| | Models | Description |
| 1. |
A model of the femur-tibia control system in stick insects (Stein et al. 2008)
|
|
|
We studied the femur-tibia joint control system of the insect leg, and its switch between resistance reflex in posture control and "active reaction" in walking. The "active reaction" is basically a reversal of the resistance reflex. Both responses are elicited by the same sensory input and the same neuronal network (the femur-tibia network).
The femur-tibia network was modeled by fitting the responses of model neurons to those obtained in animals. Each implemented neuron has a physiological counterpart. The strengths of 16 interneuronal pathways that integrate sensory input were then assigned three different values and varied independently, generating a database of more than 43 million network variants. The uploaded version contains the model that best represented the resistance reflex. Please see the README for more help.
We demonstrate that the combinatorial code of interneuronal pathways determines motor output. A switch between different behaviors such as standing to walking can thus be achieved by altering the strengths of selected sensory integration pathways.
|
| 2. |
A network model of tail withdrawal in Aplysia (White et al 1993)
|
|
|
The contributions of monosynaptic and polysynaptic circuitry to the tail-withdrawal reflex in the marine mollusk Aplysia californica were assessed by the use of physiologically based neural network models. Effects of monosynaptic circuitry were examined by the use of a two-layer network model with four sensory neurons in the input layer and one motor neuron in the output layer. Results of these simulations indicated that the monosynaptic circuit could not account fully for long-duration responses of tail motor neurons elicited by tail stimulation.
A three-layer network model was constructed by interposing a layer of two excitatory interneurons between the input and output layers of the two-layer network model. The three-layer model could account for long-duration responses in motor neurons. Sensory neurons are a known site of plasticity in Aplysia. Synaptic plasticity at more than one locus modified dramatically the input-output relationship of the three-layer network model. This feature gave the model redundancy in its plastic properties and points to the possibility of distributed memory in the circuitry mediating withdrawal reflexes in Aplysia.
Please see paper for more results and details. |
| 3. |
Burst induced synaptic plasticity in Apysia sensorimotor neurons (Phares et al 2003)
|
|
|
The Aplysia sensorimotor synapse is a key site of plasticity for several simple forms of learning. Intracellular stimulation of sensory neurons to fire a burst of action potentials at 10 Hz for 1 sec led to significant
homosynaptic depression of postsynaptic responses. During the burst, the steady-state depressed phase of the postsynaptic response, which was only 20% of the initial EPSP of the burst, still contributed to firing the motor neuron. To explore the functional contribution of transient homosynaptic depression to the response of the motor neuron, computer simulations of the sensorimotor synapse with and without depression were compared. Depression allowed the motor
neuron to produce graded responses over a wide range of presynaptic input strength.
Thus, synaptic depression increased the dynamic range of the sensorimotor synapse and can, in principle, have a profound effect on
information processing. Please see paper for results and details. |
| 4. |
Classic model of the Tritonia Swim CPG (Getting, 1989)
|
|
|
Classic model developed by Petter Getting of the 3-cell core CPG (DSI, C2, and VSI-B) mediating escape swimming in Tritonia diomedea. Cells use a hybrid integrate-and-fire scheme pioneered by Peter Getting. Each model cell is reconstructed from extensive physiological measurements to precisely mimic I-F curves, synaptic waveforms, and functional connectivity. **However, continued physiological measurements show that Getting may have inadvertently incorporated modulatory and or polysynaptic effects -- the properties of this model do *not* match physiological measurements in rested preparations.** This simulation reconstructs the Getting model as reported in: Getting (1989) 'Reconstruction of small neural networks' In Methods in Neural Modeling, 1st ed, p. 171-196. See also, an earlier version of this model reported in Getting (1983). Every attempt has been made to replicate the 1989 model as precisely as possible. |
| 5. |
Computational Model of a Central Pattern Generator (Cataldo et al 2006)
|
|
|
The buccal ganglia of Aplysia contain a central pattern generator (CPG) that mediates rhythmic movements of the foregut during feeding. This CPG is a multifunctional circuit and generates at least two types of buccal motor patterns (BMPs), one that mediates ingestion (iBMP) and another that mediates rejection (rBMP). The present study used a computational approach to examine the ways in which an ensemble of identified cells and synaptic connections function as a CPG. Hodgkin-Huxley-type models were developed that mimicked the biophysical properties of these cells and synaptic connections. The results suggest that the currently identified ensemble of cells is inadequate to produce rhythmic neural activity and that several key elements of the CPG remain to be identified. |
| 6. |
Computational model of the distributed representation of operant reward memory (Costa et al. 2020)
|
|
|
Operant reward learning of feeding behavior in Aplysia increases the frequency and regularity of biting, as well as biases
buccal motor patterns (BMPs) toward ingestion-like BMPs (iBMPs). The engram underlying this memory comprises cells
that are part of a central pattern generating (CPG) circuit and includes increases in the intrinsic excitability of identified
cells B30, B51, B63, and B65, and increases in B63–B30 and B63–B65 electrical synaptic coupling. To examine the ways in
which sites of plasticity (individually and in combination) contribute to memory expression, a model of the CPG was developed.
