Spatiotemporal Synchronization of Neuronal Activity in a Hippocampal CA3 Network Model Including the O-LM Cell

Author(s):  
Seiichi Matsubara ◽  
Hatsuo Hayashi ◽  
Kazuki Nakada
2010 ◽  
Vol 22 (5) ◽  
pp. 1312-1332 ◽  
Author(s):  
Samat Moldakarimov ◽  
Maxim Bazhenov ◽  
Terrence J. Sejnowski

Perceiving and identifying an object is improved by prior exposure to the object. This perceptual priming phenomenon is accompanied by reduced neural activity. But whether suppression of neuronal activity with priming is responsible for the improvement in perception is unclear. To address this problem, we developed a rate-based network model of visual processing. In the model, decreased neural activity following priming was due to stimulus-specific sharpening of representations taking place in the early visual areas. Representation sharpening led to decreased interference of representations in higher visual areas that facilitated selection of one of the competing representations, thereby improving recognition. The model explained a wide range of psychophysical and physiological data observed in priming experiments, including antipriming phenomena, and predicted two functionally distinct stages of visual processing.


2019 ◽  
Author(s):  
Ankur Sinha ◽  
Christoph Metzner ◽  
Neil Davey ◽  
Roderick Adams ◽  
Michael Schmuker ◽  
...  

AbstractSeveral homeostatic mechanisms enable the brain to maintain desired levels of neuronal activity. One of these, homeostatic structural plasticity, has been reported to restore activity in networks disrupted by peripheral lesions by altering their neuronal connectivity. While multiple lesion experiments have studied the changes in neurite morphology that underlie modifications of synapses in these networks, the underlying mechanisms that drive these changes are yet to be explained. Evidence suggests that neuronal activity modulates neurite morphology and may stimulate neurites to selective sprout or retract to restore network activity levels. We developed a new spiking network model, simulations of which accurately reproduce network rewiring after peripheral lesions as reported in experiments, to study these activity dependent growth regimes of neurites. To ensure that our simulations closely resemble the behaviour of networks in the brain, we deafferent a biologically realistic network model that exhibits low frequency Asynchronous Irregular (AI) activity as observed in cerebral cortex.Our simulation results indicate that the re-establishment of activity in neurons both within and outside the deprived region, the Lesion Projection Zone (LPZ), requires opposite activity dependent growth rules for excitatory and inhibitory post-synaptic elements. Analysis of these growth regimes indicates that they also contribute to the maintenance of activity levels in individual neurons. Furthermore, in our model, the directional formation of synapses that is observed in experiments requires that pre-synaptic excitatory and inhibitory elements also follow opposite growth rules. Lastly, we observe that our proposed model of homeostatic structural plasticity and the inhibitory synaptic plasticity mechanism that also balances our AI network are both necessary for successful rewiring of the network.Author summaryAn accumulating body of evidence suggests that our brain can compensate for peripheral lesions by adaptive rewiring of its neuronal circuitry. The underlying process, structural plasticity, can modify the connectivity of neuronal networks in the brain, thus affecting their function. To better understand the mechanisms of structural plasticity in the brain, we have developed a novel model of peripheral lesions and the resulting activity-dependent rewiring in a simplified cortical network model that exhibits biologically realistic asynchronous irregular activity. In order to accurately reproduce the directionality and time course of rewiring after injury that is observed in peripheral lesion experiments, we derive activity dependent growth rules for different synaptic elements: dendritic and axonal contacts. Our simulation results suggest that excitatory and inhibitory synaptic elements have to react to changes in neuronal activity in opposite ways. We show that these rules result in a homeostatic stabilisation of activity in individual neurons. In our simulations, both synaptic and structural plasticity mechanisms are necessary for network repair. Furthermore, our simulations indicate that while activity is restored in neurons deprived by the peripheral lesion, the temporal firing characteristics of the network can be changed by the rewiring process.


2021 ◽  
Vol 17 (6) ◽  
pp. e1008996
Author(s):  
Ankur Sinha ◽  
Christoph Metzner ◽  
Neil Davey ◽  
Roderick Adams ◽  
Michael Schmuker ◽  
...  

Several homeostatic mechanisms enable the brain to maintain desired levels of neuronal activity. One of these, homeostatic structural plasticity, has been reported to restore activity in networks disrupted by peripheral lesions by altering their neuronal connectivity. While multiple lesion experiments have studied the changes in neurite morphology that underlie modifications of synapses in these networks, the underlying mechanisms that drive these changes are yet to be explained. Evidence suggests that neuronal activity modulates neurite morphology and may stimulate neurites to selective sprout or retract to restore network activity levels. We developed a new spiking network model of peripheral lesioning and accurately reproduced the characteristics of network repair after deafferentation that are reported in experiments to study the activity dependent growth regimes of neurites. To ensure that our simulations closely resemble the behaviour of networks in the brain, we model deafferentation in a biologically realistic balanced network model that exhibits low frequency Asynchronous Irregular (AI) activity as observed in cerebral cortex. Our simulation results indicate that the re-establishment of activity in neurons both within and outside the deprived region, the Lesion Projection Zone (LPZ), requires opposite activity dependent growth rules for excitatory and inhibitory post-synaptic elements. Analysis of these growth regimes indicates that they also contribute to the maintenance of activity levels in individual neurons. Furthermore, in our model, the directional formation of synapses that is observed in experiments requires that pre-synaptic excitatory and inhibitory elements also follow opposite growth rules. Lastly, we observe that our proposed structural plasticity growth rules and the inhibitory synaptic plasticity mechanism that also balances our AI network both contribute to the restoration of the network to pre-deafferentation stable activity levels.


2016 ◽  
Vol 28 (1) ◽  
pp. 187-215 ◽  
Author(s):  
Osamu Hoshino ◽  
Meihong Zheng ◽  
Kazuo Watanabe

Variability is a prominent characteristic of cognitive brain function. For instance, different trials of presentation of the same stimulus yield higher variability in its perception: subjects sometimes fail in perceiving the same stimulus. Perceptual variability could be attributable to ongoing-spontaneous fluctuation in neuronal activity prior to sensory stimulation. Simulating a cortical neural network model, we investigated the underlying neuronal mechanism of perceptual variability in relation to variability in ongoing-spontaneous neuronal activity. In the network model, populations of principal cells (cell assemblies) encode information about sensory features. Each cell assembly is sensitive to one particular feature stimulus. Transporters on GABAergic interneurons regulate ambient GABA concentration in a neuronal activity-dependent manner. Ambient GABA molecules activate extrasynaptic GABA[Formula: see text] receptors on principal cells and interneurons, and provide them with tonic inhibitory currents. We controlled the variability of ongoing-spontaneous neuronal activity by manipulating the basal level of ambient GABA and assessed the perceptual performance of the network: detection of a feature stimulus. In an erroneous response, stimulus-irrelevant but not stimulus-relevant principal cells were activated, generating trains of action potentials. Perceptual variability, reflected in error rate in detecting the same stimulus that was presented repeatedly to the network, was increased as the variability in ongoing-spontaneous membrane potential among cell assemblies increased. Frequent, transient membrane depolarization below firing threshold was the major cause of the increased neuronal variability, for which a decrease in basal ambient GABA concentration was responsible. We suggest that ambient GABA in the brain may have a role in reducing the variability in ongoing-spontaneous neuronal activity, leading to a decrease in perceptual variability and therefore to reliable sensory perception.


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