Faculty Opinions recommendation of Defining POMC neurons using transgenic reagents: impact of transient Pomc expression in diverse immature neuronal populations.

2012 ◽  
Author(s):  
Andrzej Bartke
Keyword(s):  
Neuronal Populations

scholarly journals Understanding Emotions: Origins and Roles of the Amygdala

Biomolecules ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 823
Author(s):  
Goran Šimić ◽  
Mladenka Tkalčić ◽  
Vana Vukić ◽  
Damir Mulc ◽  
Ena Španić ◽  
...  
Keyword(s):  
Sensory Information ◽  
Anterior Cingulate ◽  
Central Nucleus ◽  
Subcortical Structures ◽  
Neuronal Populations ◽  
Efferent Projections ◽  
Short And Long Term ◽  
Understanding Emotions ◽  
Fight Or Flight Response ◽  
And Behavior

Emotions arise from activations of specialized neuronal populations in several parts of the cerebral cortex, notably the anterior cingulate, insula, ventromedial prefrontal, and subcortical structures, such as the amygdala, ventral striatum, putamen, caudate nucleus, and ventral tegmental area. Feelings are conscious, emotional experiences of these activations that contribute to neuronal networks mediating thoughts, language, and behavior, thus enhancing the ability to predict, learn, and reappraise stimuli and situations in the environment based on previous experiences. Contemporary theories of emotion converge around the key role of the amygdala as the central subcortical emotional brain structure that constantly evaluates and integrates a variety of sensory information from the surroundings and assigns them appropriate values of emotional dimensions, such as valence, intensity, and approachability. The amygdala participates in the regulation of autonomic and endocrine functions, decision-making and adaptations of instinctive and motivational behaviors to changes in the environment through implicit associative learning, changes in short- and long-term synaptic plasticity, and activation of the fight-or-flight response via efferent projections from its central nucleus to cortical and subcortical structures.

Mesencephalic GABA neuronal development: no more on the other side of oblivion

2014 ◽  
Vol 5 (5) ◽  
pp. 371-382 ◽  
Author(s):  
Suyan Li ◽  
Sampada Joshee ◽  
Anju Vasudevan
Keyword(s):  
Transcriptional Control ◽  
Neuronal Development ◽  
Developmental Regulation ◽  
Molecular Characteristics ◽  
Psychiatric Illnesses ◽  
Neuronal Populations ◽  
Gaba Neurons ◽  
Recent Developments ◽  
And Function ◽  
The Brain

AbstractMidbrain GABA neurons, endowed with multiple morphological, physiological and molecular characteristics as well as projection patterns are key players interacting with diverse regions of the brain and capable of modulating several aspects of behavior. The diversity of these GABA neuronal populations based on their location and function in the dorsal, medial or ventral midbrain has challenged efforts to rapidly uncover their developmental regulation. Here we review recent developments that are beginning to illuminate transcriptional control of GABA neurons in the embryonic midbrain (mesencephalon) and discuss its implications for understanding and treatment of neurological and psychiatric illnesses.

scholarly journals Quantitative investigation of memory recall performance of a computational microcircuit model of the hippocampus

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Nikolaos Andreakos ◽  
Shigang Yue ◽  
Vassilis Cutsuridis
Keyword(s):  
Recall Performance ◽  
Network Size ◽  
Excitatory Input ◽  
Quantitative Investigation ◽  
Great Cost ◽  
Neuronal Populations ◽  
Global Threshold ◽  
Tightly Coupled ◽  
Memory Pattern ◽  
Determining Factor

AbstractMemory, the process of encoding, storing, and maintaining information over time to influence future actions, is very important in our lives. Losing it, it comes with a great cost. Deciphering the biophysical mechanisms leading to recall improvement should thus be of outmost importance. In this study, we embarked on the quest to improve computationally the recall performance of a bio-inspired microcircuit model of the mammalian hippocampus, a brain region responsible for the storage and recall of short-term declarative memories. The model consisted of excitatory and inhibitory cells. The cell properties followed closely what is currently known from the experimental neurosciences. Cells’ firing was timed to a theta oscillation paced by two distinct neuronal populations exhibiting highly regular bursting activity, one tightly coupled to the trough and the other to the peak of theta. An excitatory input provided to excitatory cells context and timing information for retrieval of previously stored memory patterns. Inhibition to excitatory cells acted as a non-specific global threshold machine that removed spurious activity during recall. To systematically evaluate the model’s recall performance against stored patterns, pattern overlap, network size, and active cells per pattern, we selectively modulated feedforward and feedback excitatory and inhibitory pathways targeting specific excitatory and inhibitory cells. Of the different model variations (modulated pathways) tested, ‘model 1’ recall quality was excellent across all conditions. ‘Model 2’ recall was the worst. The number of ‘active cells’ representing a memory pattern was the determining factor in improving the model’s recall performance regardless of the number of stored patterns and overlap between them. As ‘active cells per pattern’ decreased, the model’s memory capacity increased, interference effects between stored patterns decreased, and recall quality improved.

The densitometric physical fractionator for counting neuronal populations: application to a mouse model of familial amyotrophic lateral sclerosis

2003 ◽  
Vol 129 (1) ◽  
pp. 61-71 ◽  
Author(s):  
Giuseppe Luca Ciavarro ◽  
Novella Calvaresi ◽  
Andrea Botturi ◽  
Caterina Bendotti ◽  
Giuseppe Andreoni ◽  
...  
Keyword(s):  
Amyotrophic Lateral Sclerosis ◽  
Mouse Model ◽  
Familial Amyotrophic Lateral Sclerosis ◽  
Neuronal Populations ◽  
Lateral Sclerosis

scholarly journals Activity Correlation Imaging: Visualizing Function and Structure of Neuronal Populations

2009 ◽  
Vol 96 (9) ◽  
pp. 3801-3809 ◽  
Author(s):  
Stephan Junek ◽  
Tsai-Wen Chen ◽  
Mihai Alevra ◽  
Detlev Schild
Keyword(s):  
Neuronal Populations
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