The cortex as a central pattern generator

Embryonically active piriform cortex neurons promote intracortical recurrent connectivity during development

Neuronal activity plays a critical role in the maturation of circuits that propagate sensory information into the brain. How widely does early activity regulate circuit maturation across the developing brain? Here, we used targeted recombination in active populations (TRAP) to perform a brain-wide survey for prenatally active neurons in mice and identified the piriform cortex as an abundantly TRAPed region. Whole-cell recordings in neonatal slices revealed preferential interconnectivity within embryonically TRAPed piriform neurons and their enhanced synaptic connectivity with other piriform neurons. In vivo Neuropixels recordings in neonates demonstrated that embryonically TRAPed piriform neurons exhibit broad functional connectivity within piriform and lead spontaneous synchronized population activity during a transient neonatal period, when recurrent connectivity is strengthening. Selectively activating or silencing these neurons in neonates enhanced or suppressed recurrent synaptic strength, respectively. Thus, embryonically TRAPed piriform neurons represent an interconnected hub-like population whose activity promotes recurrent connectivity in early development.

Embryonically Active Piriform Cortex Neurons Promote Intracortical Recurrent Connectivity during Development

Neuronal activity plays a critical role in the maturation of circuits that propagate sensory information into the brain. How widely does early activity regulate circuit maturation across the developing brain? Here, we used Targeted Recombination in Active Populations (TRAP) to perform a brain-wide survey for prenatally active neurons in mice and identified the piriform cortex as an abundantly TRAPed region. Whole-cell recordings in neonatal slices revealed preferential interconnectivity within embryonically TRAPed piriform neurons and their enhanced synaptic connectivity with other piriform neurons. In vivo Neuropixels recordings in neonates demonstrated that embryonically TRAPed piriform neurons exhibit broad functional connectivity within piriform and lead spontaneous synchronized population activity during a transient neonatal period, when recurrent connectivity is strengthening. Selectively activating or silencing of these neurons in neonates enhanced or suppressed recurrent synaptic strength, respectively. Thus, embryonically TRAPed piriform neurons represent an interconnected hub-like population whose activity promotes recurrent connectivity in early development.

Error Function Optimization to Compare Neural Activity and Train Blended Rhythmic Networks

We present a novel set of quantitative measures for "likeness" (error function) designed to alleviate the time-consuming and subjective nature of manually comparing biological recordings from electrophysiological experiments with the outcomes of their mathematical models. Our innovative "blended" system approach offers an objective, high-throughput, and computationally efficient method for comparing biological and mathematical models. This approach involves using voltage recordings of biological neurons to drive and train mathematical models, facilitating the derivation of the error function for further parameter optimization. Our calibration process incorporates measurements such as action potential (AP) frequency, voltage moving average, voltage envelopes, and the probability of post-synaptic channels. To assess the effectiveness of our method, we utilized the sea slug Melibe leonina swim central pattern generator (CPG) as our model circuit and conducted electrophysiological experiments with TTX to isolate CPG interneurons. During the comparison of biological recordings and mathematically simulated neurons, we performed a grid search of inhibitory and excitatory synapse conductance. Our findings indicate that a weighted sum of simple functions is essential for comprehensively capturing a neuron's rhythmic activity. Overall, our study suggests that our blended system approach holds promise for enabling objective and high-throughput comparisons between biological and mathematical models, offering significant potential for advancing research in neural circuitry and related fields.

Early Intervention Guided by the General Movements Examination at Term Corrected Age-Short Term Outcomes

BACKGROUND AND AIM

The early identification of the former premature neonates at risk of neurologic sequelae could lead to early intervention and a better prognosis. This pilot study aimed to investigate whether the General Movement patterns observed at term-equivalent age in former premature infants could serve as predictors for guiding early intervention and improving prognosis.

MATERIALS AND METHODS

In a population of 44 premature neonates (mean gestational age 33.59 weeks (+2.43 weeks)) examined at term-equivalent age, 10 neonates with a cramped-synchronized General Movements motor pattern were identified. These neonates were included in an early intervention program consisting of physiotherapy executed both by the therapist and by the parents at home. They were again examined at a corrected age of 12 weeks. The presence or absence of fidgety movements and the MOS-R (motor optimality score revised) was noted. The examinations were performed by certified specialists.

RESULTS

Normal fidgety movements and a MOS-R of 20-24 were presented in 9/10 of the former premature infants, with normal foot to foot contact present in 7/10, and normal hand to hand contact present in 5/10. The atypical patterns noted were side to side movements of the head in 5/10, a non-centered head in 9/10, asymmetric tonic neck reflex in 9/10 and jerky movements in 10/10. One patient presented with no fidgety movements and a MOS-R score of 9.

CONCLUSION

Early intervention in our group of patients allowed for an improvement in the neurologic status, demonstrated by the presence of fidgety movements. We suggest that early intervention should be indicated in all premature infants that present with a cramped-synchronized GM pattern during examination at term-equivalent age. However, due to the small sample size, the absence of statistical analysis and a control group, and the limited follow-up period, the conclusions must be approached with caution.

Preparatory activity and the expansive null-space

The study of the cortical control of movement experienced a conceptual shift over recent decades, as the basic currency of understanding shifted from single-neuron tuning towards population-level factors and their dynamics. This transition was informed by a maturing understanding of recurrent networks, where mechanism is often characterized in terms of population-level factors. By estimating factors from data, experimenters could test network-inspired hypotheses. Central to such hypotheses are 'output-null' factors that do not directly drive motor outputs yet are essential to the overall computation. In this Review, we highlight how the hypothesis of output-null factors was motivated by the venerable observation that motor-cortex neurons are active during movement preparation, well before movement begins. We discuss how output-null factors then became similarly central to understanding neural activity during movement. We discuss how this conceptual framework provided key analysis tools, making it possible for experimenters to address long-standing questions regarding motor control. We highlight an intriguing trend: as experimental and theoretical discoveries accumulate, the range of computational roles hypothesized to be subserved by output-null factors continues to expand.

Prefrontal projections modulate recurrent circuitry in the insular cortex to support short-term memory

Short-term memory (STM) maintains information during a short delay period. How long-range and local connections interact to support STM encoding remains elusive. Here, we tackle the problem focusing on long-range projections from the medial prefrontal cortex (mPFC) to the anterior agranular insular cortex (aAIC) in head-fixed mice performing an olfactory delayed-response task. Optogenetic and electrophysiological experiments reveal the behavioral importance of the two regions in encoding STM information. Spike-correlogram analysis reveals strong local and cross-region functional coupling (FC) between memory neurons encoding the same information. Optogenetic suppression of mPFC-aAIC projections during the delay period reduces behavioral performance, the proportion of memory neurons, and memory-specific FC within the aAIC, whereas optogenetic excitation enhances all of them. mPFC-aAIC projections also bidirectionally modulate the efficacy of STM-information transfer, measured by the contribution of FC spiking pairs to the memory-coding ability of following neurons. Thus, prefrontal projections modulate insular neurons' functional connectivity and memory-coding ability to support STM.

