Models | Description | |

1. | A 1000 cell network model for Lateral Amygdala (Kim et al. 2013) | |

1000 Cell Lateral Amygdala model for investigation of plasticity and memory storage during Pavlovian Conditioning. | ||

2. | CA3 Network Model of Epileptic Activity (Sanjay et. al, 2015) | |

This computational study investigates how a CA3 neuronal network consisting of pyramidal cells, basket cells and OLM interneurons becomes epileptic when dendritic inhibition to pyramidal cells is impaired due to the dysfunction of OLM interneurons. After standardizing the baseline activity (theta-modulated gamma oscillations), systematic changes are made in the connectivities between the neurons, as a result of step-wise impairment of dendritic inhibition. | ||

3. | Gamma and theta rythms in biophysical models of hippocampus circuits (Kopell et al. 2011) | |

" ... the main rhythms displayed by the hippocampus, the gamma (30–90 Hz) and theta (4–12 Hz) rhythms. We concentrate on modeling in vitro experiments, but with an eye toward possible in vivo implications. ... We use simpler biophysical models; all cells have a single compartment only, and the interneurons are restricted to two types: fast-spiking (FS) basket cells and oriens lacunosum-moleculare (O-LM) cells. ... , we aim not so much at reproducing dynamics in great detail, but at clarifying the essential mechanisms underlying the production of the rhythms and their interactions (Kopell, 2005). ..." | ||

4. | High frequency oscillations in a hippocampal computational model (Stacey et al. 2009) | |

"... 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). ... 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. ... 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." | ||

5. | Hippocampal CA3 network and circadian regulation (Stanley et al. 2013) | |

This model produces the hippocampal CA3 neural network model used in the paper below. It has two modes of operation, a default mode and a circadian mode. In the circadian mode, parameters are swept through a range of values. This model can be quite easily adapted to produce theta and gamma oscillations, as certain parameter sweeps will reveal (see Figures). BASH scripts interact with GENESIS 2.3 to implement parameter sweeps. The model contains four cell types derived from prior papers. CA3 pyramidal are derived from Traub et al (1991); Basket, stratum oriens (O-LM), and Medial Septal GABAergic (MSG) interneurons are taken from Hajos et al (2004). | ||

6. | Ih tunes oscillations in an In Silico CA3 model (Neymotin et al. 2013) | |

" ... We investigated oscillatory control using a multiscale computer model of hippocampal CA3, where each cell class (pyramidal, basket, and oriens-lacunosum moleculare cells), contained type-appropriate isoforms of Ih. Our model demonstrated that modulation of pyramidal and basket Ih allows tuning theta and gamma oscillation frequency and amplitude. Pyramidal Ih also controlled cross-frequency coupling (CFC) and allowed shifting gamma generation towards particular phases of the theta cycle, effected via Ih’s ability to set pyramidal excitability. ..." | ||

7. | Ketamine disrupts theta modulation of gamma in a computer model of hippocampus (Neymotin et al 2011) | |

"Abnormalities in oscillations have been suggested to play a role in schizophrenia. We studied theta-modulated gamma oscillations in a computer model of hippocampal CA3 in vivo with and without simulated application of ketamine, an NMDA receptor antagonist and psychotomimetic. Networks of 1200 multi-compartment neurons (pyramidal, basket and oriens-lacunosum moleculare, OLM, cells) generated theta and gamma oscillations from intrinsic network dynamics: basket cells primarily generated gamma and amplified theta, while OLM cells strongly contributed to theta. ..." | ||

8. | Long time windows from theta modulated inhib. in entorhinal–hippo. loop (Cutsuridis & Poirazi 2015) | |

"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. ... 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..." | ||

9. | Mean Field Equations for Two-Dimensional Integrate and Fire Models (Nicola and Campbell, 2013) | |

The zip file contains the files used to perform numerical simulation and bifurcation studies of large networks of two-dimensional integrate and fire neurons and of the corresponding mean field models derived in our paper. The neural models used are the Izhikevich model and the Adaptive Exponential model. | ||

10. | Mechanisms of very fast oscillations in axon networks coupled by gap junctions (Munro, Borgers 2010) | |

Axons connected by gap junctions can produce very fast oscillations (VFOs, > 80 Hz) when stimulated randomly at a low rate. The models here explore the mechanisms of VFOs that can be seen in an axonal plexus, (Munro & Borgers, 2009): a large network model of an axonal plexus, small network models of axons connected by gap junctions, and an implementation of the model underlying figure 12 in Traub et al. (1999) . The large network model consists of 3,072 5-compartment axons connected in a random network. The 5-compartment axons are the 5 axonal compartments from the CA3 pyramidal cell model in Traub et al. (1994) with a fixed somatic voltage. The random network has the same parameters as the random network in Traub et al. (1999), and axons are stimulated randomly via a Poisson process with a rate of 2/s/axon. The small network models simulate waves propagating through small networks of axons connected by gap junctions to study how local connectivity affects the refractory period. | ||

11. | Model of long range transmission of gamma oscillation (Murray 2007) | |

"... A minimal mathematical model was developed for a preliminary study of long-range neural transmission of gamma oscillation from the CA3 to the entorhinal cortex via the CAI region of the hippocampus, a subset within a larger complex set of pathways. A module was created for each local population of neurons with common intrinsic properties and connectivity to simplify the connection process and make the model more flexible. Three modules were created using MATLAB Simulink® and tested to confirm that they transmit gamma through the system. The model also revealed that a portion of the signal from CAI to the entorhinal cortex may be lost in transmission under certain conditions." | ||

12. | Network recruitment to coherent oscillations in a hippocampal model (Stacey et al. 2011) | |

"... 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. ..." | ||

13. | Role for short term plasticity and OLM cells in containing spread of excitation (Hummos et al 2014) | |

This hippocampus model was developed by matching experimental data, including neuronal behavior, synaptic current dynamics, network spatial connectivity patterns, and short-term synaptic plasticity. Furthermore, it was constrained to perform pattern completion and separation under the effects of acetylcholine. The model was then used to investigate the role of short-term synaptic depression at the recurrent synapses in CA3, and inhibition by basket cell (BC) interneurons and oriens lacunosum-moleculare (OLM) interneurons in containing the unstable spread of excitatory activity in the network. | ||

14. | Spiking GridPlaceMap model (Pilly & Grossberg, PLoS One, 2013) | |

Development of spiking grid cells and place cells in the entorhinal-hippocampal system to represent positions in large spaces |