| | Models | Description |
| 1. |
CA1 network model for place cell dynamics (Turi et al 2019)
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Biophysical model of CA1 hippocampal region. The model simulates place cells/fields and explores the place cell dynamics as function of VIP+ interneurons. |
| 2. |
CA1 network model: interneuron contributions to epileptic deficits (Shuman et al 2020)
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Temporal lobe epilepsy causes significant cognitive deficits in both humans and rodents, yet the specific circuit mechanisms underlying these deficits remain unknown. There are profound and selective interneuron death and axonal reorganization within the hippocampus of both humans and animal models of temporal lobe epilepsy.
To assess the specific contribution of these mechanisms on spatial coding, we developed a biophysically constrained network model of the CA1 region that consists of different subtypes of interneurons. More specifically, our network consists of 150 cells, 130 excitatory pyramidal cells and 20 interneurons (Fig. 1A). To simulate place cell formation in the network model, we generated grid cell and place cell inputs from the Entorhinal Cortex (ECLIII) and CA3 regions, respectively, activated in a realistic manner as observed when an animal transverses a linear track. Realistic place fields emerged in a subpopulation of pyramidal cells (40-50%), in which similar EC and CA3 grid cell inputs converged onto distal/proximal apical and basal dendrites. The tuning properties of these cells are very similar to the ones observed experimentally in awake, behaving animals
To examine the role of interneuron death and axonal reorganization in the formation and/or tuning properties of place fields we selectively varied the contribution of each interneuron type and desynchronized the two excitatory inputs. We found that desynchronized inputs were critical in reproducing the experimental data, namely the profound reduction in place cell numbers, stability and information content. These results demonstrate that the desynchronized firing of hippocampal neuronal populations contributes to poor spatial processing in epileptic mice, during behavior. Given the lack of experimental data on the selective contributions of interneuron death and axonal reorganization in spatial memory, our model findings predict the mechanistic effects of these alterations at the cellular and network levels. |
| 3. |
Computational analysis of NN activity and spatial reach of sharp wave-ripples (Canakci et al 2017)
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Network oscillations of different frequencies, durations and amplitudes are hypothesized to coordinate information processing and transfer across brain areas. Among these oscillations, hippocampal sharp wave-ripple complexes (SPW-Rs) are one of the most prominent. SPW-Rs occurring in the hippocampus are suggested to play essential roles in memory consolidation as well as information transfer to the neocortex. To-date, most of the knowledge about SPW-Rs comes from experimental studies averaging responses from neuronal populations monitored by conventional microelectrodes. In this work, we investigate spatiotemporal characteristics of SPW-Rs and how microelectrode size and distance influence SPW-R recordings using a biophysical model of hippocampus. We also explore contributions from neuronal spikes and synaptic potentials to SPW-Rs based on two different types of network activity. Our study suggests that neuronal spikes from pyramidal cells contribute significantly to ripples while high amplitude sharp waves mainly arise from synaptic activity. Our simulations on spatial reach of SPW-Rs show that the amplitudes of sharp waves and ripples exhibit a steep decrease with distance from the network and this effect is more prominent for smaller area electrodes. Furthermore, the amplitude of the signal decreases strongly with increasing electrode surface area as a result of averaging. The relative decrease is more pronounced when the recording electrode is closer to the source of the activity. Through simulations of field potentials across a high-density microelectrode array, we demonstrate the importance of finding the ideal spatial resolution for capturing SPW-Rs with great sensitivity. Our work provides insights on contributions from spikes and synaptic potentials to SPW-Rs and describes the effect of measurement configuration on LFPs to guide experimental studies towards improved SPW-R recordings. |
| 4. |
Effects of increasing CREB on storage and recall processes in a CA1 network (Bianchi et al. 2014)
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Several recent results suggest that boosting the CREB pathway improves hippocampal-dependent memory in healthy rodents and restores this type of
memory in an AD mouse model. However, not much is known about how CREB-dependent neuronal alterations in synaptic strength, excitability and