Role of the basal ganglia in innate and learned behavioural sequences

Integrating individual actions into coherent, organised behavioural units, a process called chunking, is a fundamental, evolutionarily conserved process that renders actions automatic. In vertebrates, evidence points to the basal ganglia - a complex network believed to be involved in action selection - as a key component of action sequence encoding, although the underlying mechanisms are only just beginning to be understood. Central pattern generators control many innate automatic behavioural sequences that form some of the most basic behaviours in an animal's repertoire, and in vertebrates, brainstem and spinal pattern generators are under the control of higher order structures such as the basal ganglia. Evidence suggests that the basal ganglia play a crucial role in the concatenation of simpler behaviours into more complex chunks, in the context of innate behavioural sequences such as chain grooming in rats, as well as sequences in which innate capabilities and learning interact such as birdsong, and sequences that are learned from scratch, such as lever press sequences in operant behaviour. It has been proposed that the role of the striatum, the largest input structure of the basal ganglia, might lie in selecting and allowing the relevant central pattern generators to gain access to the motor system in the correct order, while inhibiting other behaviours. As behaviours become more complex and flexible, the pattern generators seem to become more dependent on descending signals. Indeed, during learning, the striatum itself may adopt the functional characteristics of a higher order pattern generator, facilitated at the microcircuit level by striatal neuropeptides.

Correlations between Head Ultrasounds Performed at Term-Equivalent Age in Premature Neonates and General Movements Neurologic Examination Patterns

BACKGROUND AND AIM

Our research aims to find correlations between the brain imaging performed at term-corrected age and the atypical general movement (GM) patterns noticed during the same visit a-cramped-synchronized (CS) or poor repertoire (PR)-in formerly premature neonates to provide evidence for the structures involved in the modulation of GM patterns that could be injured and result in the appearance of these patterns and further deficits.

MATERIALS AND METHODS

A total of 44 preterm neonates ((mean GA, 33.59 weeks (+2.43 weeks)) were examined in the follow-up program at Life Memorial Hospital Bucharest at term-equivalent age (TEA). The GM and ultrasound examinations were performed by trained and certified specialists. Three GM pattens were noted (normal, PR, or CS), and the measurements of the following cerebral structures were conducted via head ultrasounds: ventricular index, the short and long axes of the lateral ventricles, the midbody distance of the lateral ventricle, the diagonal of the caudate nucleus, the width of the basal ganglia, the width of the interhemispheric fissure, the sinocortical width, the length and thickness of the callosal body, the anteroposterior diameter of the pons, the diameter of the vermis, and the transverse diameters of the cerebellum and vermis. The ultrasound measurements were compared between the groups in order to find statistically significant correlations by using the FANOVA test (significance p < 0.05).

RESULTS

The presence of the CS movement pattern was significantly associated with an increased ventricular index (mean 11.36 vs. 8.90; p = 0.032), increased midbody distance of the lateral ventricle-CS versus PR (8.31 vs. 3.73; p = 0.001); CS versus normal (8.31 vs. 3.34; p = 0.001), increased long and short axes of the lateral ventricles (p < 0.001), and decreased width of the basal ganglia-CS versus PR (11.07 vs. 15.69; p = 0.001); CS versus normal pattern (11.07 vs. 15.15; p = 0.0010). The PR movement pattern was significantly associated with an increased value of the sinocortical width when compared to the CS pattern (p < 0.001) and a decreased anteroposterior diameter of the pons when compared to both the CS (12.06 vs. 16.83; p = 0.001) and normal (12.06 vs. 16.78; p = 0.001) patterns. The same correlations were present when the subgroup of infants with a GA ≤ 32 weeks was analyzed.

CONCLUSIONS

Our study demonstrated that there are correlations between atypical GM patterns (cramped-synchronized-CS and poor repertoire-PR) and abnormalities in the dimensions of the structures measured via ultrasound at the term-equivalent age. The correlations could provide information about the structures that are affected and could lead to a lack of modulation in the GM patterns.

Effects of different food hardness on cognitive inhibitory control function

Mastication leads to an immediate enhancement in cognitive functions, including inhibitory control. Furthermore, the hardness of the food increases sympathetic nerve activity during and immediately after mastication. Hence, the cognitive function could be enhanced by increased sympathetic nerve activity. The present study aimed to investigate the effects of food hardness on cognitive inhibitory control function in humans. The participants were 23 healthy adults (19-22 years old). Experiments were conducted with two types of gummies (soft and hard). The participants ingested 13 g of gummies and performed a stop-signal task to measure cognitive inhibitory control function after they rested for 5 min. The reaction time for the stop-signal task after gummy consumption was significantly shorter in the hard gummy condition compared to the soft gummy condition (p < .05). Furthermore, the accuracy rate of the responses was also significantly higher in the hard gummy condition compared to the soft gummy condition (p < .05). The results of the present study suggest that food hardness enhances cognitive inhibitory control function in humans.

Autogenous cerebral processes: an invitation to look at the brain from inside out

While external stimulation can reliably trigger neuronal activity, cerebral processes can operate independently from the environment. In this study, we conceptualize autogenous cerebral processes (ACPs) as intrinsic operations of the brain that exist on multiple scales and can influence or shape stimulus responses, behavior, homeostasis, and the physiological state of an organism. We further propose that the field should consider exploring to what extent perception, arousal, behavior, or movement, as well as other cognitive functions previously investigated mainly regarding their stimulus-response dynamics, are ACP-driven.

