Marcelo Bussotti Reyes

Neural mechanisms underlying the generation of the lobster gastric mill motor pattern

The lobster gastric mill central pattern generator (CPG) is located in the stomatogastric ganglion and consists of 11 neurons whose circuitry is well known. Because all of the neurons are identifiable and accessible, it can serve as a prime experimental model for analyzing how microcircuits generate multiphase oscillatory spatiotemporal patterns. The neurons that comprise the gastric mill CPG consist of one interneuron, five burster neurons and six tonically firing neurons. The single interneuron (Int 1) is shared by the medial tooth subcircuit (containing the AM, DG and GMs) and the lateral teeth subcircuit (LG, MG and LPGs). By surveying cell-to-cell connections and the cooperative dynamics of the neurons we find that the medial subcircuit is essentially a feed forward system of oscillators. The Int 1 neuron entrains the DG and AM cells by delayed excitation and this pair then periodically inhibits the tonically firing GMs causing them to burst. The lateral subcircuit consists of two negative feedback loops of reciprocal inhibition from Int 1 to the LG/MG pair and from the LG/MG to the LPGs. Following a fast inhibition from Int 1, the LG/MG neurons receive a slowly developing excitatory input similar to that which Int 1 puts onto DG/AM. Thus Int 1 plays a key role in synchronizing both subcircuits. This coordinating role is assisted by additional, weaker connections between the two subsets but those are not sufficient to synchronize them in the absence of Int 1. In addition to the experiments, we developed a conductance-based model of a slightly simplified gastric circuit. The mathematical model can reproduce the fundamental rhythm and many of the experimentally induced perturbations. Our findings shed light on the functional role of every cell and synapse in this small circuit providing a detailed understanding of the rhythm generation and pattern formation in the gastric mill network.

Visual causality judgments correlate with the phase of alpha oscillations

The detection of causality is essential for our understanding of whether distinct events relate. A central requirement for the sensation of causality is temporal contiguity: As the interval between events increases, causality ratings decrease; for intervals longer than approximately 100 msec, the events start to appear independent. It has been suggested that this effect might be due to perception relying on discrete processing. According to this view, two events may be judged as sequential or simultaneous depending on their temporal relationship within a discrete neuronal process. To assess if alpha oscillations underlie this discrete neuronal process, we investigated how these oscillations modulate the judgment of causality. We used the classic launching effect with concurrent recording of EEG signal. In each trial, a disk moved horizontally toward a second disk at the center of the screen and stopped when they touched each other. After a delay that varied between 0 and 400 msec after contact, the right disk began to move. Participants were instructed to judge whether or not they had a feeling that the first disk caused the movement of the second disk. We found that frontocentral alpha phase significantly biased causality estimates. Moreover, we found that alpha phase was concentrated around different angles for trials in which participants judged events as causally related versus not causally related. We conclude that alpha phase plays a key role in biasing causality judgments.

Artificial synaptic modification reveals a dynamical invariant in the pyloric CPG

The sequential firing of neurons in central pattern generators (CPGs) is generally thought to be a result of an interaction between intrinsic cellular and synaptic properties of the component neurons. Due to experimental limitations, it is usually difficult to address the role of each of these properties separately. We have done so by using the crustacean stomatogastric CPG and the dynamic clamp technique to measure how the network responds to the selective modification of an individual important synapse. Our results show that the burst periods and the phase lags between the constrictor (LP) and dilator (PD) neurons across preparations showed significant variability during equivalent experimental manipulations. Despite this variability, the ratio between the change in the burst period and the change in the phase lag between the same neurons was tightly preserved in all preparations, revealing a dynamical invariant in the system. This dynamical invariant was preserved despite the individual variability in the period and phase lag measurements, suggesting a tightly regulated constraint between the parameters of the network.

Communication-Based on Topology Preservation of Chaotic Dynamics

By using the Chua circuit we present experimental results for the feasibility of a chaotic communication scheme in which large parameter variations are allowed. The parameters are varied along special codimension one directions, on which the topology of chaotic attractors remains roughly invariant.

Connection Topology Selection in Central Pattern Generators by Maximizing the Gain of Information

A study of a general central pattern generator (CPG) is carried out by means of a measure of the gain of information between the number of available topology configurations and the output rhythmic activity. The neurons of the CPG are chaotic Hindmarsh-Rose models that cooperate dynamically to generate either chaotic or regular spatiotemporal patterns. These model neurons are implemented by computer simulations and electronic circuits. Out of a random pool of input configurations, a small subset of them maximizes the gain of information. Two important characteristics of this subset are emphasized: (1) the most regular output activities are chosen, and (2) none of the selected input configurations are networks with open topology. These two principles are observed in living CPGs as well as in model CPGs that are the most efficient in controlling mechanical tasks, and they are evidence that the information-theoretical analysis can be an invaluable tool in searching for general properties of CPGs.

