The synchronization properties of a network of inhibitory interneurons depend on the biophysical model

Coupled sensory interneurons mediate escape neural circuit processing in an aquatic annelid worm, Lumbriculus variegatus

The interneurons associated with rapid escape circuits are adapted for fast pathway activation and rapid conduction. An essential aspect of fast activation is the processing of sensory information with limited delays. Although aquatic annelid worms have some of the fastest escape responses in nature, the sensory networks that mediate their escape behavior are not well defined. Here, we demonstrate that the escape circuit of the mud worm, Lumbriculus variegatus, is a segmentally arranged network of sensory interneurons electrically coupled to the central medial giant fiber (MGF), the command-like interneuron for head withdrawal. Electrical stimulation of the body wall evoked fast, short-duration spikelets in the MGF, which we suggest are the product of intermediate giant fiber activation coupled to MGF collateral dendrites. Since these contact sites have immunoreactivity with a glutamate receptor antibody, and the glutamate receptor antagonist 6-cyano-7-nitroquinoxaline-2,3-dion abolishes evoked MGF responses, we conclude that the afferent pathway for MGF-mediated escape is glutamatergic. This electrically coupled sensory network may facilitate rapid escape activation by enhancing the amplitude of giant axon depolarization.

Robust Adaptive Synchronization of Ring Configured Uncertain Chaotic FitzHugh-Nagumo Neurons under Direction-Dependent Coupling

This paper exploits the dynamical modeling, behavior analysis, and synchronization of a network of four different FitzHugh–Nagumo (FHN) neurons with unknown parameters linked in a ring configuration under direction-dependent coupling. The main purpose is to investigate a robust adaptive control law for the synchronization of uncertain and perturbed neurons, communicating in a medium of bidirectional coupling. The neurons are assumed to be different and interconnected in a ring structure. The strength of the gap junctions is taken to be different for each link in the network, owing to the inter-neuronal coupling medium properties. Robust adaptive control mechanism based on Lyapunov stability analysis is employed and theoretical criteria are derived to realize the synchronization of the network of four FHN neurons in a ring form with unknown parameters under direction-dependent coupling and disturbances. The proposed scheme for synchronization of dissimilar neurons, under external electrical stimuli, coupled in a ring communication topology, having all parameters unknown, and subject to directional coupling medium and perturbations, is addressed for the first time as per our knowledge. To demonstrate the efficacy of the proposed strategy, simulation results are provided.

Modeling of inter-neuronal coupling medium and its impact on neuronal synchronization

In this paper, modeling of the coupling medium between two neurons, the effects of the model parameters on the synchronization of those neurons, and compensation of coupling strength deficiency in synchronization are studied. Our study exploits the inter-neuronal coupling medium and investigates its intrinsic properties in order to get insight into neuronal-information transmittance and, there from, brain-information processing. A novel electrical model of the coupling medium that represents a well-known RLC circuit attributable to the coupling medium’s intrinsic resistive, inductive, and capacitive properties is derived. Surprisingly, the integration of such properties reveals the existence of a natural three-term control strategy, referred to in the literature as the proportional integral derivative (PID) controller, which can be responsible for synchronization between two neurons. Consequently, brain-information processing can rely on a large number of PID controllers based on the coupling medium properties responsible for the coherent behavior of neurons in a neural network. Herein, the effects of the coupling model (or natural PID controller) parameters are studied and, further, a supervisory mechanism is proposed that follows a learning and adaptation policy based on the particle swarm optimization algorithm for compensation of the coupling strength deficiency.

Cooperation and competition of gamma oscillation mechanisms

Oscillations of neuronal activity in different frequency ranges are thought to reflect important aspects of cortical network dynamics. Here we investigate how various mechanisms that contribute to oscillations in neuronal networks may interact. We focus on networks with inhibitory, excitatory, and electrical synapses, where the subnetwork of inhibitory interneurons alone can generate interneuron gamma (ING) oscillations and the interactions between interneurons and pyramidal cells allow for pyramidal-interneuron gamma (PING) oscillations. What type of oscillation will such a network generate? We find that ING and PING oscillations compete: The mechanism generating the higher oscillation frequency "wins"; it determines the frequency of the network oscillation and suppresses the other mechanism. For type I interneurons, the network oscillation frequency is equal to or slightly above the higher of the ING and PING frequencies in corresponding reduced networks that can generate only either of them; if the interneurons belong to the type II class, it is in between. In contrast to ING and PING, oscillations mediated by gap junctions and oscillations mediated by inhibitory synapses may cooperate or compete, depending on the type (I or II) of interneurons and the strengths of the electrical and chemical synapses. We support our computer simulations by a theoretical model that allows a full theoretical analysis of the main results. Our study suggests experimental approaches to deciding to what extent oscillatory activity in networks of interacting excitatory and inhibitory neurons is dominated by ING or PING oscillations and of which class the participating interneurons are.

