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Individual sensory neurons can be tuned to many stimuli, each driving unique, stimulus-relevant behaviors, and the ability of multimodal nociceptor neurons to discriminate between potentially harmful and innocuous stimuli is broadly important for organismal survival. Moreover, disruptions in the capacity to differentiate between noxious and innocuous stimuli can result in neuropathic pain. Drosophila larval class III (CIII) neurons are peripheral noxious cold nociceptors and innocuous touch mechanosensors; high levels of activation drive cold-evoked contraction (CT) behavior, while low levels of activation result in a suite of touch-associated behaviors. However, it is unknown what molecular factors underlie CIII multimodality. Here, we show that the TMEM16/anoctamins subdued and white walker (wwk; CG15270) are required for cold-evoked CT, but not for touch-associated behavior, indicating a conserved role for anoctamins in nociception. We also evidence that CIII neurons make use of atypical depolarizing chloride currents to encode cold, and that overexpression of ncc69—a fly homologue of NKCC1—results in phenotypes consistent with neuropathic sensitization, including behavioral sensitization and neuronal hyperexcitability, making Drosophila CIII neurons a candidate system for future studies of the basic mechanisms underlying neuropathic pain.

Individual sensory neurons can be tuned to many stimuli, each driving unique, stimulus-relevant behaviors, and the ability of multimodal nociceptor neurons to discriminate between potentially harmful and innocuous stimuli is broadly important for organismal survival. Moreover, disruptions in the capacity to differentiate between noxious and innocuous stimuli can result in neuropathic pain. Drosophila larval Class III (CIII) neurons are peripheral noxious cold nociceptors and gentle touch mechanosensors; high levels of activation drive cold-evoked contraction (CT) behavior, while low levels of activation result in a suite of touch-associated behaviors. However, it is unknown what molecular factors underlie CIII multimodality. Here, we show that the TMEM16/anoctamins subdued and white walker (wwk; CG15270) are required for cold-evoked CT, but not for touch-associated behavior, indicating a conserved role for anoctamins in nociception. We also evidence that CIII neurons make use of atypical depolarizing chloride currents to encode cold, and that overexpression of ncc69—a fly homologue of NKCC1—results in phenotypes consistent with neuropathic sensitization, including behavioral hypersensitization and spontaneous nociceptor activity, making Drosophila CIII neurons a candidate system for future studies of the basic mechanisms underlying neuropathic pain.

Developing spinal motor networks produce a diverse array of outputs, including episodic and continuous patterns of rhythmic activity. Variation in excitability state and neuromodulatory tone can facilitate transitions between episodic and continuous rhythms; however, the intrinsic mechanisms that govern these rhythms and their transitions are poorly understood. Here, we tested the capacity of a single central pattern generator (CPG) circuit with tunable properties to generate multiple outputs. To address this, we deployed a computational model composed of an inhibitory half-center oscillator (HCO). Following predictions of our computational model, we tested the contributions of key properties to the generation of an episodic rhythm produced by isolated spinal cords of the newborn mouse. The model recapitulates the diverse state-dependent rhythms evoked by dopamine. In the model, episodic bursting depended predominantly on the endogenous oscillatory properties of neurons, with Na

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In mammals, the main olfactory bulb (MOB) is driven by air flow respiratory input into the nose. This slow (2-10 Hz) rhythmic input can be observed both at the local field potential (LFP) level, single cell discharge pattern level and intracellular slow oscillations of mitral cells (principal cells of the MOB). Using intracellular recordings in freely breathing anesthetized rats, we examined membrane potential slow oscillations (MPSO) in mitral cells and their relationship with their discharge patterns [1]. Interestingly, we found cells which showed no MSPO at resting membrane potential but had nevertheless a respiratory-synchronous discharge. Changing the excitability level of these cells with hyperpolarizing current injection favored the apparition of an MPSO while decreasing the cell discharge. We called such a dynamic silent MPSO. In this study, we use a modeling approach to show how such a silent MPSO can exist in mitral cells and favor the synchronization of cell discharge to the MOB slow rhythmic input. First, using a simple integrate-and-fire (IF) model, we showed that the presence of strong and balanced excitatory and inhibitory inputs with similar oscillatory dynamics led the model to display a silent MPSO. This silent MPSO is characterized by a flat membrane potential in spite of strong synaptic conductance oscillations. Second, assuming that the slow oscillatory input could be decomposed as a mean input and stochastic fluctuation with oscillating amplitudes, we showed that the model could display a slow rhythmic discharge similar to our experimental observations. However, MOB mitral cells have a complex anatomy with (i) an apical dendrite and a tuft receiving both excitatory nose input and local inhibitory regulation, and (ii) long lateral dendrites which receive a large recurrent inhibitory input from the MOB network. We thus wondered how our previous modeling results could generalize in this case. To this aim, we used a previously developed detailed mitral cell model and showed with numerical simulations that silent MPSO could be generated in this model. However, the precise dynamics of the oscillations depended on the main inhibitory input of the mitral cell. If most of the inhibition comes from the tuft, then the silent MPSO can quite easily be generated in the cell, and partial imprecision in the excitation-inhibition balance can be hindered by the apical dendrite filtering. However if a fraction of the inhibition is coming from the lateral dendrites, the silent MPSOs are more difficult to stabilize and the resulting MPSOs have complex shapes resembling some of our intracellular recordings. Overall, we found that mitral cell silent MPSOs with respiratory-patterned discharge, that we observed experimentally, can indeed be a consequence of the precise balance of their slow oscillatory excitatory and inhibitory inputs.

