scholarly journals Self-organization of modular network architecture by activity-dependent neuronal migration and outgrowth

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Samora Okujeni ◽  
Ulrich Egert
Keyword(s):  
Neurite Outgrowth ◽  
Cortical Neurons ◽  
Network Architecture ◽  
Activity Levels ◽  
Range Interaction ◽  
Firing Rates ◽  
Rat Cortical Neurons ◽  
Average Activity ◽  
Highly Correlated ◽  
Activity Dependent

The spatial distribution of neurons and activity-dependent neurite outgrowth shape long-range interaction, recurrent local connectivity and the modularity in neuronal networks. We investigated how this mesoscale architecture develops by interaction of neurite outgrowth, cell migration and activity in cultured networks of rat cortical neurons and show that simple rules can explain variations of network modularity. In contrast to theoretical studies on activity-dependent outgrowth but consistent with predictions for modular networks, spontaneous activity and the rate of synchronized bursts increased with clustering, whereas peak firing rates in bursts increased in highly interconnected homogeneous networks. As Ca2+ influx increased exponentially with increasing network recruitment during bursts, its modulation was highly correlated to peak firing rates. During network maturation, long-term estimates of Ca2+ influx showed convergence, even for highly different mesoscale architectures, neurite extent, connectivity, modularity and average activity levels, indicating homeostatic regulation towards a common set-point of Ca2+ influx.

scholarly journals Compensatory variability in network parameters enhances memory performance in the Drosophila mushroom body

2021 ◽  
Author(s):  
Nada Y. Abdelrahman ◽  
Eleni Vasilaki ◽  
Andrew C. Lin
Keyword(s):  
Mushroom Body ◽  
Neural Circuit ◽  
Memory Performance ◽  
Activity Levels ◽  
Compensatory Mechanisms ◽  
Network Parameters ◽  
Kenyon Cells ◽  
Average Activity ◽  
Circuit Function ◽  
Activity Dependent

AbstractNeural circuits use homeostatic compensation to achieve consistent behaviour despite variability in underlying intrinsic and network parameters. However, it remains unclear how compensation regulates variability across a population of the same type of neurons within an individual, and what computational benefits might result from such compensation. We address these questions in the Drosophila mushroom body, the fly’s olfactory memory center. In a computational model, we show that memory performance is degraded when the mushroom body’s principal neurons, Kenyon cells (KCs), vary realistically in key parameters governing their excitability, because the resulting inter-KC variability in average activity levels makes odor representations less separable. However, memory performance is rescued while maintaining realistic variability if parameters compensate for each other to equalize KC average activity. Such compensation can be achieved through both activity-dependent and activity-independent mechanisms. Finally, we show that correlations predicted by our model’s compensatory mechanisms appear in the Drosophila hemibrain connectome. These findings reveal compensatory variability in the mushroom body and describe its computational benefits for associative memory.Significance statementHow does variability between neurons affect neural circuit function? How might neurons behave similarly despite having different underlying features? We addressed these questions in neurons called Kenyon cells, which store olfactory memories in flies. Kenyon cells differ among themselves in key features that affect how active they are, and in a model of the fly’s memory circuit, adding this inter-neuronal variability made the model fly worse at learning the values of multiple odors. However, memory performance was rescued if compensation between the variable underlying features allowed Kenyon cells to be equally active on average, and we found the hypothesized compensatory variability in real Kenyon cells’ anatomy. This work reveals the existence and computational benefits of compensatory variability in neural networks.

scholarly journals Activity-Dependent A-to-I RNA Editing in Rat Cortical Neurons

Genetics ◽  
2012 ◽  
Vol 192 (1) ◽  
pp. 281-287 ◽  
Author(s):  
Neville E. Sanjana ◽  
Erez Y. Levanon ◽  
Emily A. Hueske ◽  
Jessica M. Ambrose ◽  
Jin Billy Li
Keyword(s):  
Rna Editing ◽  
Cortical Neurons ◽  
Rat Cortical Neurons ◽  
Activity Dependent

scholarly journals State-dependent modulation of activity in distinct layer 6 corticothalamic neurons in barrel cortex of awake mice

2021 ◽  
Author(s):  
Suryadeep Dash ◽  
Dawn M Autio ◽  
Shane R Crandall
Keyword(s):  
Cortical Neurons ◽  
Barrel Cortex ◽  
Related Activity ◽  
Functional Roles ◽  
Firing Rates ◽  
State Dependent ◽  
Rich Diversity ◽  
Response Profiles ◽  
Strategic Position ◽  
Activity Dependent

Layer 6 corticothalamic (L6 CT) neurons are in a strategic position to control sensory input to the neocortex, yet we understand very little about their functions. Apart from studying their anatomical, physiological and synaptic properties, most recent efforts have focused on the activity-dependent influences CT cells can exert on thalamic and cortical neurons through causal optogenetic manipulations. However, few studies have attempted to study them during behavior. In an attempt to address this gap, we performed juxtacellular recordings from optogenetically identified CT neurons in whisker-related primary somatosensory cortex (wS1) of awake, head-fixed mice (of either sex) free to rest quietly or self-initiate bouts of whisking and locomotion. We found a rich diversity of response profiles exhibited by CT cells. Broadly, their spiking patterns were either modulated by whisker-related behavior (~28%) or not (~72%). Whisker-related neurons exhibited both increases as well as decreases in firing rates. We also encountered cells with preceding modulations in firing rate before whisking onset. Overall, whisking better explained these changes in rates than overall changes in arousal. The CT cells that showed no whisker-related activity had low spontaneous firing rates (<0.5 Hz), with many all but silent. Remarkably, this relatively silent population preferentially spiked at the state transition point when pupil diameter constricted and the mouse entered quiet wakefulness. Thus, our results demonstrate that L6 CT neurons in wS1 show diverse spiking patterns, perhaps subserving distinct functional roles related to precisely timed responses during complex behaviors and transitions between discrete waking states.

Distinct regulation of activity-dependent transcription of immediate early genes in cultured rat cortical neurons

2017 ◽  
Vol 490 (3) ◽  
pp. 682-687 ◽  
Author(s):  
Mamoru Fukuchi ◽  
Tomofumi Sanabe ◽  
Toshifumi Watanabe ◽  
Takane Kubota ◽  
Akiko Tabuchi ◽  
...  
Keyword(s):  
Cortical Neurons ◽  
Immediate Early Genes ◽  
Immediate Early ◽  
Early Genes ◽  
Rat Cortical Neurons ◽  
Regulation Of Activity ◽  
Activity Dependent

Survival and neurite outgrowth of rat cortical neurons in three-dimensional agarose and collagen gel matrices

2001 ◽  
Vol 304 (3) ◽  
pp. 189-193 ◽  
Author(s):  
Stephen M O'Connor ◽  
David A Stenger ◽  
Kara M Shaffer ◽  
Wu Ma
Keyword(s):  
Neurite Outgrowth ◽  
Cortical Neurons ◽  
Three Dimensional ◽  
Collagen Gel ◽  
Rat Cortical Neurons
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