cortical column
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Author(s):  
Vinay Parameshwarappa ◽  
Laurent Pezard ◽  
Arnaud Jean Norena

In the auditory modality, noise trauma has often been used to investigate cortical plasticity as it causes cochlear hearing loss. One limitation of these past studies, however, is that the effects of noise trauma have been mostly documented at the granular layer, which is the main cortical recipient of thalamic inputs. Importantly, the cortex is composed of six different layers each having its own pattern of connectivity and specific role in sensory processing. The present study aims at investigating the effects of acute and chronic noise trauma on the laminar pattern of spontaneous activity in primary auditory cortex of the anesthetized guinea pig. We show that spontaneous activity is dramatically altered across cortical layers after acute and chronic noise-induced hearing loss. First, spontaneous activity was globally enhanced across cortical layers, both in terms of firing rate and amplitude of spike-triggered average of local field potentials. Second, current source density on (spontaneous) spike-triggered average of local field potentials indicates that current sinks develop in the supra- and infragranular layers. These latter results suggest that supragranular layers become a major input recipient and that the propagation of spontaneous activity over a cortical column is greatly enhanced after acute and chronic noise-induced hearing loss. We discuss the possible mechanisms and functional implications of these changes.


Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1458
Author(s):  
Alexander Telnykh ◽  
Irina Nuidel ◽  
Olga Shemagina ◽  
Vladimir Yakhno

How do living systems process information? The search for an answer to this question is ongoing. We have developed an intelligent video analytics system. The process of the formation of detectors for content-based image retrieval aimed at detecting objects of various types simulates the operation of the structural and functional modules for image processing in living systems. The process of detector construction is, in fact, a model of the formation (or activation) of connections in the cortical column (structural and functional unit of information processing in the human and animal brain). The process of content-based image retrieval, that is, the detection of various types of images in the developed system, reproduces the process of “triggering” a model biomorphic column, i.e., a detector in which connections are formed during the learning process. The recognition process is a reaction of the receptive field of the column to the activation by a given signal. Since the learning process of the detector can be visualized, it is possible to see how a column (a detector of specific stimuli) is formed: a face, a digit, a number, etc. The created artificial cognitive system is a biomorphic model of the recognition column of living systems.


eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Ariana R Andrei ◽  
Samantha Debes ◽  
Mircea Chelaru ◽  
Xiaoqin Liu ◽  
Elsa Rodarte ◽  
...  

Cortical inactivation represents a key causal manipulation that allows the study of cortical circuits and their impact on behavior. A key assumption in these studies is that the neurons in the target area become silent while the surrounding cortical tissue is only negligibly impacted. However, individual neurons are embedded in complex local circuits comprised of excitatory and inhibitory cells with connections extending hundreds of microns. This raises the possibility that silencing one part of the network could induce complex, unpredictable activity changes in neurons outside the targeted inactivation zone. These off-target side effects can potentially complicate interpretations of inactivation manipulations, especially when they are related to changes in behavior. Here, we demonstrate that optogenetic inactivation of glutamatergic neurons in the superficial layers of monkey V1 induces robust suppression at the light-targeted site, but destabilizes stimulus responses in the neighboring, untargeted network. We identified 4 types of stimulus-evoked neuronal responses within a cortical column, ranging from full suppression to facilitation, and a mixture of both. Mixed responses were most prominent in middle and deep cortical layers. Importantly, these results demonstrate that response modulation driven by lateral network connectivity is diversely implemented throughout a cortical column. Furthermore, consistent behavioral changes induced by optogenetic inactivation were only achieved when cumulative network activity was homogeneously suppressed. Therefore, careful consideration of the full range of network changes outside the inactivated cortical region is required, as heterogeneous side-effects can confound interpretation of inactivation experiments.


