Thalamic control of sensory enhancement and sleep spindle properties in a biophysical model of thalamoreticular microcircuitry

Iavarone, Elisabetta; Simko, Jane; Shi, Ying; Bertschy, Marine; García‐Amado, María; Litvak, Polina; Kaufmann, Anna-Kristin; O’Reilly, Christian; Amsalem, Oren; Abdellah, Marwan; Chevtchenko, Grigori; Coste, B.; Courcol, Jean-Denis; Ecker, András; Favreau, Cyrille; Fleury, Adrien Christian; Geit, Werner Van; Gevaert, Michael; Guerrero, Nadir Román; Herttuainen, Joni; Ivaska, Genrich; Kerrien, Samuel; King, James G.; Kumbhar, Pramod; Lurie, Patrycja; Magkanaris, Ioannis; Muddapu, Vignayanandam Ravindernath; Nair, Jayakrishnan; Pereira, F.L.; Perin, Rodrigo; Petitjean, Fabien; Ranjan, Rajnish; Reimann, Michael; Soltuzu, Liviu; Sy, Mohameth François; Tuncel, Mustafa Anil; Ulbrich, Alexander; Wolf, M.; Clascá, Francisco; Markram, Henry; Hill, Sean

doi:10.1101/2022.02.28.482273

Cited by 4 publications

(4 citation statements)

References 251 publications

(519 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…S18). A substantial body of anatomical and neurophysiological data on higher cortical areas and their connectivity to V1 is currently available for that [see, e.g., (6,32,33)]. We expect that deficits in visual processing capabilities of the V1 model, such as limited spatial integration of image features and a relatively short working memory time span, will disappear when the V1 model is combined with models of higher brain areas.…”

Section: Discussionmentioning

confidence: 99%

A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing

2022

View full text Add to dashboard Cite

We analyze visual processing capabilities of a large-scale model for area V1 that arguably provides the most comprehensive accumulation of anatomical and neurophysiological data to date. We find that this brain-like neural network model can reproduce a number of characteristic visual processing capabilities of the brain, in particular the capability to solve diverse visual processing tasks, also on temporally dispersed visual information, with remarkable robustness to noise. This V1 model, whose architecture and neurons markedly differ from those of deep neural networks used in current artificial intelligence (AI), such as convolutional neural networks (CNNs), also reproduces a number of characteristic neural coding properties of the brain, which provides explanations for its superior noise robustness. Because visual processing is substantially more energy efficient in the brain compared with CNNs in AI, such brain-like neural networks are likely to have an impact on future technology: as blueprints for visual processing in more energy-efficient neuromorphic hardware.

show abstract

Section: Discussionmentioning

confidence: 99%

A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing

2022

View full text Add to dashboard Cite

show abstract

“…Previous TC circuit models included less biophysically-detailed neuron models and simpler connectivity ( Izhikevich and Edelman, 2008 ), or focused on single cell ( Iavarone et al, 2019 ) or small circuit models ( Destexhe et al, 1996a ). An impressively detailed model of the thalamoreticular microcircuit has recently been developed, although this is limited to the VPL and RTN somatosensory thalamus regions ( Iavarone et al, 2022 ).…”

Section: Discussionmentioning

confidence: 99%

Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE

Borges

Moreira

Takarabe

et al. 2022

Front. Neuroinform.

