| || Models ||Description|
Composite spiking network/neural field model of Parkinsons (Kerr et al 2013)
||This code implements a composite model of Parkinson's disease (PD). The
composite model consists of a leaky integrate-and-fire spiking neuronal
network model being driven by output from a neural field model (instead
of the more usual white noise drive). Three different sets of parameters
were used for the field model: one with basal ganglia parameters based
on data from healthy individuals, one based on data from individuals
with PD, and one purely thalamocortical model. The aim of this model is
to explore how the different dynamical patterns in each each of these
field models affects the activity in the network model.
Computer models of corticospinal neurons replicate in vitro dynamics (Neymotin et al. 2017)
||"Corticospinal neurons (SPI), thick-tufted pyramidal neurons in motor
cortex layer 5B that project caudally via the medullary pyramids,
display distinct class-specific electrophysiological properties in
vitro: strong sag with hyperpolarization, lack of adaptation, and a
nearly linear frequency-current (FI) relationship. We used our
electrophysiological data to produce a pair of large archives of SPI
neuron computer models in two model classes: 1. Detailed models with
full reconstruction; 2. Simplified models with 6 compartments. We
used a PRAXIS and an evolutionary multiobjective optimization (EMO) in
sequence to determine ion channel conductances.
Correcting space clamp in dendrites (Schaefer et al. 2003 and 2007)
||In voltage-clamp experiments, incomplete space clamp distorts the recorded currents, rendering accurate analysis impossible. Here, we present
a simple numerical algorithm that corrects such distortions. The method enabled accurate
retrieval of the local densities, kinetics, and density gradients of somatic and dendritic channels. The correction method was applied to two-electrode voltage-clamp recordings of K currents from the apical dendrite of layer 5 neocortical pyramidal
neurons. The generality and robustness of the algorithm make it a useful tool for voltage-clamp analysis of voltage-gated
currents in structures of any morphology that is amenable to the voltage-clamp technique.
Cortical model with reinforcement learning drives realistic virtual arm (Dura-Bernal et al 2015)
||We developed a 3-layer sensorimotor cortical network of consisting of 704 spiking model-neurons, including excitatory, fast-spiking and low-threshold spiking interneurons. Neurons were interconnected with AMPA/NMDA, and GABAA synapses. We trained our model using spike-timing-dependent reinforcement learning to control a virtual musculoskeletal human arm, with realistic anatomical and biomechanical properties, to reach a target. Virtual arm position was used to simultaneously control a robot arm via a network interface.
Cortical network model of posttraumatic epileptogenesis (Bush et al 1999)
||This simulation from Bush, Prince, and Miller 1999 shows the epileptiform response (Fig. 6C) to a brief single stimulation in a 500 cell
network of multicompartment models, some of which have active dendrites. The results which I obtained under Redhat Linux is shown in result.gif.
Original 1997 code from Paul Bush modified slightly by Bill Lytton to make it work with
current version of NEURON (5.7.139). Thanks to Paul Bush and Ken Miller for
making the code available.
Deconstruction of cortical evoked potentials generated by subthalamic DBS (Kumaravelu et al 2018)
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.
we recorded cEP over ipsilateral primary motor cortex during different
frequencies of STN DBS in awake healthy and unilateral 6-OHDA lesioned
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.
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
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."