LTP can boost memory formation in the complex architecture of a neuronal network. Using a model of a CA1 microcircuit, we investigate whether
hippocampal CA1 pyramidal neuron properties altered by increasing CREB activity may contribute to improve memory storage and recall. With a set of patterns presented to a network, we find that the pattern recall quality under AD-like conditions is significantly better when boosting CREB function with respect to control. The results are robust and consistent upon increasing the synaptic damage expected by AD progression, supporting the idea that the use of CREB-based therapies could provide a new approach
to treat AD. |
| 5. |
Encoding and retrieval in a model of the hippocampal CA1 microcircuit (Cutsuridis et al. 2009)
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This NEURON code implements a small network model (100 pyramidal cells
and 4 types of inhibitory interneuron) of storage and recall of patterns
in the CA1 region of the mammalian hippocampus. Patterns of PC activity
are stored either by a predefined weight matrix generated by Hebbian learning,
or by STDP at CA3 Schaffer collateral AMPA synapses. |
| 6. |
High frequency oscillations in a hippocampal computational model (Stacey et al. 2009)
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"... Using a physiological computer model of hippocampus, we investigate random synaptic activity
(noise) as a potential initiator of HFOs (high-frequency oscillations).
We explore parameters necessary to produce these oscillations and quantify the response
using the tools of stochastic resonance (SR) and coherence resonance
(CR).
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Our results show that, under normal coupling conditions, synaptic noise was able to produce
gamma (30–100 Hz) frequency oscillations.
Synaptic noise generated HFOs in the ripple range (100–200 Hz) when the network had
parameters similar to pathological findings in epilepsy: increased gap
junctions or recurrent synaptic connections, loss of inhibitory interneurons
such as basket cells, and increased synaptic noise.
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We propose that increased synaptic noise and physiological coupling mechanisms are sufficient to generate gamma
oscillations and that pathologic changes in noise and coupling similar
to those in epilepsy can produce abnormal ripples."
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| 7. |
Hippocampal basket cell gap junction network dynamics (Saraga et al. 2006)
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2 cell network of hippocampal basket cells connected by gap junctions. Paper explores how distal gap junctions and active dendrites can tune network dynamics. |
| 8. |
Hippocampal CA1 NN with spontaneous theta, gamma: full scale & network clamp (Bezaire et al 2016)
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This model is a full-scale, biologically constrained rodent hippocampal CA1 network model that includes 9 cells types (pyramidal cells and 8 interneurons) with realistic proportions of each and realistic connectivity between the cells. In addition, the model receives realistic numbers of afferents from artificial cells representing hippocampal CA3 and entorhinal cortical layer III. The model is fully scaleable and parallelized so that it can be run at small scale on a personal computer or large scale on a supercomputer. The model network exhibits spontaneous theta and gamma rhythms without any rhythmic input. The model network can be perturbed in a variety of ways to better study the mechanisms of CA1 network dynamics. Also see online code at http://bitbucket.org/mbezaire/ca1 and further information at http://mariannebezaire.com/models/ca1 |
| 9. |
Long time windows from theta modulated inhib. in entorhinal–hippo. loop (Cutsuridis & Poirazi 2015)
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"A recent experimental study (Mizuseki et al., 2009) has shown that the temporal
delays between population activities in successive entorhinal and hippocampal anatomical stages are
longer (about 70–80 ms) than expected from axon conduction velocities and passive synaptic integration
of feed-forward excitatory inputs. We investigate via computer simulations the mechanisms that give
rise to such long temporal delays in the hippocampus structures.
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The model shows that the experimentally
reported long temporal delays in the DG, CA3 and CA1 hippocampal regions are due to theta
modulated somatic and axonic inhibition..." |
| 10. |
Modulation of septo-hippocampal theta activity by GABAA receptors (Hajos et al. 2004)
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Theta frequency oscillation of the septo-hippocampal system has been considered as a prominent activity associated with cognitive function and affective processes.
