scholarly journals Cellular connectomes as arbiters of local circuit models in the cerebral cortex

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Emmanuel Klinger ◽  
Alessandro Motta ◽  
Carsten Marr ◽  
Fabian J. Theis ◽  
Moritz Helmstaedter
Keyword(s):  
Cerebral Cortex ◽  
Model Selection ◽  
Posterior Probability ◽  
Model Discrimination ◽  
Local Circuit ◽  
Experimental Strategy ◽  
Cellular Resolution ◽  
Layer 4 ◽  
Approximate Bayesian ◽  
Cortical Models

AbstractWith the availability of cellular-resolution connectivity maps, connectomes, from the mammalian nervous system, it is in question how informative such massive connectomic data can be for the distinction of local circuit models in the mammalian cerebral cortex. Here, we investigated whether cellular-resolution connectomic data can in principle allow model discrimination for local circuit modules in layer 4 of mouse primary somatosensory cortex. We used approximate Bayesian model selection based on a set of simple connectome statistics to compute the posterior probability over proposed models given a to-be-measured connectome. We find that the distinction of the investigated local cortical models is faithfully possible based on purely structural connectomic data with an accuracy of more than 90%, and that such distinction is stable against substantial errors in the connectome measurement. Furthermore, mapping a fraction of only 10% of the local connectome is sufficient for connectome-based model distinction under realistic experimental constraints. Together, these results show for a concrete local circuit example that connectomic data allows model selection in the cerebral cortex and define the experimental strategy for obtaining such connectomic data.

Establishment of a mouse line for inducible ablation of neurons in the layer 4 of the cerebral cortex

2009 ◽  
Vol 65 ◽  
pp. S233
Author(s):  
Nozomi Endo ◽  
Takeshi Uemura ◽  
Kenji Sakimura ◽  
Shigeyoshi Itohara ◽  
Masayoshi Mishina
Keyword(s):  
Cerebral Cortex ◽  
Mouse Line ◽  
Layer 4

The Pyramidal Cell and its Local-Circuit Interneurons: A Hypothetical Unit of the Mammalian Cerebral Cortex

1990 ◽  
Vol 2 (3) ◽  
pp. 180-194 ◽  
Author(s):  
Miguel Marín-Padilla
Keyword(s):  
Cerebral Cortex ◽  
Pyramidal Cell ◽  
Three Dimensional ◽  
Pyramidal Cells ◽  
Cortical Function ◽  
Local Circuit ◽  
Proposed Model ◽  
Dimensional Distribution ◽  
Chandelier Cells ◽  
Species Specific

A pyramidal cell with five of its local-circuit interneurons (Cajal–Retzius, Martinotti, Cajal double-bouquet, basket, and chandelier cells), constitutes a distinct structural/functional assemblage of the mammalian neocortex. This pyramidal/local-circuit neuronal assemblage is proposed herein as a basic neocortical unit. This unit is shared by all mammals, embodies both specific structural as well as functional elements, and constitutes an essential developmental building block of the neocortex. In the model, the pyramidal cell represents a distinct, stable, projective, excitatory neuron that has remained essentially unchanged in the course of mammalian phylogeny. On the other hand, its local-circuit interneurons are more likely to be inhibitory and less stable, designed perhaps to adapt, and modify in response to environmental needs. The proposed model infers that the number of pyramidal cells contacted by each local-circuit interneuron as well as the number of synaptic contacts established with each one are elements acquired post-natally in response to individual needs. Thereby, the overall three-dimensional distribution and extent of these pyramidal/local-circuit neuronal assemblages should be species-specific, variable among individual of the same species, and able to adapt in response to environmental needs. The model introduces a different approach, perhaps a new vantage point, for the study of the basic structural organization of the mammalian cerebral cortex. Relationships of the proposed model to cortical function in general and to learning behavior in particular are discussed.

Bayesian Inference of Trend and Difference-Stationarity

1994 ◽  
Vol 10 (3-4) ◽  
pp. 596-608 ◽  
Author(s):  
Robert E. McCulloch ◽  
Ruey S. Tsay
Keyword(s):  
Time Series ◽  
Bayesian Inference ◽  
Model Selection ◽  
Odds Ratio ◽  
Model Discrimination ◽  
Posterior Probabilities ◽  
Competing Models ◽  
Computational Difficulty ◽  
Changes Over Time ◽  
Over Time

This paper proposes a general Bayesian framework for distinguishing between trend- and difference-stationarity. Usually, in model selection, we assume that all of the data were generated by one of the models under consideration. In studying time series, however, we may be concerned that the process is changing over time, so that the preferred model changes over time as well. To handle this possibility, we compute the posterior probabilities of the competing models for each observation. This way we can see if different segments of the series behave differently with respect to the competing models. The proposed method is a generalization of the usual odds ratio for model discrimination in Bayesian inference. In application, we employ the Gibbs sampler to overcome the computational difficulty. The procedure is illustrated by a real example.

scholarly journals A probabilistic model for indel evolution: differentiating insertions from deletions

