Intrinsic rescaling of granule cells restores pattern separation ability of a dentate gyrus network model during epileptic hyperexcitability

Hippocampus ◽  
2014 ◽  
Vol 25 (3) ◽  
pp. 297-308 ◽  
Author(s):  
Man Yi Yim ◽  
Alexander Hanuschkin ◽  
Jakob Wolfart
Keyword(s):  
Dentate Gyrus ◽  
Network Model ◽  
Granule Cells ◽  
Pattern Separation

scholarly journals Fast signaling and focal connectivity of PV+ interneurons ensure efficient pattern separation by lateral inhibition in a full-scale dentate gyrus network model

2019 ◽  
Author(s):  
Segundo Jose Guzman ◽  
Alois Schlögl ◽  
Claudia Espinoza ◽  
Xiaomin Zhang ◽  
Ben Suter ◽  
...  
Keyword(s):  
Dentate Gyrus ◽  
Network Model ◽  
Lateral Inhibition ◽  
Biological Networks ◽  
Granule Cells ◽  
Full Scale ◽  
Pattern Separation ◽  
Hippocampal Ca3 ◽  
Scale Network ◽  
Winner Takes All

ABSTRACTPattern separation is a fundamental brain computation that converts small differences in synaptic input patterns into large differences in action potential (AP) output patterns. Pattern separation plays a key role in the dentate gyrus, enabling the efficient storage and recall of memories in downstream hippocampal CA3 networks. Several mechanisms for pattern separation have been proposed, including expansion of coding space, sparsification of neuronal activity, and simple thresholding mechanisms. Alternatively, a winner-takes-all mechanism, in which the most excited cells inhibit all less-excited cells by lateral inhibition, might be involved. Although such a mechanism is computationally powerful, it remains unclear whether it operates in biological networks. Here, we develop a full-scale network model of the dentate gyrus, comprised of granule cells (GCs) and parvalbumin+ (PV+) inhibitory interneurons, based on experimentally determined biophysical cellular properties and synaptic connectivity rules. Our results demonstrate that a biologically realistic principal neuron–interneuron (PN–IN) network model is a highly efficient pattern separator. Mechanistic dissection in the model revealed that a winner-takes-all mechanism by lateral inhibition plays a crucial role in pattern separation. Furthermore, both fast signaling properties of PV+ interneurons and focal GC–interneuron connectivity are essential for efficient pattern separation. Thus, PV+ interneurons are not only involved in basic microcircuit functions, but also contribute to higher-order computations in neuronal networks, such as pattern separation.

scholarly journals Adult dentate gyrus neurogenesis: a functional model

2019 ◽  
Author(s):  
Olivia Gozel ◽  
Wulfram Gerstner
Keyword(s):  
Theoretical Model ◽  
Dentate Gyrus ◽  
Granule Cells ◽  
Functional Integration ◽  
Functional Model ◽  
Behavioral Pattern ◽  
Pattern Separation ◽  
Cell Maturation ◽  
Newborn Cell ◽  
Newborn Neurons

SummaryIn adult dentate gyrus neurogenesis, the link between maturation of newborn neurons and their function, such as behavioral pattern separation, has remained puzzling. By analyzing a theoretical model, we show that the switch from excitation to inhibition of the GABAergic input onto maturing newborn cells is crucial for their proper functional integration. When the GABAergic input is excitatory, cooperativity drives the growth of synapses such that newborn cells become sensitive to stimuli similar to those that activate mature cells. When GABAergic input switches to inhibitory, competition pushes the configuration of synapses onto newborn cells towards stimuli that are different from previously stored ones. This enables the maturing newborn cells to code for concepts that are novel, yet similar to familiar ones. Our theory of newborn cell maturation explains both how adult-born dentate granule cells integrate into the preexisting network and why they promote separation of similar but not distinct patterns.

scholarly journals Dentate Gyrus Mossy Cells Share a Role in Pattern Separation with Dentate Granule Cells and Proximal CA3 Pyramidal Cells

2019 ◽  
Vol 39 (48) ◽  
pp. 9570-9584 ◽  
Author(s):  
Douglas GoodSmith ◽  
Heekyung Lee ◽  
Joshua P. Neunuebel ◽  
Hongjun Song ◽  
James J. Knierim
Keyword(s):  
Dentate Gyrus ◽  
Granule Cells ◽  
Pyramidal Cells ◽  
Pattern Separation ◽  
Dentate Granule Cells ◽  
Mossy Cells ◽  
Ca3 Pyramidal Cells

scholarly journals A functional model of adult dentate gyrus neurogenesis

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Olivia Gozel ◽  
Wulfram Gerstner
Keyword(s):  
Theoretical Model ◽  
Dentate Gyrus ◽  
Granule Cells ◽  
Functional Integration ◽  
Functional Model ◽  
Behavioral Pattern ◽  
Pattern Separation ◽  
Cell Maturation ◽  
Newborn Cell ◽  
Newborn Neurons