The arrow of time of brain signals in cognition: Potential intriguing role of parts of the default mode network

A promising idea in human cognitive neuroscience is that the default mode network (DMN) is responsible for coordinating the recruitment and scheduling of networks for computing and solving task-specific cognitive problems. This is supported by evidence showing that the physical and functional distance of DMN regions is maximally removed from sensorimotor regions containing environment-driven neural activity directly linked to perception and action, which would allow the DMN to orchestrate complex cognition from the top of the hierarchy. However, discovering the functional hierarchy of brain dynamics requires finding the best way to measure interactions between brain regions. In contrast to previous methods measuring the hierarchical flow of information using, for example, transfer entropy, here we used a thermodynamics-inspired, deep learning based Temporal Evolution NETwork (TENET) framework to assess the asymmetry in the flow of events, 'arrow of time', in human brain signals. This provides an alternative way of quantifying hierarchy, given that the arrow of time measures the directionality of information flow that leads to a breaking of the balance of the underlying hierarchy. In turn, the arrow of time is a measure of nonreversibility and thus nonequilibrium in brain dynamics. When applied to large-scale Human Connectome Project (HCP) neuroimaging data from close to a thousand participants, the TENET framework suggests that the DMN plays a significant role in orchestrating the hierarchy, that is, levels of nonreversibility, which changes between the resting state and when performing seven different cognitive tasks. Furthermore, this quantification of the hierarchy of the resting state is significantly different in health compared to neuropsychiatric disorders. Overall, the present thermodynamics-based machine-learning framework provides vital new insights into the fundamental tenets of brain dynamics for orchestrating the interactions between cognition and brain in complex environments.

A universal description of stochastic oscillators

Many systems in physics, chemistry, and biology exhibit oscillations with a pronounced random component. Such stochastic oscillations can emerge via different mechanisms, for example, linear dynamics of a stable focus with fluctuations, limit-cycle systems perturbed by noise, or excitable systems in which random inputs lead to a train of pulses. Despite their diverse origins, the phenomenology of random oscillations can be strikingly similar. Here, we introduce a nonlinear transformation of stochastic oscillators to a complex-valued function [Formula: see text](x) that greatly simplifies and unifies the mathematical description of the oscillator's spontaneous activity, its response to an external time-dependent perturbation, and the correlation statistics of different oscillators that are weakly coupled. The function [Formula: see text] (x) is the eigenfunction of the Kolmogorov backward operator with the least negative (but nonvanishing) eigenvalue λ1 = μ1 + iω1. The resulting power spectrum of the complex-valued function is exactly given by a Lorentz spectrum with peak frequency ω1 and half-width μ1; its susceptibility with respect to a weak external forcing is given by a simple one-pole filter, centered around ω1; and the cross-spectrum between two coupled oscillators can be easily expressed by a combination of the spontaneous power spectra of the uncoupled systems and their susceptibilities. Our approach makes qualitatively different stochastic oscillators comparable, provides simple characteristics for the coherence of the random oscillation, and gives a framework for the description of weakly coupled oscillators.

Dynamic models for musical rhythm perception and coordination

Rhythmicity permeates large parts of human experience. Humans generate various motor and brain rhythms spanning a range of frequencies. We also experience and synchronize to externally imposed rhythmicity, for example from music and song or from the 24-h light-dark cycles of the sun. In the context of music, humans have the ability to perceive, generate, and anticipate rhythmic structures, for example, "the beat." Experimental and behavioral studies offer clues about the biophysical and neural mechanisms that underlie our rhythmic abilities, and about different brain areas that are involved but many open questions remain. In this paper, we review several theoretical and computational approaches, each centered at different levels of description, that address specific aspects of musical rhythmic generation, perception, attention, perception-action coordination, and learning. We survey methods and results from applications of dynamical systems theory, neuro-mechanistic modeling, and Bayesian inference. Some frameworks rely on synchronization of intrinsic brain rhythms that span the relevant frequency range; some formulations involve real-time adaptation schemes for error-correction to align the phase and frequency of a dedicated circuit; others involve learning and dynamically adjusting expectations to make rhythm tracking predictions. Each of the approaches, while initially designed to answer specific questions, offers the possibility of being integrated into a larger framework that provides insights into our ability to perceive and generate rhythmic patterns.

Recurrent network interactions explain tectal response variability and experience-dependent behavior

Response variability is an essential and universal feature of sensory processing and behavior. It arises from fluctuations in the internal state of the brain, which modulate how sensory information is represented and transformed to guide behavioral actions. In part, brain state is shaped by recent network activity, fed back through recurrent connections to modulate neuronal excitability. However, the degree to which these interactions influence response variability and the spatial and temporal scales across which they operate, are poorly understood. Here, we combined population recordings and modeling to gain insights into how neuronal activity modulates network state and thereby impacts visually evoked activity and behavior. First, we performed cellular-resolution calcium imaging of the optic tectum to monitor ongoing activity, the pattern of which is both a cause and consequence of changes in network state. We developed a minimal network model incorporating fast, short range, recurrent excitation and long-lasting, activity-dependent suppression that reproduced a hallmark property of tectal activity - intermittent bursting. We next used the model to estimate the excitability state of tectal neurons based on recent activity history and found that this explained a portion of the trial-to-trial variability in visually evoked responses, as well as spatially selective response adaptation. Moreover, these dynamics also predicted behavioral trends such as selective habituation of visually evoked prey-catching. Overall, we demonstrate that a simple recurrent interaction motif can be used to estimate the effect of activity upon the incidental state of a neural network and account for experience-dependent effects on sensory encoding and visually guided behavior.

Cocaine regulates sensory filtering in cortical pyramidal neurons

Exposure to cocaine leads to robust changes in the structure and function of neurons within the mesocorticolimbic pathway. However, little is known about how cocaine influences the processing of information within the sensory cortex. We address this by using patch-clamp and juxtacellular voltage recordings and two-photon Ca2+ imaging in vivo to investigate the influence of acute cocaine exposure on layer 2/3 (L2/3) pyramidal neurons within the primary somatosensory cortex (S1). Here, cocaine dampens membrane potential state transitions and decreases spontaneous somatic action potentials and Ca2+ transients. In contrast to the uniform decrease in background spontaneous activity, cocaine has a heterogeneous influence on sensory encoding, increasing tactile-evoked responses in dendrites that do not typically encode sensory information and decreasing responses in those dendrites that are more reliable sensory encoders. Combined, these findings suggest that cocaine acts as a filter that suppresses background noise to selectively modulate incoming sensory information.

An intrinsic oscillator underlies visual navigation in ants

Many insects display lateral oscillations while moving, but how these oscillations are produced and participate in visual navigation remains unclear. Here, we show that visually navigating ants continuously display regular lateral oscillations coupled with variations of forward speed that strongly optimize the distance covered while simultaneously enabling them to scan left and right directions. This pattern of movement is produced endogenously and conserved across navigational contexts in two phylogenetically distant ant species. Moreover, the oscillations' amplitude can be modulated by both innate or learnt visual cues to adjust the exploration/exploitation balance to the current need. This lower-level motor pattern thus drastically reduces the degree of freedom needed for higher-level strategies to control behavior. The observed dynamical signature readily emerges from a simple neural circuit model of the insect's conserved pre-motor area known as the lateral accessory lobe, offering a surprisingly simple but effective neural control and endorsing oscillation as a core, ancestral way of moving in insects.