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While monkeys perform a task alternating between behavioral adaptation --relying on feedback monitoring and memory of previous choices-- and repetition of previous actions, firing rates in dorsal Anterior Cingulate Cortex (dACC) modulate with cognitive control levels [1]. Further, it has been hypothesized that dorsolateral Prefrontal Cortex (dlPFC) could make use of dACC signals to take the adapted decision [2]. However, little is known about whether dACC spike timing may participate to behavioral adaptation signaling, and how multiple unit activities may be jointly decoded by dlPFC. We tested the hypothesis that temporal (or spatiotemporal) pattern matching mediate information transmission by dACC. We used spike-train metrics [3] to decode dACC activity recorded in a problem solving task [1] (336 cells, 2 monkeys). We computed a similarity measure between every spike train and the activity patterns in different task epochs for classification. When computing the similarity, we explored different degrees of (i) timing sensitivity and (ii) distinction between spikes from different neurons, which led to different classification performances. We found that timing sensitivity could improve behavioral adaptation vs. repetition classification of single unit spike trains. Optimal decoding occurred when accounting for spike times at a resolution

For patients with partial epilepsy, one or several brain regions generate seizures ('epileptogenic zone' [1]), which may recruit other regions that are themselves non-epileptogenic. For these patients, the epileptogenic zone can sometimes be surgically removed and thus must be precisely delineated using intracranial EEG, giving us a deeper insight into the spatiotemporal dynamic of the seizure. Intracranial EEG times series show that brain regions and seizures may display a large variability in dynamics: (i) some seizures recruit more brain regions than others, (ii) the delays between the onset of the seizure in the epileptogenic zone and other areas can change on a time scale of several seconds, (iii) recruited areas exhibit different levels of coherence with the epileptogenic zone according to their position. Here we propose a model able to reproduce these 3 features (for other macroscopic models of seizures see [2,3]). Our network model comprises two neural masses autonomously able to produce epileptic seizure. We symmetrically coupled two of these neural mass models on a slow time scale (several seconds) and studied systematically the effect of two parameters : the epileptogenicity of the neural masses and the coupling values between neural masses. By choosing specific values of these parameters, we reproduce each of the features above (see Figure ​Figure1).1). We mathematically reduced the network model to uncover the essential mechanisms underlying a coupling acting on a slow time scale. Figure 1 Simultaneous times series of two coupled neural masses. The neural mass 1 starts first the seizure and then recruits the neural mass 2.

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This article describes a method for building the foundations for an ontology of brain areas, brain circuits and cerebral functions. This ontology challenges the complex pattern of neural connections that underlie cognition and behavior. The purpose of this ontology is to explore the structural foundations of human brain function. The ontology is composed from three main modules. The first one concerns the brain anatomy and more exactly the brain areas (e.g. 'the frontal lobe', 'the inferior frontal gyrus'), the second one concerns the brain circuits (e.g. 'the arcuate fasciculus connecting Wernicke's and Broca's areas') and finally the last one which concerns the cognitive anatomy (e.g. 'the conduction aphasia is interpreted as a disconnection between the temporal and frontal brain language areas'). We built the brain anatomy module (i.e., the brain areas module) using the BrainInfo resource [1] and more exactly NeuroNames, an ontology designed to compensate for ambiguities in neuroanatomical nomenclature. NeuroNames recognizes a very large set of names of neuroanatomical entities and accommodates synonyms and homonyms of the standard terms in many languages. It defines complex structures as models composed of primary structures, which are defined in unambiguous operational terms [2]. NeuroNames is a resource vocabulary of the NLM's Unified Medical Language System (UMLS, 2011) and the basis for the brain regions component of NIFSTD [3]. The brain anatomy module contains 2900 concepts extracted from Neuronames including the primary cerebral structures, superstructures and related superficial features and others ways subdividing the brain. We did not hold for this study, the hierarchical relations of each structure to its superstructures and substructures providing by Neuronames because the links will be established as a connected and more topological way. We are currently building the brain circuits module using the Atlas of Human Brain Connections [3] which capitalizes on diffusion MRI tractography methods to provide a comprehensive overview of connections derived from virtual in vivo tractography dissections of the human brain. This atlas details descriptions of the major white matter connections, their function, and associated clinical syndromes. We will build the function module as a summary and a anatomo-functional synthesis amongst the previous modules. We will retain two entries: the normal functioning (e.g., the language function) and the pathological entry entry (e.g., the aphasia). This issue will consist to associate a function (and dysfunction) to a specific circuit and linked areas using authoritative neuroscience textbooks (e.g. Geschwind attributed the arcuate fasciculus the main role in speech repetition disturbances and resulting in the so-called Wernicke-Geschwind model of language). We maintain two manifestations of the ontology. The first manifestation is specified in the formal Web Ontology Language OWL. The second one is specified in a graph format. To visualize the graphs we use Gephi [4], which is an interactive visualization and exploration platform for all kind of networks and complex systems, dynamic and hierarchical graphs.