2020 ◽  
Author(s):  
Gil Loewenthal ◽  
Dana Rapoport ◽  
Oren Avram ◽  
Asher Moshe ◽  
Alon Itzkovitch ◽  
...  
Keyword(s):  
Model Selection ◽  
Probabilistic Models ◽  
Simulated Data ◽  
Selection Procedure ◽  
Biological Data ◽  
Effective Population ◽  
Selection Scheme ◽  
Insertions And Deletions ◽  
Deletion Rate ◽  
Approximate Bayesian

AbstractInsertions and deletions (indels) are common molecular evolutionary events. However, probabilistic models for indel evolution are under-developed due to their computational complexity. Here we introduce several improvements to indel modeling: (1) while previous models for indel evolution assumed that the rates and length distributions of insertions and deletions are equal, here, we propose a richer model that explicitly distinguishes between the two; (2) We introduce numerous summary statistics that allow Approximate Bayesian Computation (ABC) based parameter estimation; (3) We develop a neural-network model-selection scheme to test whether the richer model better fits biological data compared to the simpler model. Our analyses suggest that both our inference scheme and the model-selection procedure achieve high accuracy on simulated data. We further demonstrate that our proposed indel model better fits a large number of empirical datasets and that, for the majority of these datasets, the deletion rate is higher than the insertion rate. Finally, we demonstrate that indel rates are negatively correlated to the effective population size across various phylogenomic clades.

scholarly journals Phylogeographic model selection using convolutional neural networks

2020 ◽  
Author(s):  
Emanuel Masiero da Fonseca ◽  
Guarino R. Colli ◽  
Fernanda P. Werneck ◽  
Bryan C. Carstens
Keyword(s):  
Neural Networks ◽  
Gene Flow ◽  
Model Selection ◽  
Convolutional Neural Networks ◽  
Model Organisms ◽  
South American ◽  
Effective Population ◽  
Climatic Oscillations ◽  
Demographic Models ◽  
Approximate Bayesian

AbstractThe field of phylogeography has evolved rapidly in terms of the analytical toolkit to analyze the ever-increasing amounts of genomic data. Despite substantial advances, researchers have not fully explored all potential analytical tools to tackle the challenge posed by the huge size of genomic datasets. For example, deep learning techniques, such as convolutional neural networks (CNNs), widely employed in image and video classification, are largely unexplored for phylogeographic model selection. In non-model organisms, the lack of information about their ecology, natural history, and evolution can lead to uncertainty about which set of demographic models should be considered. Here we investigate the utility of CNNs for assessing a large number of competing phylogeographic models using South American lizards as an example, and approximate Bayesian computation (ABC) to contrast the performance of CNNs. First, we evaluated three demographic scenarios (constant, expansion, and bottleneck) for each of four recovered lineages and found that the overall model accuracy was higher than 98% for all lineages. Next, we evaluated a set of 26 models that accounted for evolutionary relationships, gene flow, and changes in effective population size among these lineages and recovered an overall accuracy of 87%. In contrast, ABC was unable to single out a best fit model among 26 competing models. Finally, we used the CNN model to investigate the evolutionary history of two South American lizards. Our results indicate the presence of hidden genetic diversity, gene flow between non-sister populations, and changes in effective population sizes through time, likely in response to Pleistocene climatic oscillations. Our results demonstrate that CNNs can be easily and usefully incorporated into the phylogeographer’s toolkit.

scholarly journals Learning-induced plasticity in the barrel cortex is disrupted by inhibition of layer 4 somatostatin-containing interneurons

2020 ◽  
Author(s):  
G Dobrzanski ◽  
A Lukomska ◽  
R Zakrzewska ◽  
A Posluszny ◽  
D Kanigowski ◽  
...  
Keyword(s):  
Cerebral Cortex ◽  
Conditioned Stimulus ◽  
Barrel Cortex ◽  
Specific Cell ◽  
Conditioned Reaction ◽  
Functional Representation ◽  
Layer 4 ◽  
Plastic Changes ◽  
Plastic Change ◽  
Stimulation Of

ABSTRACTLearning-related plasticity in the cerebral cortex is linked to the action of disinhibitory circuits of interneurons. Pavlovian conditioning, in which stimulation of the vibrissae is used as conditioned stimulus, induces plastic enlargement of the cortical functional representation of vibrissae activated during conditioning, visualized with [14C]-2-deoxyglucose (2DG). Using layer-specific, cell-selective DREADD transductions, we examined the involvement of somatostatin- (SOM-INs) and vasoactive intestinal peptide (VIP-INs)-containing interneurons in the development of learning-related plastic changes. We injected DREADD-expressing vectors into layer IV (L4) barrels or layer II/III (L2/3) areas corresponding to activated vibrissae. The activity of interneurons was modulated during training, and 2DG maps were obtained 24 h later. In mice with L4 but not L2/3 SOM-INs suppressed during conditioning, the plastic change of whisker representation and the conditioned reaction were absent. No effect of inhibiting VIP-INs was found. We report that the activity of L4 SOM-INs is indispensable for learning-induced plastic change.

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