scholarly journals Thalamocortical Spectral Transmission Relies on Balanced Input Strengths

2021 ◽  
Author(s):  
Matteo Saponati ◽  
Jordi Garcia-Ojalvo ◽  
Enrico Cataldo ◽  
Alberto Mazzoni
Keyword(s):  
Field Potential ◽  
Inhibitory Neurons ◽  
Feedforward Network ◽  
Sensory Transmission ◽  
Thalamocortical Connections ◽  
Primary Sensory Cortex ◽  
Spiking Network ◽  
Back Ground ◽  
Corticothalamic Feedback ◽  
The Brain

AbstractThe thalamus is a key element of sensory transmission in the brain, as it gates and selects sensory streams through a modulation of its internal activity. A preponderant role in these functions is played by its internal activity in the alpha range ([8–14] Hz), but the mechanism underlying this process is not completely understood. In particular, how do thalamocortical connections convey stimulus driven information selectively over the back-ground of thalamic internally generated activity? Here we investigate this issue with a spiking network model of feedforward connectivity between thalamus and primary sensory cortex reproducing the local field potential of both areas. We found that in a feedforward network, thalamic oscillations in the alpha range do not entrain cortical activity for two reasons: (i) alpha range oscillations are weaker in neurons projecting to the cortex, (ii) the gamma resonance dynamics of cortical networks hampers oscillations over the 10–20 Hz range thus weakening alpha range oscillations. This latter mechanism depends on the balance of the strength of thalamocortical connections toward excitatory and inhibitory neurons in the cortex. Our results highlight the relevance of corticothalamic feedback to sustain alpha range oscillations and pave the way toward an integrated understanding of the sensory streams traveling between the periphery and the cortex.

scholarly journals Thalamocortical spectral transmission relies on balanced input strengths

2020 ◽  
Author(s):  
Matteo Saponati ◽  
Jordi Garcia-Ojalvo ◽  
Enrico Cataldo ◽  
Alberto Mazzoni
Keyword(s):  
Sensory Information ◽  
Field Potential ◽  
Sensory Cortex ◽  
Inhibitory Neurons ◽  
Spectral Transmission ◽  
Sensory Transmission ◽  
Thalamocortical Connections ◽  
Primary Sensory Cortex ◽  
Spiking Network ◽  
The Brain

AbstractThe thalamus is a key element of sensory transmission in the brain, as all sensory information is processed by the thalamus before reaching the cortex. The thalamus is known to gate and select sensory streams through a modulation of its internal activity in which spindle oscillations play a preponderant role, but the mechanism underlying this process is not completely understood. In particular, how do thalamocortical connections convey stimulus-driven information selectively over the background of thalamic internally generated activity (such as spindle oscillations)? Here we investigate this issue with a spiking network model of connectivity between thalamus and primary sensory cortex reproducing the local field potential of both areas. We found two features of the thalamocortical dynamics that filter out spindle oscillations: i) spindle oscillations are weaker in neurons projecting to the cortex, ii) the resonance dynamics of cortical networks selectively blocks frequency in the range encompassing spindle oscillations. This latter mechanism depends on the balance of the strength of thalamocortical connections toward excitatory and inhibitory neurons in the cortex. Our results pave the way toward an integrated understanding of the sensory streams traveling between the periphery and the cortex.

Coherent Electrical Activity Between Vibrissa Sensory Areas of Cerebellum and Neocortex Is Enhanced During Free Whisking

2002 ◽  
Vol 87 (4) ◽  
pp. 2137-2148 ◽  
Author(s):  
Sean M. O'Connor ◽  
Rune W. Berg ◽  
David Kleinfeld
Keyword(s):  
Electrical Activity ◽  
Sensory Input ◽  
Field Potential ◽  
Sensory Cortex ◽  
Brain Areas ◽  
Low Coherence ◽  
Primary Sensory Cortex ◽  
High Level ◽  
The Brain ◽  
Local Field Potential Lfp

