Neuronal Circuit Model Research Articles

Understanding of nicotinic acetylcholine receptors

Understanding of nicotinic acetylcholine receptors

A mixed analog–digital vision sensor for detecting objects approaching on a collision course

Bio-inspired vision system is a particularly good candidate for navigation of mobile robots and vehicles because of its computational advantages, e.g., low power dissipation and compact hardware. Previously, we had designed a mixed analog–digital integrated vision system for collision detection inspired by a locust neuronal circuit model. The response of the system was, however, susceptible to the luminance of approaching objects and the vibratory self-motion of a car when it was installed on a miniature mobile car. In the present study, we developed a new collision detection algorithm to overcome these problems based on robust image-motion detection and applied the algorithm to control a miniature mobile car.

C. elegans の化学走性神経回路モデルによるストレス応答シミュレーション

C. elegans の化学走性神経回路モデルによるストレス応答シミュレーション

Ghosts in the Machine

It is a generally accepted notion that there is little room for plasticity of functions in a connectivity system based on a “wiring” that is rigidly predetermined by some blueprint. Conversely, a certain degree of plasticity is intuitively acceptable in a system in which there are inbuilt dynamic possibilities for alternative routes.–Janos Szentagothai, 1978

A model of motor control of the nematode C. elegans with neuronal circuits

Living organisms have mechanisms to adapt to various conditions of external environments. If we can realize these mechanisms on the computer, it may be possible to apply methods of biological and biomimetic adaptation to the engineering of artificial machines. This paper focuses on the nematode Caenorhabditis elegans (C. elegans), which has a relatively simple structure and is one of the most studied multicellular organisms. We aim to develop its computer model, artificial C. elegans, to analyze control mechanisms with respect to motion. Although C. elegans processes many kinds of external stimuli, we focused on gentle touch stimulation. The proposed model consists of a neuronal circuit model for motor control that responds to gentle touch stimuli and a kinematic model of the body for movement. All parameters included in the neuronal circuit model are adjusted by using the real-coded genetic algorithm. Also, the neuronal oscillator model is employed in the body model to generate the sinusoidal movement. The motion velocity of the body model is controlled by the neuronal circuit model so as to correspond to the touch stimuli that are received in sensory neurons. The computer simulations confirmed that the proposed model is capable of realizing motor control similar to that of the actual organism qualitatively. By using the artificial organism it may be possible to clarify or predict some characteristics that cannot be measured in actual experiments. With the recent development of computer technology, such a computational analysis becomes a real possibility. The artificial C. elegans will contribute for studies in experimental biology in future, although it is still developing at present.

The influences of somatic and dendritic inhibition on bursting patterns in a neuronal circuit model

The balance between inhibition and excitation plays a crucial role in the generation of synchronous bursting activity in neuronal circuits. In human and animal models of epilepsy, changes in both excitatory and inhibitory synaptic inputs are known to occur. Locations and distribution of these excitatory and inhibitory synaptic inputs on pyramidal cells play a role in the integrative properties of neuronal activity, e.g., epileptiform activity. Thus the location and distribution of the inputs onto pyramidal cells are important parameters that influence neuronal activity in epilepsy. However, the location and distribution of inhibitory synapses converging onto pyramidal cells have not been fully studied. The objectives of this study are to investigate the roles of the relative location of inhibitory synapses on the dendritic tree and soma in the generation of bursting activity. We investigate influences of somatic and dendritic inhibition on bursting activity patterns in several paradigms of potential connections using a simplified multicompartmental model. We also investigate the effects of distribution of fast and slow components of GABAergic inhibition in pyramidal cells. Interspike interval (ISI) analysis is used for examination of bursting patterns. Simulations show that the inhibitory interneuron regulates neuronal bursting activity. Bursting behavior patterns depend on the synaptic weight and delay of the inhibitory connection as well as the location of the synapse. When the inhibitory interneuron synapses on the pyramidal neuron, inhibitory action is stronger if the inhibitory synapse is close to the soma. Alterations of synaptic weight of the interneuron can be compensatory for changes in the location of synaptic input. The relative changes in these parameters exert a considerable influence on whether synchronous bursting activity is facilitated or reduced. Additional simulations show that the slow GABAergic inhibitory component is more effective than the fast component in distal dendrites. Taken together, these findings illustrate the potential for GABAergic inhibition in the soma and dendritic tree to play an important modulatory role in bursting activity patterns.

A transgenic model of comorbid Tourette's syndrome and obsessive-compulsive disorder circuitry

The tic disorder Tourette's Syndrome (TS) and obsessive-compulsive disorder (OCD) are comorbid behavioral disorders, suggesting a shared but still unknown neuronal basis. To 'circuit-test' such behaviors, we previously engineered transgenic mice expressing a neuropotentiating protein (cholera toxin A1 subunit) within a cortical-limbic subset of dopamine D1-receptor expressing (D1+) neurons known to trigger glutamatergic excitation of orbitofrontal, sensorimotor, limbic and efferent striatal circuits thought to be hyperactive or affected in OCD and TS. These mice exhibited OCD-like behaviors including generalized behavioral perseveration and compulsion-like leaping and grooming-associated pulling and biting of skin and hair. We now report that these OCD-like mice, like humans, also exhibit comorbid TS-like behaviors, including juvenile-onset tics; increased tic number, complexity and flurries; increased tic severity in males; voluntary tic suppression; and tic responsiveness to a non-cataleptic TS+OCD drug therapy (clonidine, 0.01 mg kg(-1)). These data suggest that hormonal gender differences, apart from the influence of genetic or autoimmune etiologic factors, may be sufficient to aggravate tic severity in human TS males compared to TS females. These data also proffer a precise neuronal basis for TS+OCD, wherein tics and primary compulsions or obsessions are evoked by hyperactivity of various cortical-limbic projection neurons' glutamatergic output to efferent targets like the striatum. The 'Cortical-limbic Glutamatergic Neuron' (CGN) neuronal circuit model merges formerly opposed neurotransmitter models of TS and OCD, and is consistent with new clinical reports of increased cortical hyperactivity, striatal glutamate and striatal inhibitory D2 receptors, and reduced striatal responsiveness, in these disorders.

Synaptic integration of NMDA and non-NMDA receptors in large neuronal network models solved by means of differential equations.

Alpha functions are commonly used to describe different receptor channel kinetics (non-NMDA, GABAA and GABAB). In this paper we show that they may be represented as solutions to simple ordinary differential equations. This alternative method is compared with the commonly used direct summation of the alpha function conductances in a high-level neuronal circuit model. A parametric study shows that the differential equation method greatly speeds up the previous summation method. The forward Euler method used to solve this differential equation is shown to be accurate for this type of simulation. The modelling of NMDA receptor channel kinetics is also discussed.