Caressed by music: Related preferences for velocity of touch and tempo of music?

Given that both hearing and touch are 'mechanical senses' that respond to physical pressure or mechanical energy and that individuals appear to have a characteristic internal or spontaneous tempo, individual preferences in musical and touch rhythms might be related. We explored this in two experiments probing individual preferences for tempo in the tactile and auditory modalities. Study 1 collected ratings of received stroking on the forearm and measured the velocity the participants used for stroking a fur. Music tempo preferences were assessed as mean beats per minute of individually selected music pieces and via the adjustment of experimenter-selected music to a preferred tempo. Heart rate was recorded to measure levels of physiological arousal. We found that the preferred tempo of favorite (self-selected) music correlated positively with the velocity with which each individual liked to be touched. In Study 2, participants rated videos of repeated touch on someone else's arm and videos of a drummer playing with brushes on a snare drum, both at a variety of tempos. We found that participants with similar rating patterns for the different stroking speeds did not show similar rating patterns for the different music beats. The results suggest that there may be a correspondence between preferences for favorite music and felt touch, but this is either weak or it cannot be evoked effectively with vicarious touch and/or mere drum beats. Thus, if preferences for touch and music are related, this is likely to be dependent on the specific type of stimulation.

Electric-field-coupled oscillators for collective electrochemical perception in biohybrid robotics

This work explores the application of nonlinear oscillators coupled by an electric field in water, inspired by weakly electric fish. Such coupled oscillators operate in clear and colloidal (mud, bottom silt) water and represent a collective electrochemical sensor that is sensitive to global environmental parameters, the geometry of the common electric field and spatial dynamics of autonomous underwater vehicles (AUVs). Implemented in hardware and software, this approach can be used to create global awareness in a group of robots, which possess limited sensing and communication capabilities. Using oscillators from different AUVs enables extension of the range limitations related to the electric dipole of a single AUV. Applications of this technique are demonstrated for detecting the number of AUVs, distances between them, perception of dielectric objects and synchronization of behavior. Recognizing self-/nonself-generated signals by electric fish is re-embodied in a technological way through an 'electrical mirror' for discrimination between 'collective self' and 'collective nonself'. These approaches have been implemented in several research projects with bioinspired/biohybrid systems in fresh and salt water, and electrochemical sensing in fluidic media.

Neuronal metabolism in learning and memory: The anticipatory activity perspective

Current research on the molecular mechanisms of learning and memory is based on the "stimulus-response" paradigm, in which the neural circuits connecting environmental events with behavioral responses are strengthened. By contrast, cognitive and systems neuroscience emphasize the intrinsic activity of the brain that integrates information, establishes anticipatory actions, executes adaptive actions, and assesses the outcome via regulatory feedback mechanisms. We believe that the difference in the perspectives of systems and molecular studies is a major roadblock to further progress toward understanding the mechanisms of learning and memory. Here, we briefly overview the current studies in molecular mechanisms of learning and memory and propose that studying the predictive properties of neuronal metabolism will significantly advance our knowledge of how intrinsic, predictive activity of neurons shapes a new learning event. We further suggest that predictive metabolic changes in the brain may also take place in non-neuronal cells, including those of peripheral tissues. Finally, we present a path forward toward more in-depth studies of the role of cell metabolism in learning and memory.

Cortical traveling waves reflect state-dependent hierarchical sequencing of local regions in the human connectome network

Recent human studies using electrocorticography have demonstrated that alpha and theta band oscillations form traveling waves on the cortical surface. According to neural synchronization theories, the cortical traveling waves may group local cortical regions and sequence them by phase synchronization; however these contributions have not yet been assessed. This study aimed to evaluate the functional contributions of traveling waves using connectome-based network modeling. In the simulation, we observed stable traveling waves on the entire cortical surface wherein the topographical pattern of these phases was substantially correlated with the empirically obtained resting-state networks, and local radial waves also appeared within the size of the empirical networks (< 50 mm). Importantly, individual regions in the entire network were instantaneously sequenced by their internal frequencies, and regions with higher intrinsic frequency were seen in the earlier phases of the traveling waves. Based on the communication-through-coherence theory, this phase configuration produced a hierarchical organization of each region by unidirectional communication between the arbitrarily paired regions. In conclusion, cortical traveling waves reflect the intrinsic frequency-dependent hierarchical sequencing of local regions, global traveling waves sequence the set of large-scale cortical networks, and local traveling waves sequence local regions within individual cortical networks.

Sequential Attractors in Combinatorial Threshold-Linear Networks

Sequences of neural activity arise in many brain areas, including cortex, hippocampus, and central pattern generator circuits that underlie rhythmic behaviors like locomotion. While network architectures supporting sequence generation vary considerably, a common feature is an abundance of inhibition. In this work, we focus on architectures that support sequential activity in recurrently connected networks with inhibition-dominated dynamics. Specifically, we study emergent sequences in a special family of threshold-linear networks, called combinatorial threshold-linear networks (CTLNs), whose connectivity matrices are defined from directed graphs. Such networks naturally give rise to an abundance of sequences whose dynamics are tightly connected to the underlying graph. We find that architectures based on generalizations of cycle graphs produce limit cycle attractors that can be activated to generate transient or persistent (repeating) sequences. Each architecture type gives rise to an infinite family of graphs that can be built from arbitrary component subgraphs. Moreover, we prove a number of graph rules for the corresponding CTLNs in each family. The graph rules allow us to strongly constrain, and in some cases fully determine, the fixed points of the network in terms of the fixed points of the component subnetworks. Finally, we also show how the structure of certain architectures gives insight into the sequential dynamics of the corresponding attractor.

Exploiting Spherical Projections To Generate Human-Like Wrist Pointing Movements

The mechanism behind the generation of human movements is of great interest in many fields (e.g. robotics and neuroscience) to improve therapies and technologies. Optimal Feedback Control (OFC) and Passive Motion Paradigm (PMP) are currently two leading theories capable of effectively producing human-like motions, but they require solving nonlinear inverse problems to find a solution. The main benefit of using PMP is the possibility of generating path-independent movements consistent with the stereotypical behaviour observed in humans, while the equivalent OFC formulation is path-dependent. Our results demonstrate how the path-independent behaviour observed for the wrist pointing task can be explained by spherical projections of the planar tasks. The combination of the projections with the fractal impedance controller eliminates the nonlinear inverse problem, which reduces the computational cost compared to previous methodologies. The motion exploits a recently proposed PMP architecture that replaces the nonlinear inverse optimisation with a nonlinear anisotropic stiffness impedance profile generated by the Fractal Impedance Controller, reducing the computational cost and not requiring a task-dependent optimisation.