We tested if coherent signaling between the sensory vibrissa areas of cerebellum and neocortex in rats was enhanced as they whisked in air. Whisking was accompanied by 5- to 15-Hz oscillations in the mystatial electromyogram, a measure of vibrissa position, and by 5- to 20-Hz oscillations in the differentially recorded local field potential (∇LFP) within the vibrissa area of cerebellum and within the ∇LFP of primary sensory cortex. We observed that only 10% of the activity in either cerebellum or sensory neocortex was significantly phase-locked to rhythmic motion of the vibrissae; the extent of this modulation is in agreement with the results from previous single-unit measurements in sensory neocortex. In addition, we found that 40% of the activity in the vibrissa areas of cerebellum and neocortex was significantly coherent during periods of whisking. The relatively high level of coherence between these two brain areas, in comparison with their relatively low coherence with whisking per se, implies that the vibrissa areas of cerebellum and neocortex communicate in a manner that is incommensurate with whisking. To the extent that the vibrissa areas of cerebellum and neocortex communicate over the same frequency band as that used by whisking, these areas must multiplex electrical activity that is internal to the brain with activity that is that phase-locked to vibrissa sensory input.

scholarly journals Artificial intelligence, dynamic efficiency and Economics

2021 ◽  
pp. 337-350
Author(s):  
Vincent Wolters
Keyword(s):  
Artificial Intelligence ◽  
Optimization Problems ◽  
Evolutionary Process ◽  
Human Action ◽  
Medical Doctor ◽  
Dynamic Efficiency ◽  
Demand And Supply ◽  
New Information ◽  
Back Ground ◽  
The Brain

In this work I will lend support to the theory of «dynamic efficien - cy», as outlined by Prof. Huerta de Soto in The Theory of Dynamic Efficiency (2010a). Whereas Huerta de Soto connects economics with ethics, I will take a different approach. Since I have a back-ground in Artificial Intelligence (A.I.), I will show that this and related fields have yielded insights that, when applied to the study of economics, may call for a different way of looking at the eco-nomy and its processes. At first glance, A.I. and economics do not seem to have a lot in common. The former is thought to attempt to build a human being; the latter is supposed to deal with depressions, growth, inflation, etc. That view is too simplistic; in fact there are strong similarities. First, economics is based on (inter-)acting individuals, i.e. on human action. A.I. tries to understand and simulate human (and animal) behavior. Second, economics deals with information pro-cessing, such as how the allocation of resources can best be orga-nized. A.I. also investigates information processing. This can be in specific systems, such as the brain, or the evolutionary process, or purely in an abstract form. Finally, A.I. tries to answer more philosophical questions like: what is intelligence? What is a mind? What is consciousness? Is there free will? These topics play a less prominent role in economics, but are sometimes touched upon, together with the related topic of the «entrepreneurial function». The paradigm that was dominant in the early days of A.I. is static in nature. Reaching a solution is done in different steps. First: gathering all necessary information. Second: processing this in - formation. Finally: the outcome of this process, a clear conclusion. Each step in the process is entirely separate. During information gathering no processing is done, and during processing, no new information is added. The conclusion reached is final and cannot change later on. Logical problems are what is mostly dealt with, finding ways in which a computer can perform deductions based on the information that is represented as logical statements. Other applications are optimization problems, and so-called «Expert Systems», developed to perform the work of a judge reaching a verdict, or a medical doctor making a diagnosis based on the symptoms of the patient. This paradigm is also called «top-down», because information flows to a central point where it is processed, or «symbolic processing», referring to deduction in formal logic.1 In economics there is a similar paradigm, and it is still the do-minant one. This is the part of economics that deals with opti - mization of resources: given costs and given prices, what is the allocation that will lead to the highest profit? Also belonging to this paradigm are the equilibrium models. Demand and supply curves are supposed to be knowable and unchangeable, and the price is a necessary outcome. The culmination is central planning that supposes all necessary information, such as demand and supply curves and available resources to be known. Based on this, the central planner determines prices.

scholarly journals The Spatial and Cell-Type Distribution of SARS-CoV-2 Receptor ACE2 in the Human and Mouse Brains

2021 ◽  
Vol 11 ◽  
Author(s):  
Rongrong Chen ◽  
Keer Wang ◽  
Jie Yu ◽  
Derek Howard ◽  
Leon French ◽  
...  
Keyword(s):  
Endothelial Cells ◽  
Spatial Distribution ◽  
Host Cells ◽  
Inhibitory Neurons ◽  
Middle Temporal Gyrus ◽  
Distribution Analysis ◽  
Cell Type ◽  
Type Distribution ◽  
The Brain ◽  
Human And Mouse

