Spatiotemporal Patterns of Granule Cell Activity Revealed by a Large-Scale, Biologically Realistic Model of the Hippocampal Dentate Gyrus

Yu, Gene J.; Hendrickson, Phillip J.; Song, Dong; Berger, Theodore W.

doi:10.1007/978-3-319-99103-0_12

Cited by 3 publications

(8 citation statements)

References 87 publications

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“…The entorhinal-dentate network used in the present study was the same as described in Yu et al (2018Yu et al ( , 2019 and is extensively described there. Dentate granule cells were represented using a simplified morphology that was constructed using the same technique as for the CA3 pyramidal cell models (Marasco et al, 2012).…”

Section: Neuron Modelsmentioning

confidence: 99%

“…Pairwise spike correlations between neurons have been shown to capture much of the statistical properties of a single neuron and provide a measure for studying the properties of population activity (Helias et al, 2014;Dettner et al, 2016). Weak pairwise correlations have been demonstrated to give rise to emergent spatiotemporal structures in population activity (Halliday, 2000;Schneidman et al, 2006;Kriener et al, 2009;Renart et al, 2010;Senk et al, 2018;Yu et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Such studies have also revealed how the interactions between feedforward and recurrent inhibitory circuits nonlinearly affect the spatial structure of correlation. Beyond spatial correlation, the temporal correlation can also be considered in the correlation structure which is determined by the electrophysiology of the postsynaptic neuron and the synaptic properties (Tetzlaff et al, 2008;Hong et al, 2012;Chan et al, 2016;Yu et al, 2018). Given these principles, we investigated the population dynamics and spatiotemporal correlation structure that resulted from different types of connectivity in a hippocampus-specific context.…”

Section: Introductionmentioning

confidence: 99%

“…Using an entorhinal-dentate network, we had previously revealed a role of anatomically-constrained connectivity in organizing random inputs into spatially and temporally dense regions of activity called clusters (Hendrickson et al, 2016;Yu et al, 2018). The spatial properties of the clusters were found to be influenced by the anatomy of the axonal projections, i.e., the spatial extent of the axon terminal field.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Topographic Organization of Correlation Along the Longitudinal and Transverse Axes in Rat Hippocampal CA3 Due to Excitatory Afferents

Bouteiller

Berger

2020

Front. Comput. Neurosci.

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The topographic organization of afferents to the hippocampal CA3 subfield are well-studied, but their role in influencing the spatiotemporal dynamics of population activity is not understood. Using a large-scale, computational neuronal network model of the entorhinal-dentate-CA3 system, the effects of the perforant path, mossy fibers, and associational system on the propagation and transformation of network spiking patterns were investigated. A correlation map was constructed to characterize the spatial structure and temporal evolution of pairwise correlations which underlie the emergent patterns found in the population activity. The topographic organization of the associational system gave rise to changes in the spatial correlation structure along the longitudinal and transverse axes of the CA3. The resulting gradients may provide a basis for the known functional organization observed in hippocampus.

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Section: Neuron Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Topographic Organization of Correlation Along the Longitudinal and Transverse Axes in Rat Hippocampal CA3 Due to Excitatory Afferents

Bouteiller

Berger

2020

Front. Comput. Neurosci.

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“…Model components (nodes, synapses, connectivity) should have to mimic the operations of real neurons, synapses and circuits. Several strides toward this direction have already been made (Cutsuridis and Wenneckers, 2009; Cutsuridis et al, 2010b, 2011; Cutsuridis and Hasselmo, 2012; Schneider et al, 2012; Pendyam et al, 2013; Bezaire et al, 2016; Sanjay and Krothapalli, 2019; Yu et al, 2019). One such stride was the Cutsuridis et al (2010b) microcircuit model of region CA1 dynamics in encoding and retrieval of memories.…”

mentioning

confidence: 99%

Memory Prosthesis: Is It Time for a Deep Neuromimetic Computing Approach?

Cutsuridis

2019

Front. Neurosci.

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Memory loss, one of the most dreaded afflictions of the human condition, presents considerable burden on the world's health care system and it is recognized as a major challenge in the elderly. There are only a few neuromodulation treatments for memory dysfunctions. Open loop deep brain stimulation is such a treatment for memory improvement, but with limited success and conflicting results. In recent years closed-loop neuroprosthesis systems able to simultaneously record signals during behavioral tasks and generate with the use of internal neural factors the precise timing of stimulation patterns are presented as attractive alternatives and show promise in memory enhancement and restoration. A few such strides have already been made in both animals and humans, but with limited insights into their mechanisms of action. Here, I discuss why a deep neuromimetic computing approach linking multiple levels of description, mimicking the dynamics of brain circuits, interfaced with recording and stimulating electrodes could enhance the performance of current memory prosthesis systems, shed light into the neurobiology of learning and memory and accelerate the progress of memory prosthesis research. I propose what the necessary components (nodes, structure, connectivity, learning rules, and physiological responses) of such a deep neuromimetic model should be and what type of data are required to train/test its performance, so it can be used as a true substitute of damaged brain areas capable of restoring/enhancing their missing memory formation capabilities. Considerations to neural circuit targeting, tissue interfacing, electrode placement/implantation, and multi-network interactions in complex cognition are also provided.

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Generation of Granule Cell Dendritic Morphologies by Estimating the Spatial Heterogeneity of Dendritic Branching

Berger

2020

Front. Comput. Neurosci.

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Biological realism of dendritic morphologies is important for simulating electrical stimulation of brain tissue. By adding point process modeling and conditional sampling to existing generation strategies, we provide a novel means of reproducing the nuanced branching behavior that occurs in different layers of granule cell dendritic morphologies. In this study, a heterogeneous Poisson point process was used to simulate branching events. Conditional distributions were then used to select branch angles depending on the orthogonal distance to the somatic plane. The proposed method was compared to an existing generation tool and a control version of the proposed method that used a homogeneous Poisson point process. Morphologies were generated with each method and then compared to a set of digitally reconstructed neurons. The introduction of a conditionally dependent branching rate resulted in the generation of morphologies that more accurately reproduced the emergent properties of dendritic material per layer, Sholl intersections, and proximal passive current flow. Conditional dependence was critically important for the generation of realistic granule cell dendritic morphologies.

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Spatiotemporal Patterns of Granule Cell Activity Revealed by a Large-Scale, Biologically Realistic Model of the Hippocampal Dentate Gyrus

Cited by 3 publications

References 87 publications

Topographic Organization of Correlation Along the Longitudinal and Transverse Axes in Rat Hippocampal CA3 Due to Excitatory Afferents

Topographic Organization of Correlation Along the Longitudinal and Transverse Axes in Rat Hippocampal CA3 Due to Excitatory Afferents

Memory Prosthesis: Is It Time for a Deep Neuromimetic Computing Approach?

Generation of Granule Cell Dendritic Morphologies by Estimating the Spatial Heterogeneity of Dendritic Branching

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