Zolpidem for Insomnia: A Double-Edged Sword. A Systematic Literature Review on Zolpidem-Induced Complex Sleep Behaviors

BACKGROUND

Being a nonbenzodiazepine, zolpidem is believed to have a favorable side-effect profile and is widely prescribed for insomnia. However, in the past few years, numerous neuropsychiatric adverse reactions, particularly complex sleep behaviors (CSBs), have been reported with zolpidem.

OBJECTIVE

To conduct a systematic review of zolpidem-associated CSBs.

DATA SOURCES

An electronic search was conducted using MEDLINE, Embase, PubMed, and Cochrane database of systematic reviews to extract relevant articles till July 2020.

STUDY ELIGIBILITY CRITERIA

Any type of literature article (case report, case series, and observational or interventional study) reporting CSBs associated with zolpidem.

RESULTS

In this review, we present aggregate summarized data from 148 patients presenting with zolpidem-induced CSBs (79 patients from 23 case reports and 5 case series; 69 patients out of 1454 taking zolpidem [4.7%] from three observational clinical studies). Various types of CSBs associated with zolpidem were reported, most common being sleepwalking/somnambulism and sleep-related eating disorder. On causality assessment, around 88% of cases were found to have a probable association with zolpidem.

LIMITATIONS

Extraction of data from observational studies and spontaneous reports, due to nonavailability of any randomized controlled trials relevant to the study objective.

CONCLUSION AND IMPLICATION OF KEY FINDINGS

Zolpidem-induced CSBs, although not very common, may develop when the drug is used at therapeutic doses for insomnia. Doctors need to be alert to monitor such adverse effects of zolpidem and exercise caution while prescribing it.

Multiple states in ongoing neural activity in the rat visual cortex

The brain continuously produces internal activity in the absence of afferently salient sensory input. Spontaneous neural activity is intrinsically defined by circuit structures and associated with the mode of information processing and behavioral responses. However, the spatiotemporal dynamics of spontaneous activity in the visual cortices of behaving animals remain almost elusive. Using a custom-made electrode array, we recorded 32-site electrocorticograms in the primary and secondary visual cortex of freely behaving rats and determined the propagation patterns of spontaneous neural activity. Nonlinear dimensionality reduction and unsupervised clustering revealed multiple discrete states of the activity patterns. The activity remained stable in one state and suddenly jumped to another state. The diversity and dynamics of the internally switching cortical states would imply flexibility of neural responses to various external inputs.

Blocking NK1 receptors disrupts the sequential and temporal organization of chain grooming in rats

The basal ganglia are a group of sub-cortical structures believed to play a critical role in action selection and sequencing. The striatum is the largest input structure of the basal ganglia and contains the neuropeptide substance P in abundance. Recent computational work has suggested that substance P could play a critical role in action sequence performance and acquisition, but this has not been tested experimentally before. The aim of the present study was to test how blocking substance P's main NK1-type receptors affected the sequential and temporal organization of spontaneous behavioral patterns. We did this in rats by focusing on the grooming chain, an innate and highly stereotyped ordered sequence. We performed an open field experiment in which the NK1 receptor antagonist L-733,060 was injected intraperitoneally in rats at two doses (2 and 4 mg/kg/ml), in a within-subject counterbalanced design. We used first order transition probabilities, Variable Length Markov Models, entropy metrics and T-pattern analysis to evaluate the effects of L-733,060 on sequential and temporal aspects of spontaneously ordered behavioral sequences. Our results suggest that blocking NK1 receptors made the transitions between the grooming chain elements significantly more variable, the transition structure of the grooming bouts simpler, and it increased the probability of transitioning from active to inactive states. Overall, this suggest that blocking substance P receptors led to a general break down in the fluency of spontaneous behavioral sequences, suggesting that substance P could be playing a key role in the implementation of sequential patterns.

Neuronal ensembles in memory processes

A neuronal ensemble represents the concomitant activity of a specific group of neurons that could encompass a broad repertoire of brain functions such as motor, perceptual, memory or cognitive states. On the other hand, a memory engram portrays the physical manifestation of memory or the changes that enable learning and retrieval. Engram studies focused for many years on finding where memories are stored as in, which cells or brain regions represent a memory trace, and disregarded the investigation of how neuronal activity patterns give rise to such memories. Recent experiments suggest that the association and reactivation of specific neuronal groups could be the main mechanism underlying the brain's ability to remember past experiences and envision future actions. Thus, the growing consensus is that the interaction between neuronal ensembles could allow sequential activity patterns to become memories and recurrent memories to compose complex behaviors. The goal of this review is to propose how the neuronal ensemble framework could be translated and useful to understand memory processes.

The death of the cortical column? Patchwork structure and conceptual retirement in neuroscientific practice

In 1981, David Hubel and Torsten Wiesel received the Nobel Prize for their research on cortical columns-vertical bands of neurons with similar functional properties. This success led to the view that "cortical column" refers to the basic building block of the mammalian neocortex. Since the 1990s, however, critics questioned this building block picture of "cortical column" and debated whether this concept is useless and should be replaced with successor concepts. This paper inquires which experimental results after 1981 challenged the building block picture and whether these challenges warrant the elimination "cortical column" from neuroscientific discourse. I argue that the proliferation of experimental techniques led to a patchwork of locally adapted uses of the column concept. Each use refers to a different kind of cortical structure, rather than a neocortical building block. Once we acknowledge this diverse-kinds picture of "cortical column", the elimination of column concept becomes unnecessary. Rather, I suggest that "cortical column" has reached conceptual retirement: although it cannot be used to identify a neocortical building block, column research is still useful as a guide and cautionary tale for ongoing research. At the same time, neuroscientists should search for alternative concepts when studying the functional architecture of the neocortex. keywords: Cortical column, conceptual development, history of neuroscience, patchwork, eliminativism, conceptual retirement.

The brain as a dynamically active organ

Nervous systems are typically described as static networks passively responding to external stimuli (i.e., the 'sensorimotor hypothesis'). However, for more than a century now, evidence has been accumulating that this passive-static perspective is wrong. Instead, evidence suggests that nervous systems dynamically change their connectivity and actively generate behavior so their owners can achieve goals in the world, some of which involve controlling their sensory feedback. This review provides a brief overview of the different historical perspectives on general brain function and details some select modern examples falsifying the sensorimotor hypothesis.