By engaging angiotensin-converting enzyme 2 (ACE2 or Ace2), the novel pathogenic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) invades host cells and affects many organs, including the brain. However, the distribution of ACE2 in the brain is still obscure. Here, we investigated the ACE2 expression in the brain by analyzing data from publicly available brain transcriptome databases. According to our spatial distribution analysis, ACE2 was relatively highly expressed in some brain locations, such as the choroid plexus and paraventricular nuclei of the thalamus. According to cell-type distribution analysis, nuclear expression of ACE2 was found in many neurons (both excitatory and inhibitory neurons) and some non-neuron cells (mainly astrocytes, oligodendrocytes, and endothelial cells) in the human middle temporal gyrus and posterior cingulate cortex. A few ACE2-expressing nuclei were found in a hippocampal dataset, and none were detected in the prefrontal cortex. Except for the additional high expression of Ace2 in the olfactory bulb areas for spatial distribution as well as in the pericytes and endothelial cells for cell-type distribution, the distribution of Ace2 in the mouse brain was similar to that in the human brain. Thus, our results reveal an outline of ACE2/Ace2 distribution in the human and mouse brains, which indicates that the brain infection of SARS-CoV-2 may be capable of inducing central nervous system symptoms in coronavirus disease 2019 (COVID-19) patients. Potential species differences should be considered when using mouse models to study the neurological effects of SARS-CoV-2 infection.

Capturing the Forest but Missing the Trees: Microstates Inadequate for Characterizing Shorter-Scale EEG Dynamics

2019 ◽  
Vol 31 (11) ◽  
pp. 2177-2211 ◽  
Author(s):  
Saurabh Bhaskar Shaw ◽  
Kiret Dhindsa ◽  
James P. Reilly ◽  
Suzanna Becker
Keyword(s):  
Systems Analysis ◽  
Temporal Dynamics ◽  
Field Potential ◽  
Stable States ◽  
Dynamical Systems Analysis ◽  
Eeg Data ◽  
Show Evidence ◽  
Winner Takes All ◽  
Microstate Analysis ◽  
The Brain

The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Proponents of microstates postulate that the brain discontinuously switches between four quasi-stable states defined by specific EEG scalp topologies at peaks in the global field potential (GFP). These microstates are thought to be “atoms of thought,” involved with visual, auditory, salience, and attention processing. However, this method makes some major assumptions by excluding EEG data outside the GFP peaks and then clustering the EEG scalp topologies at the GFP peaks, assuming that only one microstate is active at any given time. This study explores the evidence surrounding these assumptions by studying the temporal dynamics of microstates and its clustering space using tools from dynamical systems analysis, fractal, and chaos theory to highlight the shortcomings in microstate analysis. The results show evidence of complex and chaotic EEG dynamics outside the GFP peaks, which is being missed by microstate analysis. Furthermore, the winner-takes-all approach of only one microstate being active at a time is found to be inadequate since the dynamic EEG scalp topology does not always resemble that of the assigned microstate, and there is competition among the different microstate classes. Finally, clustering space analysis shows that the four microstates do not cluster into four distinct and separable clusters. Taken collectively, these results show that the discontinuous description of EEG microstates is inadequate when looking at nonstationary short-scale EEG dynamics.

Premotor Inhibitory Neurons Carry Signals Related to Saccade Adaptation in the Monkey

2008 ◽  
Vol 99 (1) ◽  
pp. 220-230 ◽  
Author(s):  
Yoshiko Kojima ◽  
Yoshiki Iwamoto ◽  
Farrel R. Robinson ◽  
Christopher T. Noto ◽  
Kaoru Yoshida
Keyword(s):  
Gradual Increase ◽  
Cerebellar Nuclei ◽  
Inhibitory Neurons ◽  
Voluntary Movements ◽  
Burst Neurons ◽  
Premotor Neurons ◽  
Saccade Size ◽  
Motoneuron Activity ◽  
Saccade Adaptation ◽  
The Brain

