Availability and Multiplexed Codesmentioning

confidence: 99%

“…We found that information lost during action potential generation can be fully recovered by as little as 100 neurons with a time resolution of 2-3 ms. Finally, we turned to a realistic and wellconstrained simulation of a barrel column (Huang et al, 2020) to study the relation between encoding strategies in L4 and decoding strategies in L2/3 to determine the mechanisms of information recovery.…”

Section: Introductionmentioning

confidence: 99%

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Information transfer and recovery for the sense of touch

Huang

Englitz

Reznik

et al. 2020

Preprint

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Transformation of postsynaptic potentials (PSPs) into action potentials (APs) is the rate-limiting step of communication in neural networks. The efficiency of this intracellular information transfer also powerfully shapes stimulus representations in sensory cortices. Using whole-cell recordings and information-theoretic measures, we show herein that somatic PSPs accurately represent stimulus location on a trial-by-trial basis in single neurons even 4 synapses away from the sensory periphery in the whisker system. This information is largely lost during AP generation but can be rapidly (<20 ms) recovered using complementary information in local populations in a cell-type-specific manner. These results show that as sensory information is transferred from one neural locus to another, the circuits reconstruct the stimulus with high fidelity so that sensory representations of single neurons faithfully represent the stimulus in the periphery, but only in their PSPs, resulting in lossless information processing for the sense of touch in the primary somatosensory cortex.

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Section: Predictions For Cell-type Specific Coding Strategiesmentioning

confidence: 99%

“…3) and on bootstrapped populations of the in vitro data (Fig. 4, see Materials and Methods and Huang et al, (2020)).…”

Section: Information Recovery In Local Neural Populations In Silico and In Vitromentioning

confidence: 99%

Section: Contribution Of Timing/rate Reliability For Information Recoverymentioning

confidence: 99%

Section: High Information Availability and Multiplexed Codesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Information transfer and recovery for the sense of touch

Huang

Englitz

Reznik

et al. 2020

Preprint

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NETSCOPE: Information-Theory based Network Discovery and Analysis

Bergmans

Celikel

2022

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BackgroundReconstruction of molecular networks allows structural and functional characterization of cells, tissues, and organs. A network analytical approach to statistical correlations in molecular data can provide new insight into the underlying system, but currently there is not any analytical approach that is generalizable across different types of data. Here we present TITAN, an open-source toolbox in MATLAB and Octave for the reconstruction and graph analysis of molecular networks. Using an information-theoretical approach TITAN reconstructs networks from transcriptional data, revealing the topological structure of correlations in biological systems.FindingsThe toolbox includes a data processing pipeline to construct gene co-expression networks from transcriptional data, mutual information (MI) and variation of information (VI) to quantify connectivity and graph-bas ed analysis for molecular network analysis. We show that the toolbox can accurately reconstruct network structure from synthetic data with predetermined network topology. We use the toolbox to construct the gene co-expression network of S. cerevisiae and show that the resulting network is indistinguishable from gene co-expression networks previously identified experimentally.DiscussionTITAN is a general tool for network reconstruction in high dimensional datasets. It could be used to study co-transcriptional networks and reveal connectivity patterns in any large omics datasets. Considering the universal nature of MI as a measure of correlation, TITAN can potentially be used to vertically integrate datasets for a cross-omics analysis that covers a system on every level, from genes to metabolites. This universality can be extended to cross-species analysis and evolutionary genetics. Other applications include molecular fingerprinting of cells, tissues, and organs, and tracing developmental trajectories for gene and protein networks.

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Neuromorphic computing hardware and neural architectures for robotics

et al. 2022

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Neuromorphic hardware enables fast and power-efficient neural network–based artificial intelligence that is well suited to solving robotic tasks. Neuromorphic algorithms can be further developed following neural computing principles and neural network architectures inspired by biological neural systems. In this Viewpoint, we provide an overview of recent insights from neuroscience that could enhance signal processing in artificial neural networks on chip and unlock innovative applications in robotics and autonomous intelligent systems. These insights uncover computing principles, primitives, and algorithms on different levels of abstraction and call for more research into the basis of neural computation and neuronally inspired computing hardware.

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Cortical representation of touch in silico

Cited by 3 publications

References 158 publications

Information transfer and recovery for the sense of touch

Information transfer and recovery for the sense of touch

NETSCOPE: Information-Theory based Network Discovery and Analysis

Neuromorphic computing hardware and neural architectures for robotics

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