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In the present experiments we applied a combination of computational and physiological techniques to explore the functional role of GABAA receptors in theta oscillation.
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In parallel to these experimental observations, a computational model has been constructed by implementing a septal GABA neuron model with a CA1 hippocampal model containing three types of neurons (including oriens and basket interneurons and pyramidal cells; latter modeled by multicompartmental techniques; for detailed model description with network parameters see online addendum: http://geza.kzoo.edu/theta).
This connectivity made the network capable of simulating the responses of the septo-hippocampal circuitry to the modulation of GABAA transmission, and the presently described computational model proved suitable to reveal several aspects of pharmacological modulation of GABAA receptors.
In addition, computational findings indicated different roles of distinctively located GABAA receptors in theta generation. |
| 11. |
Network recruitment to coherent oscillations in a hippocampal model (Stacey et al. 2011)
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"... Here we demonstrate, via a detailed computational model, a mechanism whereby physiological noise and coupling initiate oscillations and then recruit neighboring tissue, in a manner well described by a combination of Stochastic Resonance and Coherence Resonance.
We develop a novel statistical method to quantify recruitment using several measures of network synchrony.
This measurement demonstrates that oscillations spread via preexisting network connections such as interneuronal connections, recurrent synapses, and gap junctions, provided that neighboring cells also receive sufficient inputs in the form of random synaptic noise.
..."
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| 12. |
Normal ripples, abnormal ripples, and fast ripples in a hippocampal model (Fink et al. 2015)
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"...We use a computational model of hippocampus to investigate possible network mechanisms underpinning normal ripples, pathological ripples, and fast ripples. Our results unify several prior findings regarding HFO mechanisms, and also make several new predictions regarding abnormal HFOs. We show that HFOs are generic, emergent phenomena whose characteristics reflect a wide range of connectivity and network input. Although produced by different mechanisms, both normal and abnormal HFOs generate similar ripple frequencies, underscoring that peak frequency is unable to distinguish the two. Abnormal ripples are generic phenomena that arise when input to pyramidal cells overcomes network inhibition, resulting in high-frequency, uncoordinated firing. In addition, fast ripples transiently and sporadically arise from the precise conditions that produce abnormal ripples. Lastly, we show that such abnormal conditions do not require any specific network structure to produce coherent HFOs, as even completely asynchronous activity is capable of producing abnormal ripples and fast ripples in this manner. These results provide a generic, network-based explanation for the link between pathological ripples and fast ripples, and a unifying description for the entire spectrum from normal ripples to pathological fast ripples." |
| 13. |
Parvalbumin-positive basket cells differentiate among hippocampal pyramidal cells (Lee et al. 2014)
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This detailed microcircuit model explores the network level effects of sublayer specific connectivity in the mouse CA1. The differences in strengths and numbers of synapses between PV+ basket cells and either superficial sublayer or deep sublayer pyramidal cells enables a routing of inhibition from superficial to deep pyramidal cells. At the network level of this model, the effects become quite prominent when one compares the effect on firing rates when either the deep or superficial pyramidal cells receive a selective increase in excitation.
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| 14. |
Synaptic gating at axonal branches, and sharp-wave ripples with replay (Vladimirov et al. 2013)
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The computational model of in vivo sharp-wave ripples with place cell replay. Excitatory post-synaptic potentials at dendrites gate antidromic spikes arriving from the axonal collateral, and thus determine when the soma and the main axon fire. The model allows synchronous replay of pyramidal cells during sharp-wave ripple event, and the replay is possible in both forward and reverse directions. |
| 15. |
Theta-gamma phase amplitude coupling in a hippocampal CA1 microcircuit (Ponzi et al. 2023)
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Using a data-driven model of a hippocampal microcircuit, we demonstrate that theta-gamma phase amplitude coupling (PAC) can naturally emerge from a single feedback mechanism involving an inhibitory and excitatory neuron population, which interplay to generate theta frequency periodic bursts of higher frequency gamma.. |