The study of decision-making between goal directed actions with rodents has been often based on experimental tasks in which animals were trained to perform specific sequences of actions, such as lever presses or nose pokes [4], to attain reward. This supported the hypothesis of reinforcement learning as the underlying mechanism to acquire those behavioural sequences, putatively implemented by the basal-ganglia circuitry [1,3]. However, experimental evidence suggests that whenever we extend the complexity of the motor responses towards timely constrained behaviour, it starts reflecting an influence of costs related not only to reward, but rather a compromise between the motor factors relevant to the task, and the timely requirements to attain the goal [6]. To investigate this further, we took advantage of new behavioral protocol in which rats running on a treadmill need to estimate a fixed-temporal interval to obtain a reward [5]. Interestingly rats became proficients in this task by developping very stereotyped running trajectories. The establishment of these precise running kinematics occured progressively in a trial-and-error process that lasted between 2 to 3 months. At this point if we shortened the treadmill length, animals persisted in reproducing the previously learned kinematics even if doing so they stopped receiving reward. This is consistent with that these stereotyped running kinematics are motor habit [8]. To provide a theoretical backend for these results, we developed a model-free reinforcement learning model [7]. We excluded model-based algorithms because of the inability of the rats to exploit the previously learned behavior to accelerate their learning rate when the task changes. The specificity of this model is to count reward delivery as positive reward, but also efforts generated at each time step as negative rewards. The problem is thus a speed-accuracy trade-off process: the goal of the model is to generate the motor sequence that optimizes the ratio discounted reward/effort. The main result shows that, as long as the local time and speed are included into the characterization of the kinematic state, the model can replicate the same motor sequences. This suggests that these two pieces information are required to learn time-constrained motor sequences, and predicts that if a brain structure indeed learns these habitual sequences as the model does (our suggestion would be the sensorimotor circuits of the basal ganglia [2]), it should exhibit correlates with the same variables during the entire sequence.

Noise driven exploration of a brain network’s dynamic repertoire has been hypothesized to be causally involved in cognitive function, aging and neurodegeneration. The dynamic repertoire crucially depends on the network’s capacity to store patterns, as well as their stability. Here we systematically explore the capacity of networks derived from human connectomes to store attractor states, as well as various network mechanisms to control the brain’s dynamic repertoire. Using a deterministic graded response Hopfield model with connectome-based interactions, we reconstruct the system’s attractor space through a uniform sampling of the initial conditions. Large fixed-point attractor sets are obtained in the low temperature condition, with a bigger number of attractors than ever reported so far. Different variants of the initial model, including (i) a uniform activation threshold or (ii) a global negative feedback, produce a similarly robust multistability in a limited parameter range. A numerical analysis of the distribution of the attractors identifies spatially-segregated components, with a centro-medial core and several well-delineated regional patches. Those different modes share similarity with the fMRI independent components observed in the “resting state” condition. We demonstrate non-stationary behavior in noise-driven generalizations of the models, with different meta-stable attractors visited along the same time course. Only the model with a global dynamic density control is found to display robust and long-lasting non-stationarity with no tendency toward either overactivity or extinction. The best fit with empirical signals is observed at the edge of multistability, a parameter region that also corresponds to the highest entropy of the attractors., Author Summary Recent developments in non-invasive brain imaging allow reconstructing axonal tracts in the human brain and building realistic network models of the human brain. These models resemble brain systems in their network character and allow deciphering how different regions share signals and process information. Inspired by the metastable dynamics of the spin glass model in statistical physics, we systematically explore the brain network’s capacity to process information and investigate novel avenues how to enhance it. In particular, we study how the brain activates and switches between different functional networks across time. Such non-stationary behavior has been observed in human brain imaging data and hypothesized to be linked to information processsing. To shed light on the conditions under which large-scale brain network models exhibit such dynamics, we characterize the principal network patterns and confront them with modular structures observed both in graph theoretical analysis and resting-state functional Magnetic Resonance Imaging (rs-fMRI).