Learning Long Temporal Sequences in Spiking Networks by Multiplexing Neural Oscillations

Many cognitive and behavioral tasks-such as interval timing, spatial navigation, motor control, and speech-require the execution of precisely-timed sequences of neural activation that cannot be fully explained by a succession of external stimuli. We show how repeatable and reliable patterns of spatiotemporal activity can be generated in chaotic and noisy spiking recurrent neural networks. We propose a general solution for networks to autonomously produce rich patterns of activity by providing a multi-periodic oscillatory signal as input. We show that the model accurately learns a variety of tasks, including speech generation, motor control, and spatial navigation. Further, the model performs temporal rescaling of natural spoken words and exhibits sequential neural activity commonly found in experimental data involving temporal processing. In the context of spatial navigation, the model learns and replays compressed sequences of place cells and captures features of neural activity such as the emergence of ripples and theta phase precession. Together, our findings suggest that combining oscillatory neuronal inputs with different frequencies provides a key mechanism to generate precisely timed sequences of activity in recurrent circuits of the brain.

Fate of Duplicated Neural Structures

Statistical physics determines the abundance of different arrangements of matter depending on cost-benefit balances. Its formalism and phenomenology percolate throughout biological processes and set limits to effective computation. Under specific conditions, self-replicating and computationally complex patterns become favored, yielding life, cognition, and Darwinian evolution. Neurons and neural circuits sit at a crossroads between statistical physics, computation, and (through their role in cognition) natural selection. Can we establish a statistical physics of neural circuits? Such theory would tell what kinds of brains to expect under set energetic, evolutionary, and computational conditions. With this big picture in mind, we focus on the fate of duplicated neural circuits. We look at examples from central nervous systems, with stress on computational thresholds that might prompt this redundancy. We also study a naive cost-benefit balance for duplicated circuits implementing complex phenotypes. From this, we derive phase diagrams and (phase-like) transitions between single and duplicated circuits, which constrain evolutionary paths to complex cognition. Back to the big picture, similar phase diagrams and transitions might constrain I/O and internal connectivity patterns of neural circuits at large. The formalism of statistical physics seems to be a natural framework for this worthy line of research.

Coordination Dynamics: A Foundation for Understanding Social Behavior

Humans' interactions with each other or with socially competent machines exhibit lawful coordination patterns at multiple levels of description. According to Coordination Dynamics, such laws specify the flow of coordination states produced by functional synergies of elements (e.g., cells, body parts, brain areas, people…) that are temporarily organized as single, coherent units. These coordinative structures or synergies may be mathematically characterized as informationally coupled self-organizing dynamical systems (Coordination Dynamics). In this paper, we start from a simple foundation, an elemental model system for social interactions, whose behavior has been captured in the Haken-Kelso-Bunz (HKB) model. We follow a tried and tested scientific method that tightly interweaves experimental neurobehavioral studies and mathematical models. We use this method to further develop a body of empirical research that advances the theory toward more generalized forms. In concordance with this interdisciplinary spirit, the present paper is written both as an overview of relevant advances and as an introduction to its mathematical underpinnings. We demonstrate HKB's evolution in the context of social coordination along several directions, with its applicability growing to increasingly complex scenarios. In particular, we show that accommodating for symmetry breaking in intrinsic dynamics and coupling, multiscale generalization and adaptation are principal evolutions. We conclude that a general framework for social coordination dynamics is on the horizon, in which models support experiments with hypothesis generation and mechanistic insights.

Synaptic mechanisms for motor variability in a feedforward network

Behavioral variability often arises from variable activity in the behavior-generating neural network. The synaptic mechanisms underlying this variability are poorly understood. We show that synaptic noise, in conjunction with weak feedforward excitation, generates variable motor output in the Aplysia feeding system. A command-like neuron (CBI-10) triggers rhythmic motor programs more variable than programs triggered by CBI-2. CBI-10 weakly excites a pivotal pattern-generating interneuron (B34) strongly activated by CBI-2. The activation properties of B34 substantially account for the degree of program variability. CBI-10- and CBI-2-induced EPSPs in B34 vary in amplitude across trials, suggesting that there is synaptic noise. Computational studies show that synaptic noise is required for program variability. Further, at network state transition points when synaptic conductance is low, maximum program variability is promoted by moderate noise levels. Thus, synaptic strength and noise act together in a nonlinear manner to determine the degree of variability within a feedforward network.

On the emergence of cognition: from catalytic closure to neuroglial closure

In an analogous manner as occurred during the development of a connected metabolism that at some point reached characteristics associated with what is called "life"-due mainly to a catalytic closure phenomenon when chemicals started to autocatalyze themselves forming a closed web of chemical reactions-it is here proposed that cognition and consciousness (or features associated with them) arose as a consequence of another type of closure within the nervous system, the brain especially. Proper brain function requires an efficient web of connections and once certain complexity is attained due to the number and coordinated activities of the brain cell networks, the emergent properties of cognition and consciousness take place. Seeking to identify main features of the nervous system organization for optimal function, it is here proposed that while catalytic closure yielded life, neuroglial closure produced cognition/consciousness.

Cortical pattern generation during dexterous movement is input-driven

Motor cortex controls skilled arm movement by sending temporal patterns of activity to lower motor centers¹. Local cortical dynamics are thought to shape these patterns throughout movement execution^2–4. External inputs have been implicated in setting the initial state of motor cortex^5,6, but they may also have a pattern-generating role. Here, we dissect the contribution of local dynamics and inputs to cortical pattern generation during a prehension task in mice. Perturbing cortex to an aberrant state prevented movement initiation, but after the perturbation was released, cortex either bypassed the normal initial state and immediately generated the pattern that controls reaching, or it failed to generate this pattern. The difference in these two outcomes was likely due to external inputs. We directly investigated the role of inputs by inactivating thalamus; this perturbed cortical activity and disrupted limb kinematics at any stage of the movement. Activation of thalamocortical axon terminals at different frequencies disrupted cortical activity and arm movement in a graded manner. Simultaneous recordings revealed that both thalamic activity and the current state of cortex predicted changes in cortical activity. Thus, the pattern generator for dexterous arm movement is distributed across multiple, strongly-interacting brain regions.

Effects of supraspinal feedback on human gait: rhythmic auditory distortion

BACKGROUND

Different types of sound cues have been used to adapt the human gait rhythm. We investigated whether young healthy volunteers followed subliminal metronome rhythm changes during gait.

METHODS

Twenty-two healthy adults walked at constant speed on a treadmill following a metronome sound cue (period 566 msec). The metronome rhythm was then either increased or decreased, without informing the subjects, at 1 msec increments or decrements to reach, respectively, a low (596 msec) or a high frequency (536 msec) plateaus. After 30 steps at one of these isochronous conditions, the rhythm returned to the original period with decrements or increments of 1 msec. Motion data were recorded with an optical measurement system to determine footfall. The relative phase between sound cue (stimulus) and foot contact (response) were compared.