A modeling approach on why simple central pattern generators are built of irregular neurons.

The crustacean pyloric Central Pattern Generator (CPG) is a nervous circuit that endogenously provides periodic motor patterns. Even after about 40 years of intensive studies, the rhythm genesis is still not rigorously understood in this CPG, mainly because it is made of neurons with irregular intrinsic activity. Using mathematical models we addressed the question of using a network of irregularly behaving elements to generate periodic oscillations, and we show some advantages of using non-periodic neurons with intrinsic behavior in the transition from bursting to tonic spiking (as found in biological pyloric CPGs) as building components. We studied two- and three-neuron model CPGs built either with Hindmarsh-Rose or with conductance-based Hodgkin-Huxley-like model neurons. By changing a model’s parameter we could span the neuron’s intrinsic dynamical behavior from slow periodic bursting to fast tonic spiking, passing through a transition where irregular bursting was observed. Two-neuron CPG, half center oscillator (HCO), was obtained for each intrinsic behavior of the neurons by coupling them with mutual symmetric synaptic inhibition. Most of these HCOs presented regular antiphasic bursting activity and the changes of the bursting frequencies was studied as a function of the inhibitory synaptic strength. Among all HCOs, those made of intrinsic irregular neurons presented a wider burst frequency range while keeping a reliable regular oscillatory (bursting) behavior. HCOs of periodic neurons tended to be either hard to change their behavior with synaptic strength variations (slow periodic burster neurons) or unable to perform a physiologically meaningful rhythm (fast tonic spiking neurons). Moreover, 3-neuron CPGs with connectivity and output similar to those of the pyloric CPG presented the same results.

ONSET OF PHASE SYNCHRONIZATION IN NEURONS WITH CHEMICAL SYNAPSE

We study the onset of synchronous states in realistic chaotic neurons coupled by mutually inhibitory chemical synapses. For the realistic parameters, namely the synaptic strength and the intrinsic current, this synapse introduces noncoherences in the neuronal dynamics, yet allowing for chaotic phase synchronization in a large range of parameters. As we increase the synaptic strength, the neurons reach a periodic state, and no chaotic complete synchronization is found.

Explosion of chaotic bubbling

We have studied a saddle-node bifurcation/explosion of air bubble formation driven by a sound wave, whose amplitude is the control parameter. The bubbles are formed in a nozzle submerged in a water/glycerol solution inside a cylindrical tube, and the sound wave is tuned to the air column above the fluid. The nonlinear interaction between sound wave and the fluid oscillations, caused by the air bubbles passage through the liquid, results in a route to chaos via quasi-periodicity, with some resonant states characterized by the rational winding numbers W=fs/fb, where fs is the sound wave frequency and fb is the bubbling rate. We also have shown that the bubble dynamics is similar to the one observed in the two-dimensional circle map.

Dynamic representation of time in brain states

The ability to process time on the scale of milliseconds and seconds is essential for behaviour. A growing number of studies have started to focus on brain dynamics as a mechanism for temporal encoding. Although there is growing evidence in favour of this view from computational and in vitro studies, there is still a lack of results from experiments in humans. We show that high-dimensional brain states revealed by multivariate pattern analysis of human EEG are correlated to temporal judgements. First, we show that, as participants estimate temporal intervals, the spatiotemporal dynamics of their brain activity are consistent across trials. Second, we present evidence that these dynamics exhibit properties of temporal perception, such as scale invariance. Lastly, we show that it is possible to predict temporal judgements based on brain states. These results show how scalp recordings can reveal the spatiotemporal dynamics of human brain activity related to temporal processing.

Low-frequency cortical oscillations are modulated by temporal prediction and temporal error coding.

Abstract Monitoring and updating temporal predictions are critical abilities for adaptive behavior. Here, we investigated whether neural oscillations are related to violation and updating of temporal predictions. Human participants performed an experiment in which they had to generate a target at an expected time point, by pressing a button while taking into account a variable delay between the act and the stimulus occurrence. Our behavioral results showed that participants quickly adapted their temporal predictions in face of an error. Concurrent electrophysiological (EEG) data showed that temporal errors elicited markers that are classically related to error coding. Furthermore, intertrial phase coherence of frontal theta oscillations was modulated by error magnitude, possibly indexing the degree of surprise. Finally, we found that delta phase at stimulus onset was correlated with future behavioral adjustments. Together, our findings suggest that low frequency oscillations play a key role in monitoring and in updating temporal predictions. HighlightsTemporal predictions are quickly adjusted in face of an error.Temporal errors elicit an increase in frontal intertrial theta phase coherence.Delta phase at stimulus onset is correlated with future behavioral adjustments.