| | Models | Description |
| 1. |
A detailed and fast model of extracellular recordings (Camunas-Mesa & Qurioga 2013)
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"We present a novel method to generate realistic simulations of extracellular recordings. The simulations were obtained by superimposing the activity of neurons placed randomly in a cube of brain tissue. Detailed models of individual neurons were used to reproduce the extracellular action potentials of close-by neurons. ..." |
| 2. |
A model for focal seizure onset, propagation, evolution, and progression (Liou et al 2020)
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We developed a neural network model that can account for major elements common
to human focal seizures. These include the tonic-clonic transition, slow advance of clinical semiology
and corresponding seizure territory expansion, widespread EEG synchronization, and slowing of
the ictal rhythm as the seizure approaches termination. These were reproduced by incorporating
usage-dependent exhaustion of inhibition in an adaptive neural network that receives global
feedback inhibition in addition to local recurrent projections. Our model proposes mechanisms that
may underline common EEG seizure onset patterns and status epilepticus, and postulates a role for
synaptic plasticity in the emergence of epileptic foci. Complex patterns of seizure activity and bi-
stable seizure end-points arise when stochastic noise is included. With the rapid advancement of
clinical and experimental tools, we believe that this model can provide a roadmap and potentially
an in silico testbed for future explorations of seizure mechanisms and clinical therapies. |
| 3. |
Biophysical model for field potentials of networks of I&F neurons (beim Graben & Serafim 2013)
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"...
Starting from a reduced three-compartment model of a single pyramidal neuron, we derive an observation model for dendritic dipole currents in extracellular space and thereby for the dendritic field potential (DFP) that contributes to the local field potential (LFP) of a neural population.
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Our reduced three-compartment scheme allows to derive networks of leaky integrate-and-fire (LIF) models, which facilitates comparison with existing neural network and observation models.
..." |
| 4. |
Cortical Basal Ganglia Network Model during Closed-loop DBS (Fleming et al 2020)
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We developed a computational model of the cortical basal ganglia network to investigate closed-loop control of deep brain stimulation (DBS) for Parkinson’s disease (PD). The cortical basal ganglia network model incorporates the (i) the extracellular DBS electric field, (ii) antidromic and orthodromic activation of STN afferent fibers, (iii) the LFP detected at non-stimulating contacts on the DBS electrode and (iv) temporal variation of network beta-band activity within the thalamo-cortico-basal ganglia loop. The model facilitates investigation of clinically-viable closed-loop DBS control approaches, modulating either DBS amplitude or frequency, using an LFP derived measure of network beta-activity. |
| 5. |
Deconstruction of cortical evoked potentials generated by subthalamic DBS (Kumaravelu et al 2018)
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"...
High frequency deep brain stimulation (DBS) of the
subthalamic nucleus (STN) suppresses parkinsonian motor symptoms and
modulates cortical activity.
...
Cortical evoked potentials (cEP) generated by STN DBS reflect
the response of cortex to subcortical stimulation, and the goal was to
determine the neural origin of cEP using a two-step approach.
First,
we recorded cEP over ipsilateral primary motor cortex during different
frequencies of STN DBS in awake healthy and unilateral 6-OHDA lesioned
parkinsonian rats.
Second, we used a biophysically-based model of the
thalamocortical network to deconstruct the neural origin of the
cEP. The in vivo cEP included short (R1), intermediate (R2) and
long-latency (R3) responses. Model-based cortical responses to
simulated STN DBS matched remarkably well the in vivo responses.
R1
was generated by antidromic activation of layer 5 pyramidal neurons,
while recurrent activation of layer 5 pyramidal neurons via excitatory
axon collaterals reproduced R2. R3 was generated by polysynaptic
activation of layer 2/3 pyramidal neurons via the
cortico-thalamic-cortical pathway.