Cerebellar output changes during motor learning. How these changes cause alterations of motoneuron activity and movement remains an unresolved question for voluntary movements. To answer this question, we examined premotor neurons for saccadic eye movement. Previous studies indicate that cells in the fastigial oculomotor region (FOR) within the cerebellar nuclei on one side exhibit a gradual increase in their saccade-related discharge as the amplitude of ipsiversive saccades adaptively decreases. This change in FOR activity could cause the adaptive change in saccade amplitude because neurons in the FOR project directly to the brain stem region containing premotor burst neurons (BNs). To test this possibility, we recorded the activity of saccade-related burst neurons in the area that houses premotor inhibitory burst neurons (IBNs) and examined their discharge during amplitude-reducing adaptation elicited by intrasaccadic target steps. We specifically analyzed their activity for off-direction (contraversive) saccades, in which the IBN activity would increase to reduce saccade size. Before adaptation, 29 of 42 BNs examined discharged, at least occasionally, for contraversive saccades. As the amplitude of contraversive saccades decreased adaptively, half of BNs with off-direction spike activity showed an increase in the number of spikes (14/29) or an earlier occurrence of spikes (7/14). BNs that were silent during off-direction saccades before adaptation remained silent after adaptation. These results indicate that the changes in the off-direction activity of BNs are closely related to adaptive changes in saccade size and are appropriate to cause these changes.

scholarly journals Human Development XI: The Structure of the Cerebral Cortex. Are There Really Modules in the Brain?

2007 ◽  
Vol 7 ◽  
pp. 1922-1929 ◽  
Author(s):  
Tyge Dahl Hermansen ◽  
Søren Ventegodt ◽  
Isack Kandel
Keyword(s):  
Cerebral Cortex ◽  
Brain Activity ◽  
Sensory Cortex ◽  
Biological Information ◽  
Human Consciousness ◽  
Primary Sensory Cortex ◽  
Sensory Maps ◽  
The World ◽  
The Brain ◽  
The Universe

The structure of human consciousness is thought to be closely connected to the structure of cerebral cortex. One of the most appreciated concepts in this regard is the Szanthagothei model of a modular building of neo-cortex. The modules are believed to organize brain activity pretty much like a computer. We looked at examples in the literature and argue that there is no significant evidence that supports Szanthagothei's model. We discuss the use of the limited genetic information, the corticocortical afferents termination and the columns in primary sensory cortex as arguments for the existence of the cortex-module. Further, we discuss the results of experiments with Luminization Microscopy (LM) colouration of myalinized fibres, in which vertical bundles of afferent/efferent fibres that could support the cortex module are identified. We conclude that sensory maps seem not to be an expression for simple specific connectivity, but rather to be functional defined. We also conclude that evidence for the existence of the postulated module or column does not exist in the discussed material. This opens up for an important discussion of the brain as functionally directed by biological information (information-directed self-organisation), and for consciousness being closely linked to the structure of the universe at large. Consciousness is thus not a local phenomena limited to the brain, but a much more global phenomena connected to the wholeness of the world.

scholarly journals Preferential Pharmacological Inhibition of Nav1.6, but not Nav1.1, Abolishes Epileptiform Activity Induced by 4-AP in Mouse Cortical Slices

2020 ◽  
Author(s):  
Reesha R. Patel ◽  
Xingjie Ping ◽  
Shaun R. Patel ◽  
Jeff S. McDermott ◽  
Jeffrey L. Krajewski ◽  
...  
Keyword(s):  
Expression Patterns ◽  
Epileptiform Activity ◽  
Inhibitory Neurons ◽  
Hek293 Cells ◽  
Functional Roles ◽  
Voltage Gated Sodium Channels ◽  
Whole Cell Patch Clamp ◽  
Epileptic Syndromes ◽  
The Brain ◽  
Cortical Slices

ABSTRACTBrain isoforms of voltage-gated sodium channels (VGSCs) have distinct cellular and subcellular expression patterns as well as functional roles that are critical for normal physiology as aberrations in their expression or activity lead to pathophysiological conditions. In this study, we asked how inhibition of select isoforms of VGSCs alters epileptiform activity to further parse out the roles of VGSCs in the brain. We first determined the relative selectivity of recently discovered small molecule, aryl sulfonamide, inhibitors (ICA-121431 and Compound 801) against Nav1.1, Nav1.2, and Nav1.6 activity using whole-cell patch clamp recordings obtained from HEK293 cells. To test the effects of these inhibitors on epileptiform activity, we obtained multielectrode array (MEA) recordings from mouse cortical slices in the presence of 4-aminopyridine (4-AP) to induce epileptiform activity. We found that the ICA-121431 and Compound 801 compounds are relatively selective for Nav1.1 and Nav1.6, respectively. From the MEA recordings, we found that inhibition of Nav1.6 and Nav1.2 with 500nM of the Compound 801 compound completely abolishes ictal local field potentials induced by 4-AP, whereas inhibition of Nav1.1 with 500nM of the ICA-121431 compound had minimal effect on epileptiform activity induced by 4-AP. Due to the prominent expression of Nav1.1 in inhibitory neurons, we asked whether inhibition of Nav1.1 alone alters activity. We found that, indeed, inhibition of Nav1.1 with the ICA-121431 compound increased basal activity in the absence of 4-AP. These findings expand our current understanding of the roles of VGSC isoforms in the brain and suggest that selective targeting of Nav1.6 may be a more efficacious treatment strategy for epileptic syndromes.

