Excitatory and Inhibitory Synaptic Placement and Functional Implications

Villa, Katherine L.; Nedivi, Elly

doi:10.1007/978-4-431-56050-0_18

Cited by 12 publications

(15 citation statements)

References 143 publications

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“…> and > , will provide the necessary conditions for an NBR to occur. Assuming > is actually compatible with inhibitory synapses closer to the soma (Megıás et al, 2001;Villa and Nedivi, 2016) and it has been adopted in the computational neuroscience literature (David et al, 2006;Nunez, 1995;Robinson, 2005). The spike (positive) and slow wave (negative) change in neuronal activity shown in the simulations of Figure 2 is apparently in contradiction with the EEG traces observed during these events, in which voltage deflections for both phases have the same polarity (Pittau et al, 2013).…”

Section: Discussionmentioning

confidence: 97%

“…Irrespective of the value of , a PBR is obtained when = 0; while an inhibitory-related NBR is obtained for = 2 and = 2. We use this value of for all mechanisms since it is reasonable to assume that inhibitory gains are stronger than excitatory ones-inhibitory interneurons tend to have synapses closer to the soma (Megıás et al, 2001;Villa and Nedivi, 2016). We use the PBR configuration in region 1, i.e.…”

Section: Model Type Equation Variable Referencementioning

confidence: 99%

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Identification of negative BOLD responses using windkessel models

Valdés-Hernández¹,

Bernal

Moshkforoush³

et al. 2018

Preprint

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Alongside positive BOLD responses (PBR), a variety of negative BOLD responses (NBR) with distinct underlying mechanisms also occur. We identify five mechanisms of NBR: i) local/lateral/contralateral inhibition (LCI), ii) neuronal disruption of network activity (NDA), iii) altered balance of neuro-metabolic/vascular couplings (ANC), iv) arterial blood stealing (ABS), and v) venous blood backpressure (VBB). Detecting and classifying these mechanisms from BOLD signals is pivotal in understanding normal/pathological brain functions. This requires models and parameters with anatomical/functional interpretation that furnish the understanding of how these mechanisms are fingerprinted by their BOLD responses. Here, we used a windkessel model with viscoelastic compliance as well as dynamics of both neuronal and tissue/blood O2 to investigate the generation, detection, classification and interpretation of the BOLD hemodynamic response functions (HRF) of above mechanisms. Firstly, we evaluated the use of the general linear model to detect simulated NBRs. Secondly, we tested the ability of a machine learning classifier, built from a simulated ensemble of HRFs, to predict the mechanism underlying a new HRF.Crossvalidation indicates NDA and ANC can accurately be classified solely from fMRI BOLD signals; while LCI, ABS and VBB might require additional imaging modalities. Thirdly, we demonstrated that estimators of the model parameters determinant in the NBRs formation are accurate, and precise to certain resolutions. Finally, we successfully applied our detection/classification/estimation methodology to EEG-fMRI data in a clinical situation where several of these mechanisms could coexist. We believe that the proper identification and interpretation of NBR mechanisms have important clinical and cognitive implications in fMRI studies.

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Section: Discussionmentioning

confidence: 97%

Section: Model Type Equation Variable Referencementioning

confidence: 99%

Identification of negative BOLD responses using windkessel models

Valdés-Hernández¹,

Bernal

Moshkforoush³

et al. 2018

Preprint

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“…A typical pyramidal neuron of the cortex or of the hippocampus subfields receives thousands of synaptic inputs (3000-30,000) [4][5][6]. The larger parts of these inputs (80%) are excitatory inputs which use glutamate (Glu) as neurotransmitter.…”

Section: Introductionmentioning

confidence: 99%

“…It is then reasonable to assume that these synapses are the most important way of information transfer and elaboration. Probably, the most important regulatory system of the activity of the glutamatergic pyramidal neurons is given by the inhibitory neurons which use the γ-aminobutyric acid (GABA) as neurotransmitter [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…GABAergic synapses represent between 10 and 20% of the synapses inputting on a pyramidal neuron, and they are located in strategic positions on the shaft of the dendritic branches among the excitatory glutamatergic synapses [4][5][6]. Glutamatergic synapses are normally located on spines (a sort of elongation) protruding from the shaft of the dendritic branches.…”

Section: Introductionmentioning

confidence: 99%

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Information Processing and Synaptic Transmission

Maio¹,

Santillo²

2020

Advances in Neural Signal Processing

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The brain is probably the most complex machinery for information processing we can imagine. The amount of data it manages is extremely huge. Any conscious or unconscious event both internal and coming from the environment needs to be perceived, elaborated, and responded with an appropriate action. Moreover, the high-level activities of mind require the connection of logical elaboration, the relationship with past experience (memory), and the transfer of information among different areas of the brain participating to the elaboration of the thought. Almost all brain illnesses or even simple defaults can be related to a corruption of the basic system which manage information in the brain. The main actors in transferring and managing information are the synapses, and then the understanding of the brain information processing cannot disregard the full understanding of the synaptic functionality. In the present chapter, by using as example the most common type of the brain synapse (the glutamatergic synapse), we will present the basic mechanism of synaptic transmission stressing some of the most relevant mechanisms which regulate the transfer and management of information.

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