RESULTS

Gait was entrained to the rhythmic auditory stimulus and subjects subconsciously adapted the step time and length to maintain treadmill speed, while following the rhythm changes. In most cases there was a lead error: the foot contact occurred before the sound cue. The mean error or the absolute mean relative phase increased during the isochronous high (536 msec) or low frequencies (596 msec).

CONCLUSION

These results showed that the gait period is strongly "entrained" with the first metronome rhythm while subjects still followed metronome changes with larger error. This suggests two processes: one slow-adapting, supraspinal oscillator with persistence that predicts the foot contact to occur ahead of the stimulus, and a second fast process linked to sensory inputs that adapts to the mismatch between peripheral sensory input (foot contact) and supraspinal sensory input (auditory rhythm).

Nested Neuronal Dynamics Orchestrate a Behavioral Hierarchy across Timescales

Classical and modern ethological studies suggest that animal behavior is organized hierarchically across timescales, such that longer-timescale behaviors are composed of specific shorter-timescale actions. Despite progress relating neuronal dynamics to single-timescale behavior, it remains unclear how different timescale dynamics interact to give rise to such higher-order behavioral organization. Here, we show, in the nematode Caenorhabditis elegans, that a behavioral hierarchy spanning three timescales is implemented by nested neuronal dynamics. At the uppermost hierarchical level, slow neuronal population dynamics spanning brain and motor periphery control two faster motor neuron oscillations, toggling them between different activity states and functional roles. At lower hierarchical levels, these faster oscillations are further nested in a manner that enables flexible behavioral control in an otherwise rigid hierarchical framework. Our findings establish nested neuronal activity patterns as a repeated dynamical motif of the C. elegans nervous system, which together implement a controllable hierarchical organization of behavior.

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Slow dynamics across brain and motor circuits drive upper-hierarchy motor states

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Fast dynamics in motor circuits drive lower-hierarchy movements within these states

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Slower dynamics tightly constrain the state and function of faster ones

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This rigid hierarchy nevertheless enables flexible behavioral control

Expanding our horizons: central pattern generation in the context of complex activity sequences

Central pattern generators (CPGs) are central nervous system (CNS) networks that can generate coordinated output in the absence of patterned sensory input. For decades, this concept was applied almost exclusively to simple, innate, rhythmic movements with essentially identical cycles that repeat continually (e.g. respiration) or episodically (e.g. locomotion). But many natural movement sequences are not simple rhythms, as they include different elements in a complex order, and some involve learning. The concepts and experimental approaches of CPG research have also been applied to the neural control of complex movement sequences, such as birdsong, though this is not widely appreciated. Experimental approaches to the investigation of CPG networks, both for simple rhythms and for complex activity sequences, have shown that: (1) brief activation of the CPG elicits a long-lasting naturalistic activity sequence; (2) electrical stimulation of CPG elements alters the timing of subsequent cycles or sequence elements; and (3) warming or cooling CPG elements respectively speeds up or slows down the rhythm or sequence rate. The CPG concept has also been applied to the activity rhythms of populations of mammalian cortical neurons. CPG concepts and methods might further be applied to a variety of fixed action patterns typically used in courtship, rivalry, nest building and prey capture. These complex movements could be generated by CPGs within CPGs (‘nested’ CPGs). Stereotypical, non-motor, non-rhythmic neuronal activity sequences may also be generated by CPGs. My goal here is to highlight previous applications of the CPG concept to complex but stereotypical activity sequences and to suggest additional possible applications, which might provoke new hypotheses and experiments.

Mouse corticospinal system comprises different functional neuronal ensembles depending on their hodology

BACKGROUND

Movement performance depends on the synaptic interactions generated by coherent parallel sensorimotor cortical outputs to different downstream targets. The major outputs of the neocortex to subcortical structures are driven by pyramidal tract neurons (PTNs) located in layer 5B. One of the main targets of PTNs is the spinal cord through the corticospinal (CS) system, which is formed by a complex collection of distinct CS circuits. However, little is known about intracortical synaptic interactions that originate CS commands and how different populations of CS neurons are functionally organized. To further understand the functional organization of the CS system, we analyzed the activity of unambiguously identified CS neurons projecting to different zones of the same spinal cord segment using two-photon calcium imaging and retrograde neuronal tracers.

RESULTS

Sensorimotor cortex slices obtained from transgenic mice expressing GCaMP6 funder the Thy1 promoter were used to analyze the spontaneous calcium transients in layer 5 pyramidal neurons. Distinct subgroups of CS neurons projecting to dorsal horn and ventral areas of the same segment show more synchronous activity between them than with other subgroups.

CONCLUSIONS

The results indicate that CS neurons projecting to different spinal cord zones segregated into functional ensembles depending on their hodology, suggesting that a modular organization of CS outputs controls sensorimotor behaviors in a coordinated manner.

Increasing Local Excitability of Brainstem Respiratory Nuclei Reveals a Distributed Network Underlying Respiratory Motor Pattern Formation

The core circuit of the respiratory central pattern generator (rCPG) is located in the ventrolateral medulla, especially in the pre-Bötzinger complex (pre-BötC) and the neighboring Bötzinger complex (BötC). To test the hypothesis that this core circuit is embedded within an anatomically distributed pattern-generating network, we investigated whether local disinhibition of the nucleus tractus solitarius (NTS), the Kölliker-Fuse nuclei (KFn), or the midbrain periaqueductal gray area (PAG) can similarly affect the respiratory pattern compared to disinhibition of the pre-BötC/BötC core. In arterially-perfused brainstem preparations of rats, we recorded the three-phase respiratory pattern (inspiration, post-inspiration and late-expiration) from phrenic and vagal nerves before and after bilateral microinjections of the GABA(A)R antagonist bicuculline (50 nl, 10 mM). Local disinhibition of either NTS, pre-BötC/BötC, or KFn, but not PAG, triggered qualitatively similar disruptions of the respiratory pattern resulting in a highly significant increase in the variability of the respiratory cycle length, including inspiratory and expiratory phase durations. To quantitatively analyze these motor pattern perturbations, we measured the strength of phase synchronization between phrenic and vagal motor outputs. This analysis showed that local disinhibition of all brainstem target nuclei, but not the midbrain PAG, significantly decreased the strength of phase synchronization. The convergent perturbations of the respiratory pattern suggest that the rCPG expands rostrally and dorsally from the designated core but does not include higher mid-brain structures. Our data also suggest that excitation-inhibition balance of respiratory network synaptic interactions critically determines the network dynamics that underlie vital respiratory rhythm and pattern formation.