In adult dentate gyrus neurogenesis, the link between maturation of newborn neurons and their function, such as behavioral pattern separation, has remained puzzling. By analyzing a theoretical model, we show that the switch from excitation to inhibition of the GABAergic input onto maturing newborn cells is crucial for their proper functional integration. When the GABAergic input is excitatory, cooperativity drives the growth of synapses such that newborn cells become sensitive to stimuli similar to those that activate mature cells. When GABAergic input switches to inhibitory, competition pushes the configuration of synapses onto newborn cells towards stimuli that are different from previously stored ones. This enables the maturing newborn cells to code for concepts that are novel, yet similar to familiar ones. Our theory of newborn cell maturation explains both how adult-born dentate granule cells integrate into the preexisting network and why they promote separation of similar but not distinct patterns.

scholarly journals Dendrites of DG granule cells contribute to pattern separation by controlling sparsity

2016 ◽  
Author(s):  
Spyridon Chavlis ◽  
Panagiotis C. Petrantonakis ◽  
Panayiota Poirazi
Keyword(s):  
Dentate Gyrus ◽  
Computational Model ◽  
Granule Cells ◽  
Separation Efficiency ◽  
Pattern Separation ◽  
Biologically Relevant ◽  
Incoming Information ◽  
And Performance ◽  
Dendritic Atrophy

AbstractThe hippocampus plays a key role in pattern separation, the process of transforming similar incoming information to highly dissimilar, non-overlapping representations. Sparse firing granule cells (GCs) in the dentate gyrus (DG) have been proposed to undertake this computation, but little is known about which of their properties influence pattern separation. Dendritic atrophy has been reported in diseases associated with pattern separation deficits, suggesting a possible role for dendrites in this phenomenon. To investigate whether and how the dendrites of GCs contribute to pattern separation, we build a simplified, biologically relevant, computational model of the DG. Our model suggests that the presence of GC dendrites is associated with high pattern separation efficiency while their atrophy leads to increased excitability and performance impairments. These impairments can be rescued by restoring GC sparsity to control levels through various manipulations. We predict that dendrites contribute to pattern separation as a mechanism for controlling sparsity.

scholarly journals Dentate gyrus somatostatin cells are required for contextual discrimination during episodic memory encoding

2019 ◽  
Author(s):  
Cristian Morales ◽  
Juan Facundo Morici ◽  
Nelson Espinosa ◽  
Agostina Sacson ◽  
Ariel Lara-Vasquez ◽  
...  
Keyword(s):  
Episodic Memory ◽  
Dentate Gyrus ◽  
Granule Cells ◽  
Molecular Layer ◽  
Memory Systems ◽  
Pattern Separation ◽  
Activity Levels ◽  
Memory Encoding ◽  
Somatostatin Cells ◽  
Somatostatin Neurons

AbstractEpisodic memory establishes and stores relations among the different elements of an experience, which are often similar and difficult to distinguish. Pattern separation, implemented by the dentate gyrus, is a neural mechanism that allows the discrimination of similar experiences by orthogonalizing synaptic inputs. Granule cells support such disambiguation by sparse rate coding, a process tightly controlled by highly diversified GABAergic neuronal populations, such as somatostatin-expressing cells which directly target the dendritic arbor of granule cells, massively innervated by entorhinal inputs reaching the molecular layer and conveying contextual information. Here, we tested the hypothesis that somatostatin neurons regulate the excitability of the dentate gyrus, thus controlling the efficacy of pattern separation during memory encoding in mice. Indeed, optogenetic suppression of dentate gyrus somatostatin neurons increased spiking activity in putative excitatory neurons and triggered dentate spikes. Moreover, optical inhibition of somatostatin neurons impaired both contextual and spatial discrimination of overlapping episodic-like memories during task acquisition. Importantly, effects were specific for similar environments, suggesting that pattern separation was selectively engaged when overlapping conditions ought to be distinguished. Overall, our results suggest that somatostatin cells regulate excitability in the dentate gyrus and are required for effective pattern separation during episodic memory encoding.Significance statementMemory systems must be able to discriminate stored representations of similar experiences in order to efficiently guide future decisions. This is solved by pattern separation, implemented in the dentate gyrus by granule cells to support episodic memory formation. The tonic inhibitory bombardment produced by multiple GABAergic cell populations maintains low activity levels in granule cells, permitting the process of pattern separation. Somatostatin-expressing cells are one of those interneuron populations, selectively targeting the distal dendrites of granule cells, where cortical multimodal information reaches the dentate gyrus. Hence, somatostatin cells constitute an ideal candidate to regulate pattern separation. Here, by using optogenetic stimulation in mice, we demonstrate that somatostatin cells are required for the acquisition of both contextual and spatial overlapping memories.

