Excitatory postsynaptic potentials in rat neocortical neurons in vitro. III. Effects of a quinoxalinedione non-NMDA receptor antagonist

1. Intracellular microelectrodes were used to obtain recordings from neurons in layer II/III of rat frontal cortex. A bipolar electrode positioned in layer IV of the neocortex was used to evoke postsynaptic potentials. Graded series of stimulation were employed to selectively activate different classes of postsynaptic responses. The sensitivity of postsynaptic potentials and iontophoretically applied neurotransmitters to the non-N-methyl-D-asparate (NMDA) antagonist 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX) was examined. 2. As reported previously, low-intensity electrical stimulation of cortical layer IV evoked short-latency early excitatory postsynaptic potentials (eEPSPs) in layer II/III neurons. CNQX reversibly antagonized eEPSPs in a dose-dependent manner. Stimulation at intensities just subthreshold for activation of inhibitory postsynaptic potentials (IPSPs) produced long-latency (10 to 40-ms) EPSPs (late EPSPs or 1EPSPs). CNQX was effective in blocking 1EPSPs. 3. With the use of stimulus intensities at or just below threshold for evoking an action potential, complex synaptic potentials consisting of EPSP-IPSP sequences were observed. Both early, Cl(-)-dependent and late, K(+)-dependent IPSPs were reduced by CNQX. This effect was reversible on washing. This disinhibition could lead to enhanced excitability in the presence of CNQX. 4. Iontophoretic application of quisqualate produced a membrane depolarization with superimposed action potentials, whereas NMDA depolarized the membrane potential and evoked bursts of action potentials. At concentrations up to 5 microM, CNQX selectively antagonized quisqualate responses. NMDA responses were reduced by 10 microM CNQX. D-Serine (0.5-2 mM), an agonist at the glycine regulatory site on the NMDA receptor, reversed the CNQX depression of NMDA responses

The Stomatogastric Nervous System as a Model for Studying Sensorimotor Interactions in Real-Time Closed-Loop Conditions

The perception of proprioceptive signals that report the internal state of the body is one of the essential tasks of the nervous system and helps to continuously adapt body movements to changing circumstances. Despite the impact of proprioceptive feedback on motor activity it has rarely been studied in conditions in which motor output and sensory activity interact as they do in behaving animals, i.e., in closed-loop conditions. The interaction of motor and sensory activities, however, can create emergent properties that may govern the functional characteristics of the system. We here demonstrate a method to use a well-characterized model system for central pattern generation, the stomatogastric nervous system, for studying these properties in vitro. We created a real-time computer model of a single-cell muscle tendon organ in the gastric mill of the crab foregut that uses intracellular current injections to control the activity of the biological proprioceptor. The resulting motor output of a gastric mill motor neuron is then recorded intracellularly and fed into a simple muscle model consisting of a series of low-pass filters. The muscle output is used to activate a one-dimensional Hodgkin–Huxley type model of the muscle tendon organ in real-time, allowing closed-loop conditions. Model properties were either hand tuned to achieve the best match with data from semi-intact muscle preparations, or an exhaustive search was performed to determine the best set of parameters. We report the real-time capabilities of our models, its performance and its interaction with the biological motor system

Distribution of Ih Channels and their Function in the Stomatogastric Ganglion

Generation of rhythmic patterns in the absence of descending commands is an essential and powerful trait of many motor networks. Cyclic rhythmic discharges of motoneurons in repeated motor activities like locomotion, mastication and respiration require underlying circuits of neurons, which are called central pattern generators (CPG). This study examined the possible roles of Ih cation channels in the pyloric network of the stomatogastric nervous system, a rhythmically active network of motoneurons that controls movements of the lobster foregut. Of specific interest were the H-current�s involvement in maintaining firing properties, the distribution of Ih channels within the stomatogastric ganglion, and a potential role for Ih in regulation of synaptic strength. I was able to confirm a homeostatic interaction of Ih with A-type potassium channels, where the over-expression of the IA shal gene after RNA injection evoked a compensatory increase of Ih in different motoneuron types. I observed an additional, non-Ih component of the hyperpolarization activated current, which was more likely to occur in shal-RNA and gfp-RNA injected neurons, compared to untreated neurons. Further, I showed that the homeostatic response of Ih increase is unidirectional; overexpression of the Ih protein PIIH did not lead to an increase of IA. In an immunocytochemical study, I found high concentrations of Ih protein localized in the fine neuropil of the stomatogastric ganglion, an area which is rich in synaptic contacts. Finally, I demonstrate a potential role for Ih in regulating synaptic transmission, for which I found evidence in electrophysiological experiments, where the amplitude of inhibitory postsynaptic potentials decreased with increasing activation of Ih