View full text Add to dashboard Cite

The primary somatosensory cortex (S1) of mammals is critically important in the perception of touch and related sensorimotor behaviors. In 2015, the Blue Brain Project (BBP) developed a groundbreaking rat S1 microcircuit simulation with over 31,000 neurons with 207 morpho-electrical neuron types, and 37 million synapses, incorporating anatomical and physiological information from a wide range of experimental studies. We have implemented this highly detailed and complex S1 model in NetPyNE, using the data available in the Neocortical Microcircuit Collaboration Portal. NetPyNE provides a Python high-level interface to NEURON and allows defining complicated multiscale models using an intuitive declarative standardized language. It also facilitates running parallel simulations, automates the optimization and exploration of parameters using supercomputers, and provides a wide range of built-in analysis functions. This will make the S1 model more accessible and simpler to scale, modify and extend in order to explore research questions or interconnect to other existing models. Despite some implementation differences, the NetPyNE model preserved the original cell morphologies, electrophysiological responses and spatial distribution for all 207 cell types; and the connectivity properties of all 1941 pathways, including synaptic dynamics and short-term plasticity (STP). The NetPyNE S1 simulations produced reasonable physiological firing rates and activity patterns across all populations. When STP was included, the network generated a 1 Hz oscillation comparable to the original model in vitro-like state. By then reducing the extracellular calcium concentration, the model reproduced the original S1 in vivo-like states with asynchronous activity. These results validate the original study using a new modeling tool. Simulated local field potentials (LFPs) exhibited realistic oscillatory patterns and features, including distance- and frequency-dependent attenuation. The model was extended by adding thalamic circuits, including 6 distinct thalamic populations with intrathalamic, thalamocortical (TC) and corticothalamic connectivity derived from experimental data. The thalamic model reproduced single known cell and circuit-level dynamics, including burst and tonic firing modes and oscillatory patterns, providing a more realistic input to cortex and enabling study of TC interactions. Overall, our work provides a widely accessible, data-driven and biophysically-detailed model of the somatosensory TC circuits that can be employed as a community tool for researchers to study neural dynamics, function and disease.

show abstract

“…Previous thalamocortical circuit models included less biophysically-detailed neuron models and simpler connectivity (Izhikevich and Edelman 2008), or focused on single cell (Iavarone et al 2019) or small circuit models (Murray and Anticevic 2017). An impressively detailed model of the thalamoreticular microcircuit has recently been developed, although this is limited to the VPL and RTN somatosensory thalamus regions (Iavarone et al 2022).…”

Section: Discussionmentioning

confidence: 99%

Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE

Borges

Moreira

Takarabe

et al. 2022

Preprint

View full text Add to dashboard Cite

The primary somatosensory cortex (S1) of mammals is critically important in the perception of touch and related sensorimotor behaviors. In 2015 the Blue Brain Project developed a groundbreaking rat S1 microcircuit simulation with over 31,000 neurons with 207 morpho-electrical neuron types, and 37 million synapses, incorporating anatomical and physiological information from a wide range of experimental studies. We have implemented this highly-detailed and complex model S1 model in NetPyNE, using the data available in the Neocortical Microcircuit Collaboration Portal. NetPyNE provides a Python high-level interface to NEURON and allows defining complicated multiscale models using an intuitive declarative standardized language. It also facilitates running parallel simulations, automates the optimization and exploration of parameters using supercomputers, and provides a wide range of built-in analysis functions. This will make the S1 model more accessible and simpler to scale, modify and extend in order to explore research questions or interconnect to other existing models. Despite some implementation differences, the NetPyNE model closely reproduced the original cell morphologies, electrophysiological responses, spatial distribution for all 207 cell types; and the connectivity properties of the 1941 pathways, including synaptic dynamics and short-term plasticity (STP). The NetPyNE S1 simulations produced reasonable physiological rates and activity patterns across all populations, and, when STP was included, produced a 1 Hz oscillation comparable to that of the original model. We also extended the model by adding thalamic circuits, including 6 distinct thalamic populations with intrathalamic, thalamocortical and corticothalamic connectivity derived from experimental data. Overall, our work provides a widely accessible, data-driven and biophysically-detailed model of the somatosensory thalamocortical circuits that can be utilized as a community tool for researchers to study neural dynamics, function and disease.

show abstract

Thalamic control of sensory enhancement and sleep spindle properties in a biophysical model of thalamoreticular microcircuitry

Cited by 4 publications

References 251 publications

A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing

A data-based large-scale model for primary visual cortex enables brain-like robust and versatile visual processing

Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE

Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE

Contact Info

Product

Resources

About