scholarly journals Honeybee Brain Oscillations Are Generated by Microtubules. The Concept of a Brain Central Oscillator

2021 ◽  
Vol 14 ◽  
Author(s):  
Brenda C. Gutierrez ◽  
Marcelo R. Pita Almenar ◽  
Luciano J. Martínez ◽  
Manuel Siñeriz Louis ◽  
Virginia H. Albarracín ◽  
...  
Keyword(s):  
Field Potential ◽  
Oscillatory Activity ◽  
Mammalian Brain ◽  
Insect Brain ◽  
Patch Clamp Technique ◽  
Brain Wave ◽  
Signaling Mechanism ◽  
Brain Wave Activity ◽  
The Brain ◽  
Honeybee Brain

Microtubules (MTs) are important structures of the cytoskeleton in neurons. Mammalian brain MTs act as biomolecular transistors that generate highly synchronous electrical oscillations. However, their role in brain function is largely unknown. To gain insight into the MT electrical oscillatory activity of the brain, we turned to the honeybee (Apis mellifera) as a useful model to isolate brains and MTs. The patch clamp technique was applied to MT sheets of purified honeybee brain MTs. High resistance seal patches showed electrical oscillations that linearly depended on the holding potential between ± 200 mV and had an average conductance in the order of ~9 nS. To place these oscillations in the context of the brain, we also explored local field potential (LFP) recordings from the Triton X-permeabilized whole honeybee brain unmasking spontaneous oscillations after but not before tissue permeabilization. Frequency domain spectral analysis of time records indicated at least two major peaks at approximately ~38 Hz and ~93 Hz in both preparations. The present data provide evidence that MT electrical oscillations are a novel signaling mechanism implicated in brain wave activity observed in the insect brain.

scholarly journals Voltage-based inhibitory synaptic plasticity: network regulation, diversity, and flexibility

2020 ◽  
Author(s):  
Victor Pedrosa ◽  
Claudia Clopath
Keyword(s):  
Synaptic Plasticity ◽  
Pyramidal Cell ◽  
Brain Regions ◽  
Postsynaptic Membrane ◽  
Network Activity ◽  
Inhibitory Neurons ◽  
Plasticity Model ◽  
Feedforward Network ◽  
Voltage Dependent ◽  
Homeostatic Mechanisms

AbstractNeural networks are highly heterogeneous while homeostatic mechanisms ensure that this heterogeneity is kept within a physiologically safe range. One of such homeostatic mechanisms, inhibitory synaptic plasticity, has been observed across different brain regions. Computationally, however, inhibitory synaptic plasticity models often lead to a strong suppression of neuronal diversity. Here, we propose a model of inhibitory synaptic plasticity in which synaptic updates depend on presynaptic spike arrival and postsynaptic membrane voltage. Our plasticity rule regulates the network activity by setting a target value for the postsynaptic membrane potential over a long timescale. In a feedforward network, we show that our voltage-dependent inhibitory synaptic plasticity (vISP) model regulates the excitatory/inhibitory ratio while allowing for a broad range of postsynaptic firing rates and thus network diversity. In a feedforward network in which excitatory and inhibitory neurons receive correlated input, our plasticity model allows for the development of co-tuned excitation and inhibition, in agreement with recordings in rat auditory cortex. In recurrent networks, our model supports memory formation and retrieval while allowing for the development of heterogeneous neuronal activity. Finally, we implement our vISP rule in a model of the hippocampal CA1 region whose pyramidal cell excitability differs across cells. This model accounts for the experimentally observed variability in pyramidal cell features such as the number of place fields, the fields sizes, and the portion of the environment covered by each cell. Importantly, our model supports a combination of sparse and dense coding in the hippocampus. Therefore, our voltage-dependent inhibitory plasticity model accounts for network homeostasis while allowing for diverse neuronal dynamics observed across brain regions.

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