Sensorimotor Integration an a Small Motor Circuit

Rhythmic motor patterns, which underlie behaviors such as mastication, respiration and locomotion, are generated by specialized neural circuits called central pattern generators (CPGs). Although CPGs can generate their rhythmic motor output in the absence of rhythmic input, these motor patterns are modified by rhythmic sensory feedback in vivo. Furthermore, although the importance of sensory feedback in shaping CPG output is well known, most systems lack the experimental access needed to elucidate the mechanisms underlying sensorimotor integration at the cellular and synaptic level. I am therefore examining this issue using the gastric mill CPG, a circuit which generates the rhythmic retraction and protraction motor activity that drives chewing by the teeth in the gastric mill compartment of the crustacean stomach. The gastric mill CPG is well defined and very accessible at the cellular level. Specifically, I am examining the mechanism by which the gastropyloric receptor (GPR), a phasically active proprioceptor, selectively prolongs one phase (retraction) of the gastric mill rhythm in the isolated nervous system when it is activated in a pattern that mimics its in vivo activity. I first demonstrate that GPR regulation of the gastric mill rhythm relies on its presynaptic inhibition of modulatory commissural neuron 1 (MCN1), a projection neuron that activates and drives this rhythm. I also demonstrate that the GPR inhibition of MCN1 regulates the gastric mill rhythm by selectively regulating peptidergic cotransmission by MCN1. Lastly, I demonstrate that a peptide hormone (crustacean cardioactive peptide) that only modestly modifies the gastric mill rhythm, strongly gates the GPR regulation of this rhythm. Mechanistically, it acts not by influencing GPR or MCN1, but by activating the same excitatory current in the CPG neuron LG (lateral gastric) that is activated by MCN1-released peptide. This novel gating mechanism reduces GPR control over the amplitude of this excitatory current in LG. Thus, I have identified specific cellular mechanisms by which (a) phase-specific regulation of an ongoing motor pattern by a sensory input is accomplished, and (b) hormonal modulation gates that sensory input. These events are likely to reflect comparable ones occurring in the larger and less accessible vertebrate CNS

Locomotor Network Dynamics Governed By Feedback Control In Crayfish Posture And Walking

Sensorimotor circuits integrate biomechanical feedback with ongoing motor activity to produce behaviors that adapt to unpredictable environments. Reflexes are critical in modulating motor output by facilitating rapid responses. During posture, resistance reflexes generate negative feedback that opposes perturbations to stabilize a body. During walking, assistance reflexes produce positive feedback that facilitates fast transitions between swing and stance of each step cycle. Until recently, sensorimotor networks have been studied using biomechanical feedback based on external perturbations in the presence or absence of intrinsic motor activity. Experiments in which biomechanical feedback driven by intrinsic motor activity is studied in the absence of perturbation have been limited. Thus, it is unclear whether feedback plays a role in facilitating transitions between behavioral states or mediating different features of network activity independent of perturbation. These properties are important to understand because they can elucidate how a circuit coordinates with other neural networks or contributes to adaptable motor output. Computational simulations and mathematical models have been used extensively to characterize interactions of negative and positive feedback with nonlinear oscillators. For example, neuronal action potentials are generated by positive and negative feedback of ionic currents via a membrane potential. While simulations enable manipulation of system parameters that are inaccessible through biological experiments, mathematical models ascertain mechanisms that help to generate biological hypotheses and can be translated across different systems. Here, a three-tiered approach was employed to determine the role of sensory feedback in a crayfish locomotor circuit involved in posture and walking. In vitro experiments using a brain-machine interface illustrated that unperturbed motor output of the circuit was changed by closing the sensory feedback loop. Then, neuromechanical simulations of the in vitro experiments reproduced a similar range of network activity and showed that the balance of sensory feedback determined how the network behaved. Finally, a reduced mathematical model was designed to generate waveforms that emulated simulation results and demonstrated how sensory feedback can control the output of a sensorimotor circuit. Together, these results showed how the strengths of different approaches can complement each other to facilitate an understanding of the mechanisms that mediate sensorimotor integration