The optical transparency and the small dimensions of zebrafish at the larval stage make it a vertebrate model of choice for brain-wide in-vivo functional imaging. However, current point-scanning imaging techniques, such as two-photon or confocal microscopy, impose a strong limit on acquisition speed which in turn sets the number of neurons that can be simultaneously recorded [1]. At 5 Hz, this number is of the order of one thousand, i.e. approximately 1-2% of the brain. We demonstrate that this limitation can be greatly overcome by using Selective-Plane Illumination Microscopy (SPIM) [2-4]. Zebrafish larvae expressing the genetically encoded calcium indicator GCaMP3 were illuminated with a scanned laser sheet and imaged with a camera whose optical axis was oriented orthogonally to the illumination plane. This optical sectioning approach was shown to permit functional imaging of most of the brain volume of 5-9 day old larvae with single-cell resolution. The spontaneous activity of up to 5000 neurons was recorded at 20 Hz for 20-60 min. By rapidly scanning the specimen in the axial direction, the activity of 25000 individual neurons from 5 different z-planes (approximately 30% of the entire brain) could be simultaneously monitored at 4 Hz. Compared to point-scanning techniques, this imaging strategy thus yields a ~20-fold increase in data throughput (number of recorded neurons times acquisition rate) without compromising the signal-to-noise ratio. The extended field of view offered by the SPIM method allowed us to directly identify large scale ensembles of neurons, spanning several brain regions (see Figure ​Figure1),1), that displayed correlated activity and were thus likely to participate in common neural processes. Figure 1 Image of the brain of a 6 day-old GCaMP3 zebrafish obtained by SPIM. Colored neurons indicate a set of neurons showing correlated activity.

Over the past years the ongoing human brain activity at rest came into focus, emphasizing the role of the rest-state activity for brain functions such as planning and perception in both healthy and diseased brains. For instance, it has been shown that the alpha rhythm during rest can be affected (e.g., resetting, entrainment) using stimulations such as sensory or Transcranial Magnetic Stimulations (TMS). However, the origin and the mechanisms underlying the rest-state activity are not yet well understood. In this study, we focus on the propagation of largescale brain responses to stimulations such as TMS to identify sub-networks involved in the rest-state. Using The Virtual Brain [1] we model the dynamics of the human cortex as a network of 16,384 neural masses (NMs), each representing nearly 16 mm of the cortical surface. A sub-threshold Hopf oscillator with a Van der Pol term describes the temporal behavior of each NM and a Gaussian kernel defines the spatial interactions among the NMs. We also consider the connections through the white matter extracted from a combination of diffusion spectrum MRI tractography and the CoCoMac database. We systematically stimulate different brain areas and analyze the spatiotemporal responses of the model, using Principal Component Analysis. The results provide evidence for the existence of a low dimensional set of networks during rest. Stimulations of brain areas involved in resting-state networks produce stronger and longer lasting responses than stimulations

Epileptic seizures are characterized dynamically on multiple scales in space and time. Understanding the relations between components within and across those scales is among the fundamental goals of computational neuroscience [1,2], together with the analysis of the dynamical repertoire of such a neural system [3,4]. Previous research in this field has developed mathematical models that can reproduce seizure dynamics as either autonomous or driven processes [5]. In this study, we start from a phenomenological model displaying features of electrical epileptic neural activity, and we move towards a more biologically realistic network of neurons, while keeping track of the dynamical repertoire exhibited by the system [6]. Our system is composed of two neuronal populations, characterized by fast excitation and slow inhibition. The first one is modeled by 3-dimensional Hindmarsh-Rose oscillators, while the other is modeled by 2-dimensional Morris-Lecar oscillators. Both pools of fast and slow dynamics do not require manual drive in order to exhibit a repertoire of regimes that are commonly encountered in epileptic brain activity. We systematically run numerical parameter space exploration at each scale before going down to a lower scale with increased complexity. Through this framework, we investigate plausible biological causation of epileptic dynamics such as electrical and chemical synaptic connectivity, excitation/inhibition ratios, and combine them to experimental data recorded from rodent hippocampus. This multiscale architecture allows to (i) perform automatic dynamical analysis of a particular neural system, (ii) characterize the level of biological accuracy required to portray epi-phenomena, and (iii) design new experiments to comprehend seizure mechanisms. We discuss the validity of our results with regard to both biologically and computationally oriented investigations that have been performed on the topic to provide a line of thoughts for future research.