Pacemaker and network mechanisms of rhythm generation: Cooperation and competition

Global vs. Network-Specific Regulations as the Source of Intrinsic Coactivations in Resting-State Networks

Spontaneous neural activities are endowed with specific patterning characterized by synchronizations within functionally relevant distant regions that are termed as resting-state networks (RSNs). Although the mechanisms that organize the large-scale neural systems are still largely unknown, recent studies have proposed a hypothesis that network-specific coactivations indeed emerge as the result of globally propagating neural activities with specific paths of transmission. However, the extent to which such a centralized global regulation, rather than network-specific control, contributes to the RSN synchronization remains unknown. In the present study, we investigated the contribution from each mechanism by directly identifying the global as well as local component of resting-state functional MRI (fMRI) data provided by human connectome project, using temporal independent component analysis (ICA). Based on the spatial distribution pattern, each ICA component was classified as global or local. Time lag mapping of each IC revealed several paths of global or semi-global propagations that are partially overlapping yet spatially distinct to each other. Consistent with previous studies, the time lag of global oscillation, although being less spatially homogenous than what was assumed to be, contributed to the RSN synchronization. However, an equivalent contribution was also shown on the part of the more locally confined activities that are independent to each other. While allowing the view that network-specific coactivation occurs as part of the sequences of global neural activities, these results further confirm an equally important role of the network-specific regulation for its coactivation, regardless of whether vascular artifacts contaminate the global component in fMRI measures.

Control of breathing in invertebrate model systems

The invertebrates have adopted a myriad of breathing strategies to facilitate the extraction of adequate quantities of oxygen from their surrounding environments. Their respiratory structures can take a wide variety of forms, including integumentary surfaces, lungs, gills, tracheal systems, and even parallel combinations of these same gas exchange structures. Like their vertebrate counterparts, the invertebrates have evolved elaborate control strategies to regulate their breathing activity. Our goal in this article is to present the reader with a description of what is known regarding the control of breathing in some of the specific invertebrate species that have been used as model systems to study different mechanistic aspects of the control of breathing. We will examine how several species have been used to study fundamental principles of respiratory rhythm generation, central and peripheral chemosensory modulation of breathing, and plasticity in the control of breathing. We will also present the reader with an overview of some of the behavioral and neuronal adaptability that has been extensively documented in these animals. By presenting explicit invertebrate species as model organisms, we will illustrate mechanistic principles that form the neuronal foundation of respiratory control, and moreover appear likely to be conserved across not only invertebrates, but vertebrate species as well.

Low latency breathing frequency detection and monitoring on a personal computer

We demonstrate a low latency respiratory/breathing frequency detection system that is fast (<5 ms), easy to operate, requires no batteries or external power supply and operates fully via computer-standard USB connection. Exercises in controlling ones breathing frequency, usually referred to as paced-breathing exercises, have shown positive effects in treating pulmonary diseases, cardiovascular diseases and stress/anxiety-related disorders. We developed a breathing frequency detection system which uses two pairs of microphones to detect exhalation activity, eliminate noise from the environment and stream the recording data via USB connection to a personal computer. It showed 97.1% reliability (10 subjects) when monitoring breathing activity in non-guided free breathing and 100% reliability (10 subjects) when monitoring breathing activity during interactive paced-breathing exercises. We also evaluated the breathing frequency detection systems noise elimination functionality which showed a reduction of 84.2 dB for stationary (white noise) and a reduction of 79.3 dB for non-stationary (hands clapping) noise.

Simple cellular and network control principles govern complex patterns of motor behavior

The vertebrate central nervous system is organized in modules that independently execute sophisticated tasks. Such modules are flexibly controlled and operate with a considerable degree of autonomy. One example is locomotion generated by spinal central pattern generator networks (CPGs) that shape the detailed motor output. The level of activity is controlled from brainstem locomotor command centers, which in turn, are under the control of the basal ganglia. By using a biophysically detailed, full-scale computational model of the lamprey CPG (10,000 neurons) and its brainstem/forebrain control, we demonstrate general control principles that can adapt the network to different demands. Forward or backward locomotion and steering can be flexibly controlled by local synaptic effects limited to only the very rostral part of the network. Variability in response properties within each neuronal population is an essential feature and assures a constant phase delay along the cord for different locomotor speeds.

Recognizing sequences of sequences

The brain's decoding of fast sensory streams is currently impossible to emulate, even approximately, with artificial agents. For example, robust speech recognition is relatively easy for humans but exceptionally difficult for artificial speech recognition systems. In this paper, we propose that recognition can be simplified with an internal model of how sensory input is generated, when formulated in a Bayesian framework. We show that a plausible candidate for an internal or generative model is a hierarchy of ‘stable heteroclinic channels’. This model describes continuous dynamics in the environment as a hierarchy of sequences, where slower sequences cause faster sequences. Under this model, online recognition corresponds to the dynamic decoding of causal sequences, giving a representation of the environment with predictive power on several timescales. We illustrate the ensuing decoding or recognition scheme using synthetic sequences of syllables, where syllables are sequences of phonemes and phonemes are sequences of sound-wave modulations. By presenting anomalous stimuli, we find that the resulting recognition dynamics disclose inference at multiple time scales and are reminiscent of neuronal dynamics seen in the real brain.

Despite tremendous advances in neuroscience, we cannot yet build machines that recognize the world as effortlessly as we do. One reason might be that there are computational approaches to recognition that have not yet been exploited. Here, we demonstrate that the ability to recognize temporal sequences might play an important part. We show that an artificial decoding device can extract natural speech sounds from sound waves if speech is generated as dynamic and transient sequences of sequences. In principle, this means that artificial recognition can be implemented robustly and online using dynamic systems theory and Bayesian inference.