Sensorimotor Integration for Decision Making: How the Worm Steers

Animals' movements actively shape their perception and subsequent decision making. In this issue of Neuron, Liu et al. (2018) show how C. elegans nematodes steer toward an odorant: a dedicated interneuron class integrates oscillatory olfactory signals, generated by head swings, with corollary discharge motor signals.

Neuromodulation of central pattern generators and its role in the functional recovery of central pattern generator activity

Neuromodulators play an important role in how the nervous system organizes activity that results in behavior. Disruption of the normal patterns of neuromodulatory release or production is known to be related to the onset of severe pathologies such as Parkinson's disease, Rett syndrome, Alzheimer's disease, and affective disorders. Some of these pathologies involve neuronal structures that are called central pattern generators (CPGs), which are involved in the production of rhythmic activities throughout the nervous system. Here I discuss the interplay between CPGs and neuromodulatory activity, with particular emphasis on the potential role of neuromodulators in the recovery of disrupted neuronal activity. I refer to invertebrate and vertebrate model systems and some of the lessons we have learned from research on these systems and propose a few avenues for future research. I make one suggestion that may guide future research in the field: neuromodulators restrict the parameter landscape in which CPG components operate, and the removal of neuromodulators may enable a perturbed CPG in finding a new set of parameter values that can allow it to regain normal function.

Multiscale modeling of the neuromuscular system: Coupling neurophysiology and skeletal muscle mechanics

Mathematical models and computer simulations have the great potential to substantially increase our understanding of the biophysical behavior of the neuromuscular system. This, however, requires detailed multiscale, and multiphysics models. Once validated, such models allow systematic in silico investigations that are not necessarily feasible within experiments and, therefore, have the ability to provide valuable insights into the complex interrelations within the healthy system and for pathological conditions. Most of the existing models focus on individual parts of the neuromuscular system and do not consider the neuromuscular system as an integrated physiological system. Hence, the aim of this advanced review is to facilitate the prospective development of detailed biophysical models of the entire neuromuscular system. For this purpose, this review is subdivided into three parts. The first part introduces the key anatomical and physiological aspects of the healthy neuromuscular system necessary for modeling the neuromuscular system. The second part provides an overview on state-of-the-art modeling approaches representing all major components of the neuromuscular system on different time and length scales. Within the last part, a specific multiscale neuromuscular system model is introduced. The integrated system model combines existing models of the motor neuron pool, of the sensory system and of a multiscale model describing the mechanical behavior of skeletal muscles. Since many sub-models are based on strictly biophysical modeling approaches, it closely represents the underlying physiological system and thus could be employed as starting point for further improvements and future developments. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models.

Robust dynamical invariants in sequential neural activity

By studying different sources of temporal variability in central pattern generator (CPG) circuits, we unveil fundamental aspects of the instantaneous balance between flexibility and robustness in sequential dynamics -a property that characterizes many systems that display neural rhythms. Our analysis of the triphasic rhythm of the pyloric CPG (Carcinus maenas) shows strong robustness of transient dynamics in keeping not only the activation sequences but also specific cycle-by-cycle temporal relationships in the form of strong linear correlations between pivotal time intervals, i.e. dynamical invariants. The level of variability and coordination was characterized using intrinsic time references and intervals in long recordings of both regular and irregular rhythms. Out of the many possible combinations of time intervals studied, only two cycle-by-cycle dynamical invariants were identified, existing even outside steady states. While executing a neural sequence, dynamical invariants reflect constraints to optimize functionality by shaping the actual intervals in which activity emerges to build the sequence. Our results indicate that such boundaries to the adaptability arise from the interaction between the rich dynamics of neurons and connections. We suggest that invariant temporal sequence relationships could be present in other networks, including those shaping sequences of functional brain rhythms, and underlie rhythm programming and functionality.

Evolutionary aspects of reservoir computing

Reservoir computing (RC) is a powerful computational paradigm that allows high versatility with cheap learning. While other artificial intelligence approaches need exhaustive resources to specify their inner workings, RC is based on a reservoir with highly nonlinear dynamics that does not require a fine tuning of its parts. These dynamics project input signals into high-dimensional spaces, where training linear readouts to extract input features is vastly simplified. Thus, inexpensive learning provides very powerful tools for decision-making, controlling dynamical systems, classification, etc. RC also facilitates solving multiple tasks in parallel, resulting in a high throughput. Existing literature focuses on applications in artificial intelligence and neuroscience. We review this literature from an evolutionary perspective. RC's versatility makes it a great candidate to solve outstanding problems in biology, which raises relevant questions. Is RC as abundant in nature as its advantages should imply? Has it evolved? Once evolved, can it be easily sustained? Under what circumstances? (In other words, is RC an evolutionarily stable computing paradigm?) To tackle these issues, we introduce a conceptual morphospace that would map computational selective pressures that could select for or against RC and other computing paradigms. This guides a speculative discussion about the questions above and allows us to propose a solid research line that brings together computation and evolution with RC as test model of the proposed hypotheses. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'.

Visual cortex neurons phase-lock selectively to subsets of LFP oscillations

It is generally thought that apart from receptive field differences, such as preferred orientation and spatial frequency selectivity, primary visual cortex neurons are functionally similar to each other. However, the genetic diversity of cortical neurons plus the existence of inputs additional to those required to explain known receptive field properties might suggest otherwise. Here we report the existence of desynchronized states in anesthetized cat area 17 lasting up to 45 min, characterized by variable narrow-band local field potential (LFP) oscillations in the range 2-100 Hz and the absence of a synchronized 1/f frequency spectrum. During these periods, spontaneously active neurons phase-locked to variable subsets of LFP oscillations. Individual neurons often ignored frequencies that others phase-locked to. We suggest that these desynchronized periods may correspond to REM sleep-like episodes occurring under anesthesia. Frequency-selective codes may be used for signaling during these periods. Hence frequency-selective combination and frequency-labeled pathways may represent a previously unsuspected dimension of cortical organization. NEW & NOTEWORTHY We investigated spontaneous neuronal firing during periods of desynchronized local field potential (LFP) activity, resembling REM sleep, in anesthetized cats. During these periods, neurons synchronized their spikes to specific phases of multiple LFP frequency components, with some neurons ignoring frequencies that others were synchronized to. Some neurons fired at phase alignments of frequency pairs, thereby acting as phase coincidence detectors. These results suggest that internal brain signaling may use frequency combination codes to generate temporally structured spike trains.