The model included conductance-based descriptions of cells CBI-2, B4, B8, B20, B30, B31, B34, B40, B51, B52, B63,
B64, and B65, and their synaptic connections. The model generated patterned activity that resembled physiological BMPs,
and implementation of the engram reproduced increases in frequency, regularity, and bias. Combined enhancement of
B30, B63, and B65 excitabilities increased BMP frequency and regularity, but not bias toward iBMPs. Individually, B30 increased
regularity and bias, B51 increased bias, B63 increased frequency, and B65 decreased all three BMP features.
Combined synaptic plasticity contributed primarily to regularity, but also to frequency and bias. B63–B30 coupling contributed
to regularity and bias, and B63–B65 coupling contributed to all BMP features. Each site of plasticity altered multiple
BMP features simultaneously. Moreover, plasticity loci exhibited mutual dependence and synergism. These results indicate
that the memory for operant reward learning emerged from the combinatoric engagement of multiple sites of plasticity. |
| 7. |
Continuous lateral oscillations as a mechanism for taxis in Drosophila larvae (Wystrach et al 2016)
|
|
|
" ...Our analysis of larvae motion reveals a rhythmic, continuous lateral oscillation of the anterior body, encompassing all head-sweeps, small or large, without breaking the oscillatory rhythm. Further, we show that an agent-model that embeds this hypothesis reproduces a surprising number of taxis signatures observed in larvae. Also, by coupling the sensory input to a neural oscillator in continuous time, we show that the mechanism is robust and biologically plausible. ..." |
| 8. |
Escape response latency in the Giant Fiber System of Drosophila melanogastor (Augustin et al 2019)
|
|
|
"The Giant Fiber System (GFS) is a multi-component neuronal pathway mediating rapid escape response in the adult fruit-fly Drosophila melanogaster, usually in the face of a threatening visual stimulus. Two branches of the circuit promote the response by stimulating an escape jump followed by flight initiation. Our recent work demonstrated an age-associated decline in the speed of signal propagation through the circuit, measured as the stimulus-to-muscle depolarization response latency. The decline is likely due to the diminishing number of inter-neuronal gap junctions in the GFS of ageing flies. In this work, we presented a realistic conductance-based, computational model of the GFS that recapitulates our experimental results and identifies some of the critical anatomical and physiological components governing the circuit's response latency. According to our model, anatomical properties of the GFS neurons have a stronger impact on the transmission than neuronal membrane conductance densities. The model provides testable predictions for the effect of experimental interventions on the circuit's performance in young and ageing flies." |
| 9. |
Half-center oscillator database of leech heart interneuron model (Doloc-Mihu & Calabrese 2011)
|
|
|
We have created a database (HCO-db) of instances of a half-center oscillator computational model [Hill et al., 2001] for analyzing how neuronal parameters influence network activity. We systematically explored the parameter space of about 10.4 million simulated HCO instances and corresponding isolated neuron model simulations obtained by varying a set of selected parameters (maximal conductance of intrinsic and synaptic currents) in all combinations using a brute-force approach. We classified these HCO instances by their activity characteristics into identifiable groups. We built an efficient relational database table (HCO-db) with the resulting instances characteristics. |
| 10. |
Interaction of leak and IMI conductance on the STG over broad temperature range (Stadele et al 2015)
|
|
|
The ZIP file contains a Hodgkin-Huxley based circuit model and the simulation environment MadSim used to study the interaction of leak and IMI on the gastric mill network of the crab (Cancer borealis) as represented in (C. Städele, S. Heigele and W. Stein, 2015)
MadSim, the simulation environment used for this study, is freeware and included in the package. |
| 11. |
Leech heart interneuron network model (Hill et al 2001, 2002)
|
|
|
We have created a computational model of the timing network that paces the heartbeat of the medicinal leech, Hirudo medicinalis. In the intact nerve cord, segmental oscillators are mutually entrained to the same cycle period. Although experiments have shown that the segmental oscillators are coupled by inhibitory coordinating interneurons, the underlying mechanisms of intersegmental coordination have not yet been elucidated. To help understand this coordination, we have created a simple computational model with two variants: symmetric and asymmetric. See references for more details. Biologically realistic network models with two, six, and eight cells and a tutorial are available at the links to Calabrese's web site below. |