Robust synchronization of delayed chaotic FitzHugh-Nagumo neurons under external electrical stimulation

Synchronization of chaotic neurons under external electrical stimulation (EES) is studied in order to understand information processing in the brain and to improve the methodologies employed in the treatment of cognitive diseases. This paper investigates the dynamics of uncertain coupled chaotic delayed FitzHugh-Nagumo (FHN) neurons under EES for incorporated parametric variations. A global nonlinear control law for synchronization of delayed neurons with known parameters is developed. Based on local and global Lipschitz conditions, knowledge of the bounds on the neuronal states, the Lyapunov-Krasovskii functional, and the L(2) gain reduction, a less conservative local robust nonlinear control law is formulated to address the problem of robust asymptotic synchronization of delayed FHN neurons under parametric uncertainties. The proposed local control law guarantees both robust stability and robust performance and provides the L(2) bound for uncertainty rejection in the synchronization error dynamics. Separate conditions for single-input and multiple-input control schemes for synchronization of a wide class of FHN systems are provided. The results of the proposed techniques are verified through numerical simulations.

Hypotheses relating to the function of the claustrum

This paper present a new hypothesis as to the function of the claustrum. Our basic premise is that the claustrum functions as a detector and integrator of synchrony in the axonal trains in its afferent inputs. In the first place an unexpected stimulus sets up a processed signal to the sensory cortex that initiates a focus of synchronized gamma oscillations therein. This focus may then interact with a general alerting signal conveyed from the reticular formation via cholinergic mechanisms, and with other salient activations set up by the stimulus in other sensory pathways that are relayed to the cortex. This activity is relayed from the cortex to the claustrum, which then processes these several inputs by means of multiple competitive intraclaustral synchronized oscillations at different frequencies. Finally it modulates the synchronized outputs that the claustrum distributes to most cortical and many subcortical structures, including the motor cortex. In this way, during multicenter perceptual and cognitive operations, reverberating claustro-cortical loops potentiate weak intracortical synchronizations by means of connected strong intraclaustral synchronizations. These may also occur without a salient stimulus. By this mechanism, the claustrum may play a strong role in the control of interactive processes in different parts of the brain, and in the control of voluntary behavior. These may include the neural correlates of consciousness. We also consider the role of GABAergic mechanisms and deafferentation plasticity.

Pyramidal neurons switch from integrators in vitro to resonators under in vivo-like conditions

During wakefulness, pyramidal neurons in the intact brain are bombarded by synaptic input that causes tonic depolarization, increased membrane conductance (i.e., shunting), and noisy fluctuations in membrane potential; by comparison, pyramidal neurons in acute slices typically experience little background input. Such differences in operating conditions can compromise extrapolation of in vitro data to explain neuronal operation in vivo. For instance, pyramidal neurons have been identified as integrators (i.e., class 1 neurons according to Hodgkin's classification of intrinsic excitability) based on in vitro experiments but that classification is inconsistent with the ability of hippocampal pyramidal neurons to oscillate/resonate at theta frequency since intrinsic oscillatory behavior is limited to class 2 neurons. Using long depolarizing stimuli and dynamic clamp to reproduce in vivo-like conditions in slice experiments, we show that CA1 hippocampal pyramidal cells switch from integrators to resonators, i.e., from class 1 to class 2 excitability. The switch is explained by increased outward current contributed by the M-type potassium current I(M), which shifts the balance of inward and outward currents active at perithreshold potentials and thereby converts the spike-initiating mechanism as predicted by dynamical analysis of our computational model. Perithreshold activation of I(M) is enhanced by the depolarizing shift in spike threshold caused by shunting and/or sodium channel inactivation secondary to tonic depolarization. Our conclusions were validated by multiple comparisons between simulation and experimental data. Thus even so-called "intrinsic" properties may differ qualitatively between in vitro and in vivo conditions.