scholarly journals Dentate Gyrus Somatostatin Cells are Required for Contextual Discrimination During Episodic Memory Encoding

2020 ◽  
Author(s):  
Cristian Morales ◽  
Juan Facundo Morici ◽  
Nelson Espinosa ◽  
Agostina Sacson ◽  
Ariel Lara-Vasquez ◽  
...  
Keyword(s):  
Episodic Memory ◽  
Dentate Gyrus ◽  
Granule Cells ◽  
Memory Systems ◽  
Pattern Separation ◽  
Activity Levels ◽  
Connection Probability ◽  
Somatostatin Cells ◽  
Formation Pattern

Abstract Memory systems ought to store and discriminate representations of similar experiences in order to efficiently guide future decisions. This problem is solved by pattern separation, implemented in the dentate gyrus (DG) by granule cells to support episodic memory formation. Pattern separation is enabled by tonic inhibitory bombardment generated by multiple GABAergic cell populations that strictly maintain low activity levels in granule cells. Somatostatin-expressing cells are one of those interneuron populations, selectively targeting the distal dendrites of granule cells, where cortical multimodal information reaches the DG. Nonetheless, somatostatin cells have very low connection probability and synaptic efficacy with both granule cells and other interneuron types. Hence, the role of somatostatin cells in DG circuitry, particularly in the context of pattern separation, remains uncertain. Here, by using optogenetic stimulation and behavioral tasks in mice, we demonstrate that somatostatin cells are required for the acquisition of both contextual and spatial overlapping memories.

scholarly journals Impaired pattern separation in Tg2576 mice is associated with hyperexcitable dentate gyrus caused by Kv4.1 downregulation

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Kyung-Ran Kim ◽  
Yoonsub Kim ◽  
Hyeon-Ju Jeong ◽  
Jong-Sun Kang ◽  
Sang Hun Lee ◽  
...  
Keyword(s):  
Dentate Gyrus ◽  
Granule Cells ◽  
Neurodegenerative Disorder ◽  
Critical Role ◽  
Memory Loss ◽  
Pattern Separation ◽  
Antioxidant Treatment ◽  
Action Potential Firing ◽  
Functional Changes ◽  
Tg2576 Mice

AbstractAlzheimer’s disease (AD) is a progressive neurodegenerative disorder that causes memory loss. Most AD researches have focused on neurodegeneration mechanisms. Considering that neurodegenerative changes are not reversible, understanding early functional changes before neurodegeneration is critical to develop new strategies for early detection and treatment of AD. We found that Tg2576 mice exhibited impaired pattern separation at the early preclinical stage. Based on previous studies suggesting a critical role of dentate gyrus (DG) in pattern separation, we investigated functional changes in DG of Tg2576 mice. We found that granule cells in DG (DG-GCs) in Tg2576 mice showed increased action potential firing in response to long depolarizations and reduced 4-AP sensitive K+-currents compared to DG-GCs in wild-type (WT) mice. Among Kv4 family channels, Kv4.1 mRNA expression in DG was significantly lower in Tg2576 mice. We confirmed that Kv4.1 protein expression was reduced in Tg2576, and this reduction was restored by antioxidant treatment. Hyperexcitable DG and impaired pattern separation in Tg2576 mice were also recovered by antioxidant treatment. These results highlight the hyperexcitability of DG-GCs as a pathophysiologic mechanism underlying early cognitive deficits in AD and Kv4.1 as a new target for AD pathogenesis in relation to increased oxidative stress.

scholarly journals A neural network model of hippocampal contributions to category learning

2022 ◽  
Author(s):  
Jelena Sucevic ◽  
Anna C. Schapiro
Keyword(s):  
Neural Network ◽  
Dentate Gyrus ◽  
Entorhinal Cortex ◽  
Network Model ◽  
Neural Network Model ◽  
Category Learning ◽  
Critical Role ◽  
Pattern Separation ◽  
Rapid Binding ◽  
Separation Functions

In addition to its critical role in encoding individual episodes, the hippocampus is capable of extracting regularities across experiences. This ability is central to category learning, and a growing literature indicates that the hippocampus indeed makes important contributions to this kind of learning. Using a neural network model that mirrors the anatomy of the hippocampus, we investigated the mechanisms by which the hippocampus may support novel category learning. We simulated three category learning paradigms and evaluated the network's ability to categorize and to recognize specific exemplars in each. We found that the trisynaptic pathway within the hippocampus-connecting entorhinal cortex to dentate gyrus, CA3, and CA1-was critical for remembering individual exemplars, reflecting the rapid binding and pattern separation functions of this circuit. The monosynaptic pathway from entorhinal cortex to CA1, in contrast, was responsible for detecting the regularities that define category structure, made possible by the use of distributed representations and a slower learning rate. Together, the simulations provide an account of how the hippocampus and its constituent pathways support novel category learning.

Sign in / Sign up

Export Citation Format

Share Document