| 12. |
Neural Interactome: interactive simulation of a neuronal system (Kim et al 2019)
|
|
|
""Connectivity and biophysical processes determine the functionality of
neuronal networks. We, therefore, developed a real-time framework,
called Neural Interactome, to simultaneously visualize and interact
with the structure and dynamics of such networks. Neural Interactome
is a cross-platform framework, which combines graph visualization with
the simulation of neural dynamics, or experimentally recorded multi
neural time series, to allow application of stimuli to neurons to
examine network responses. In addition, Neural Interactome supports
structural changes, such as disconnection of neurons from the network
(ablation feature). Neural dynamics can be explored on a single neuron
level (using a zoom feature), back in time (using a review feature),
and recorded (using presets feature). The development of the Neural
Interactome was guided by generic concepts to be applicable to
neuronal networks with different neural connectivity and dynamics. We
implement the framework using a model of the nervous system of
Caenorhabditis elegans (C. elegans) nematode, a model organism with
resolved connectome and neural dynamics. We show that Neural
Interactome assists in studying neural response patterns associated
with locomotion and other stimuli. In particular, we demonstrate how
stimulation and ablation help in identifying neurons that shape
particular dynamics. We examine scenarios that were experimentally
studied, such as touch response circuit, and explore new scenarios
that did not undergo elaborate experimental studies." |
| 13. |
Regulation of a slow STG rhythm (Nadim et al 1998)
|
|
|
Frequency regulation of a slow rhythm by a fast periodic input. Nadim, F., Manor, Y., Nusbaum, M. P., Marder, E. (1998) J. Neurosci. 18: 5053-5067 |
| 14. |
S cell network (Moss et al 2005)
|
|
|
Excerpts from the abstract:
S cells form a chain of electrically coupled neurons that extends the length of the leech CNS and plays a critical role in sensitization during whole-body shortening. ...
Serotonin ... increasedAP latency across the electrical synapse, suggesting that serotonin reduced coupling between S cells. ...
Serotonin modulated
instantaneous AP frequency when APs were initiated in separate S
cells and in a computational model of S cell activity following
mechanosensory input. Thus, serotonergic modulation of S cell
electrical synapses may contribute to changes in the pattern of
activity in the S cell network. See paper for more. |
| 15. |
Updated Tritonia Swim CPG (Calin-Jagemann et al. 2007)
|
|
|
Model of the 3-cell core CPG (DSI, C2, and VSI-B) mediating escape swimming in Tritonia diomedea. Cells use a hybrid integrate-and-fire scheme pioneered by Peter Getting. Each model cell is reconstructed from extensive physiological measurements to precisely mimic I-F curves, synaptic waveforms, and functional connectivity. |
| 16. |
Vertical System (VS) tangential cells network model (Trousdale et al. 2014)
|
|
|
Network model of the VS tangential cell system, with 10 cells per hemisphere. Each cell is a two compartment model with one compartment for dendrites and one for the axon. The cells are coupled through axonal gap junctions. The code allows to simulate responses of the VS network to a variety of visual stimuli to investigate coding as a function of gap junction strength. |
| 17. |
Vibration-sensitive Honeybee interneurons (Ai et al 2017)
|
|
|
"Female honeybees use the “waggle dance” to communicate the location of nectar sources to their hive mates. Distance information is encoded in the duration of the waggle phase (von Frisch, 1967). During the waggle phase, the dancer produces trains of vibration pulses, which are detected by the follower bees via Johnston's organ located on the antennae. To uncover the neural mechanisms underlying the encoding of distance information in the waggle dance follower, we investigated morphology, physiology, and immunohistochemistry of interneurons arborizing in the primary auditory center of the honeybee (Apis mellifera). We identified major interneuron types, named DL-Int-1, DL-Int-2, and bilateral DL-dSEG-LP, that responded with different spiking patterns to vibration pulses applied to the antennae. Experimental and computational analyses suggest that inhibitory connection plays a role in encoding and processing the duration of vibration pulse trains in the primary auditory center of the honeybee." |