Antidromic activation of the
hyperdirect pathway and subsequent intracortical and
cortico-thalamo-cortical synaptic interactions were sufficient to
generate cEP by STN DBS, and orthodromic activation through basal
ganglia-thalamus-cortex pathways was not required. These results
demonstrate the utility of cEP to determine the neural elements
activated by STN DBS that might modulate cortical activity and
contribute to the suppression of parkinsonian symptoms." |
| 6. |
Engaging distinct oscillatory neocortical circuits (Vierling-Claassen et al. 2010)
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"Selective optogenetic drive of fast-spiking (FS) interneurons (INs) leads to enhanced local field potential (LFP) power across the traditional “gamma” frequency band (20–80 Hz; Cardin et al., 2009).
In contrast, drive to regular-spiking (RS) pyramidal cells enhances power at lower frequencies, with a peak at 8 Hz.
The first result is consistent with previous computational studies emphasizing the role of FS and the time constant of GABAA synaptic inhibition in gamma rhythmicity.
However, the same theoretical models do not typically predict low-frequency LFP enhancement with RS drive.
To develop hypotheses as to how the same network can support these contrasting behaviors, we constructed a biophysically principled network model of primary somatosensory neocortex containing FS, RS, and low-threshold spiking (LTS) INs. ..." |
| 7. |
Large scale neocortical model for PGENESIS (Crone et al 2019)
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This is model code for a large scale neocortical model based on Traub et al. (2005), modified to run on PGENESIS on supercomputing resources. "In this paper (Crone et al 2019), we evaluate the computational performance of the GEneral NEural SImulation System (GENESIS) for large scale simulations of neural networks. While many benchmark studies have been performed for large scale simulations with leaky integrate-and-fire neurons or neuronal models with only a few compartments, this work focuses on higher fidelity neuronal models represented by 50–74 compartments per neuron. ..." |
| 8. |
LFP signature of monosynaptic thalamocortical connection (Hagen et al 2017)
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"A resurgence has taken place in recent years in the use of the
extracellularly recorded local field potential (LFP) to investigate
neural network activity. To probe monosynaptic thalamic activation of
cortical postsynaptic target cells, so called spike-trigger-averaged
LFP (stLFP) signatures have been measured. In these experiments, the
cortical LFP is measured by multielectrodes covering several cortical
lamina and averaged on spontaneous spikes of thalamocortical (TC)
cells. Using a well established forward-modeling scheme, we
investigated the biophysical origin of this stLFP signature with
simultaneous synaptic activation of cortical layer-4 neurons,
mimicking the effect of a single afferent spike from a single TC
neuron.
..." |
| 9. |
Modeling local field potentials (Bedard et al. 2004)
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This demo simulates a model of local field potentials (LFP) with
variable resistivity. This model reproduces the low-pass
frequency filtering properties of extracellular potentials. The
model considers inhomogeneous spatial profiles of conductivity
and permittivity, which result from the multiple media (fluids,
membranes, vessels, ...) composing the extracellular space around
neurons. Including non-constant profiles of conductivity enables
the model to display frequency filtering properties, ie slow
events such as EPSPs/IPSPs are less attenuated than fast events
such as action potentials. The demo simulates Fig 6 of the
paper. |
| 10. |
Olfactory Bulb mitral-granule network generates beta oscillations (Osinski & Kay 2016)
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This model of the dendrodendritic mitral-granule synaptic network generates gamma and beta oscillations as a function of the granule cell excitability, which is represented by the granule cell resting membrane potential. |
| 11. |
Simulations of oscillations in piriform cortex (Wilson & Bower 1992)
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"1. A large-scale computer model of the piriform cortex was
constructed on the basis of the known anatomic and physiological
organization of this region.
2. The oscillatory field potential and electroencephalographic
(EEG) activity generated by the model was compared with actual
physiological results. The model was able to produce patterns of
activity similar to those recorded physiologically in response to
both weak and strong electrical shocks to the afferent input. The
model also generated activity patterns similar to EEGs recorded in
behaving animals.
3. ..." |
| 12. |
Subiculum network model with dynamic chloride/potassium homeostasis (Buchin et al 2016)
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This is the code implementing the single neuron and spiking neural network dynamics. The network has the dynamic ion concentrations of extracellular potassium and intracellular chloride. The code contains multiple parameter variations to study various mechanisms of the neural excitability in the context of chloride homeostasis. |