2021 ◽  
Author(s):  
Vyassa L Baratham ◽  
Maximilian E Dougherty ◽  
Peter Ledochowitsch ◽  
Michele M Maharbiz ◽  
Kristofer Bouchard

Electrocorticography (ECoG) methodologically bridges basic neuroscience and understanding of human brains in health and disease. However, the localization of ECoG signals across the surface of the brain and the spatial distribution of their generating neuronal sources are poorly understood. To address this gap, we recorded from rat auditory cortex using customized microECoG, and simulated cortical surface electrical potentials with a full-scale, biophysically detailed cortical column model. Experimentally, microECoG-derived auditory representations were tonotopically organized and signals were anisotropically localized to 200 micrometers, i.e., a single cortical column. Biophysical simulations reproduce experimental findings, and indicate that neurons in cortical layers V and VI contribute ~85% of evoked high-gamma signal recorded at the surface. Cell number and synchronicity were the primary biophysical properties determining laminar contributions to evoked microECoG signals, while distance was only a minimal factor. Thus, evoked microECoG signals primarily originate from neurons in the infragranular layers of a single cortical column.


Neuron ◽  
2021 ◽  
Vol 109 (13) ◽  
pp. 2041-2042
Author(s):  
Hyeyoung Shin ◽  
Hillel Adesnik
Keyword(s):  

2021 ◽  
Vol 15 ◽  
Author(s):  
James C. Knight ◽  
Anton Komissarov ◽  
Thomas Nowotny

More than half of the Top 10 supercomputing sites worldwide use GPU accelerators and they are becoming ubiquitous in workstations and edge computing devices. GeNN is a C++ library for generating efficient spiking neural network simulation code for GPUs. However, until now, the full flexibility of GeNN could only be harnessed by writing model descriptions and simulation code in C++. Here we present PyGeNN, a Python package which exposes all of GeNN's functionality to Python with minimal overhead. This provides an alternative, arguably more user-friendly, way of using GeNN and allows modelers to use GeNN within the growing Python-based machine learning and computational neuroscience ecosystems. In addition, we demonstrate that, in both Python and C++ GeNN simulations, the overheads of recording spiking data can strongly affect runtimes and show how a new spike recording system can reduce these overheads by up to 10×. Using the new recording system, we demonstrate that by using PyGeNN on a modern GPU, we can simulate a full-scale model of a cortical column faster even than real-time neuromorphic systems. Finally, we show that long simulations of a smaller model with complex stimuli and a custom three-factor learning rule defined in PyGeNN can be simulated almost two orders of magnitude faster than real-time.


2021 ◽  
pp. 66-84
Author(s):  
John Zerilli

The previous chapter argued that we ought to regard dissociability as the sine qua non of modularity. As for what in the brain meets this standard of modularity, the only likely candidate will be something resembling a cortical column. But this is not guaranteed. The effects of the neural network context may so compromise a region’s ability to maintain a set of stable input–output relations that it cannot be considered a genuine module. The brain’s network structure poses particular difficulties for modularity, since even if we were to treat nodes as modules, still we could be missing the point—the key to networks lies not in their nodes, but in the structure of their interactions, and these interactions make pinning down what any single node “does” a fraught enterprise. The chapter includes a table of specificity for brain regions.


2020 ◽  
Author(s):  
Natalia Orlova ◽  
Dmitri Tsyboulski ◽  
Farzaneh Najafi ◽  
Sam Seid ◽  
Sara Kivikas ◽  
...  

Cortical columns interact through dynamic routing of neuronal activity. To monitor these interactions, we developed the Multiplane Mesoscope which combines three established microscopy technologies: time-division multiplexing, remote focusing, and random-access mesoscopy. The Multiplane Mesoscope allowed us to study cortical column interactions in excitatory and inhibitory subpopulations in behaving mice. We found that distinct cortical subnetworks represent expected and unexpected events, suggesting that expectation violations modify signal routing across cortical columns, and establishing the Multiplane Mesoscope as a unique platform to study signal routing.


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