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(Regular spiking is an activity where a single action potential occurs at a regular (roughly constant) time interval.)
| Models | Description |
| A single column thalamocortical network model (Traub et al 2005) | |
| To better understand population phenomena in thalamocortical neuronal ensembles, we have constructed a preliminary network model with 3,560 multicompartment neurons (containing soma, branching dendrites, and a portion of axon). Types of neurons included superficial pyramids (with regular spiking [RS] and fast rhythmic bursting [FRB] firing behaviors); RS spiny stellates; fast spiking (FS) interneurons, with basket-type and axoaxonic types of connectivity, and located in superficial and deep cortical layers; low threshold spiking (LTS) interneurons, that contacted principal cell dendrites; deep pyramids, that could have RS or intrinsic bursting (IB) firing behaviors, and endowed either with non-tufted apical dendrites or with long tufted apical dendrites; thalamocortical relay (TCR) cells; and nucleus reticularis (nRT) cells. To the extent possible, both electrophysiology and synaptic connectivity were based on published data, although many arbitrary choices were necessary. | |
| Altered complexity in layer 2/3 pyramidal neurons (Luuk van der Velden et al. 2012) | |
| " ... Our experimental results show that hypercomplexity of the apical dendritic tuft of layer 2/3 pyramidal neurons affects neuronal excitability by reducing the amount of spike frequency adaptation. This difference in firing pattern, related to a higher dendritic complexity, was accompanied by an altered development of the afterhyperpolarization slope with successive action potentials. Our abstract and realistic neuronal models, which allowed manipulation of the dendritic complexity, showed similar effects on neuronal excitability and confirmed the impact of apical dendritic complexity. Alterations of dendritic complexity, as observed in several pathological conditions such as neurodegenerative diseases or neurodevelopmental disorders, may thus not only affect the input to layer 2/3 pyramidal neurons but also shape their firing pattern and consequently alter the information processing in the cortex." | |
| Computational Surgery (Lytton et al. 2011) | |
| Figure 2 in Neocortical simulation for epilepsy surgery guidance: Localization and intervention, by William W. Lytton, Samuel A. Neymotin, Jason C. Wester, and Diego Contreras in Computational Surgery and Dual Training, Springer, 2011 | |
| Engaging distinct oscillatory neocortical circuits (Vierling-Claassen et al. 2010) | |
| "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. ..." | |
| Extraction and classification of three cortical neuron types (Mensi et al. 2011) | |
| This script proposes a new convex fitting procedure that allows the parameters estimation of a large class of stochastic Integrate-and-Fire model upgraded with spike-triggered current and moving threshold from patch-clamp experiments (i.e. given the injected current and the recorded membrane potential). This script applies the method described in the paper to estimate the parameters of a reference model from a single voltage trace and the corresponding input current and evaluate the performance of the fitted model on a separated test set. | |
| Hodgkin-Huxley models of different classes of cortical neurons (Pospischil et al. 2008) | |
| "We review here the development of Hodgkin– Huxley (HH) type models of cerebral cortex and thalamic neurons for network simulations. The intrinsic electrophysiological properties of cortical neurons were analyzed from several preparations, and we selected the four most prominent electrophysiological classes of neurons. These four classes are “fast spiking”, “regular spiking”, “intrinsically bursting” and “low-threshold spike” cells. For each class, we fit “minimal” HH type models to experimental data. ..." | |
| Parameter estimation for Hodgkin-Huxley based models of cortical neurons (Lepora et al. 2011) | |
| Simulation and fitting of two-compartment (active soma, passive dendrite) for different classes of cortical neurons. The fitting technique indirectly matches neuronal currents derived from somatic membrane potential data rather than fitting the voltage traces directly. The method uses an analytic solution for the somatic ion channel maximal conductances given approximate models of the channel kinetics, membrane dynamics and dendrite. This approach is tested on model-derived data for various cortical neurons. | |
| Parametric computation and persistent gamma in a cortical model (Chambers et al. 2012) | |
| Using the Traub et al (2005) model of the cortex we determined how 33 synaptic strength parameters control gamma oscillations. We used fractional factorial design to reduce the number of runs required to 4096. We found an expected multiplicative interaction between parameters. | |
| Pyramidal Neuron: Deep, Thalamic Relay and Reticular, Interneuron (Destexhe et al 1998, 2001) | |
| This package shows single-compartment models of different classes of cortical neurons, such as the "regular-spiking", "fast-spiking" and "bursting" (LTS) neurons. The mechanisms included are the Na+ and K+ currents for generating action potentials (INa, IKd), the T-type calcium current (ICaT), and a slow voltage-dependent K+ current (IM). See http://cns.fmed.ulaval.ca/alain_demos.html | |
| Rate model of a cortical RS-FS-LTS network (Hayut et al. 2011) | |
| A rate model of cortical networks composed of RS, FS and LTS neurons. Synaptic depression is modelled according to the Tsodyks-Markram scheme. | |
| Reinforcement learning of targeted movement (Chadderdon et al. 2012) | |
| Synaptic information transfer in computer models of neocortical columns (Neymotin et al. 2010) | |
| "... We sought to measure how the activity of the network alters information flow from inputs to output patterns. Information handling by the network reflected the degree of internal connectivity. ... With greater connectivity strength, the recurrent network translated activity and information due to contribution of activity from intrinsic network dynamics. ... At still higher internal synaptic strength, the network corrupted the external information, producing a state where little external information came through. The association of increased information retrieved from the network with increased gamma power supports the notion of gamma oscillations playing a role in information processing." | |
| Synaptic scaling balances learning in a spiking model of neocortex (Rowan & Neymotin 2013) | |
| Learning in the brain requires complementary mechanisms: potentiation and activity-dependent homeostatic scaling. We introduce synaptic scaling to a biologically-realistic spiking model of neocortex which can learn changes in oscillatory rhythms using STDP, and show that scaling is necessary to balance both positive and negative changes in input from potentiation and atrophy. We discuss some of the issues that arise when considering synaptic scaling in such a model, and show that scaling regulates activity whilst allowing learning to remain unaltered. | |
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