Gain modulation and odor concentration invariance in early olfactory networks.

The broad receptive field of the olfactory receptors constitutes the basis of a combinatorial code that allows animals to detect and discriminate many more odorants than the actual number of receptor types that they express. One drawback is that high odor concentrations recruit lower affinity receptors which can lead to the perception of qualitatively different odors. Here we addressed the contribution that signal-processing in the antennal lobe makes to reduce concentration dependence in odor representation. By means of calcium imaging and pharmacological approach we describe the contribution that GABA receptors play in terms of the amplitude and temporal profiles of the signals that convey odor information from the antennal lobes to higher brain centers. We found that GABA reduces the amplitude of odor elicited signals and the number of glomeruli that are recruited in an odor-concentration-dependent manner. Blocking GABA receptors decreases the correlation among glomerular activity patterns elicited by different concentrations of the same odor. In addition, we built a realistic mathematical model of the antennal lobe that was used to test the viability of the proposed mechanisms and to evaluate the processing properties of the AL network under conditions that cannot be achieved in physiology experiments. Interestingly, even though based on a rather simple topology and cell interactions solely mediated by GABAergic lateral inhibitions, the AL model reproduced key features of the AL response upon different odor concentrations and provides plausible solutions for concentration invariant recognition of odors by artificial sensors.

FBB: a fast Bayesian-bound tool to calibrate RNA-seq aligners.

MOTIVATION: Despite RNA-seq reads provide quality scores that represent the probability of calling a correct base, these values are not probabilistically integrated in most alignment algorithms. Based on the quality scores of the reads, we propose to calculate a lower bound of the probability of alignment of any fast alignment algorithm that generates SAM files. This bound is called Fast Bayesian Bound (FBB) and serves as a canonical reference to compare alignment results across different algorithms. This Bayesian Bound intends to provide additional support to the current state-of-the-art aligners, not to replace them. RESULTS: We propose a feasible Bayesian bound that uses quality scores of the reads to align them to a genome of reference. Two theorems are provided to efficiently calculate the Bayesian bound that under some conditions becomes the equality. The algorithm reads the SAM files generated by the alignment algorithms using multiple command option values. The program options are mapped into the FBB reference values, and all the aligners can be compared respect to the same accuracy values provided by the FBB. Stranded paired read RNA-seq data was used for evaluation purposes. The errors of the alignments can be calculated based on the information contained in the distance between the pairs given by Theorem 2, and the alignments to the incorrect strand. Most of the algorithms (Bowtie, Bowtie 2, SHRiMP2, Soap 2, Novoalign) provide similar results with subtle variations. AVAILABILITY AND IMPLEMENTATION: Current version of the FBB software is provided at https://bitbucket.org/irenerodriguez/fbb CONTACT: rhuerta@ucsd.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Frequency-dependent regulation of intrinsic excitability by voltage-activated membrane conductances, computational modeling and dynamic clamp.

As one of the most unique properties of nerve cells, their intrinsic excitability allows them to transform synaptic inputs into action potentials. This process reflects a complex interplay between the synaptic inputs and the voltage-dependent membrane currents of the postsynaptic neuron. While neurons in natural conditions mostly fire under the action of intense synaptic bombardment and receive fluctuating patterns of excitation and inhibition, conventional techniques to characterize intrinsic excitability mainly utilize static means of stimulation. Recently, we have shown that voltage-gated membrane currents regulate the firing responses under current step stimulation and under physiologically more realistic inputs in a differential manner. At the same time, a multitude of neuron types have been shown to exhibit some form of subthreshold resonance that potentially allows them to respond to synaptic inputs in a frequency-selective manner. In this study, we performed virtual experiments in computational models of neurons to examine how specific voltage-gated currents regulate their excitability under simulated frequency-modulated synaptic inputs. The model simulations and subsequent dynamic clamp experiments on mouse hippocampal pyramidal neurons revealed that the impact of voltage-gated currents in regulating the firing output is strongly frequency-dependent and mostly affecting the synaptic integration at theta frequencies. Notably, robust frequency-dependent regulation of intrinsic excitability was observed even when conventional analysis of membrane impedance suggested no such tendency. Consequently, plastic or homeostatic regulation of intrinsic membrane properties can tune the frequency selectivity of neuron populations in a way that is not readily expected from subthreshold impedance measurements.

Learning modifies odor mixture processing to improve detection of relevant components.

Honey bees have a rich repertoire of olfactory learning behaviors, and they therefore are an excellent model to study plasticity in olfactory circuits. Recent behavioral, physiological, and molecular evidence suggested that the antennal lobe, the first relay of the olfactory system in insects and analog to the olfactory bulb in vertebrates, is involved in associative and nonassociative olfactory learning. Here we use calcium imaging to reveal how responses across antennal lobe projection neurons change after association of an input odor with appetitive reinforcement. After appetitive conditioning to 1-hexanol, the representation of an odor mixture containing 1-hexanol becomes more similar to this odor and less similar to the background odor acetophenone. We then apply computational modeling to investigate how changes in synaptic connectivity can account for the observed plasticity. Our study suggests that experience-dependent modulation of inhibitory interactions in the antennal lobe aids perception of salient odor components mixed with behaviorally irrelevant background odors.

Differential effects of static and dynamic inputs on neuronal excitability.

The intrinsic excitability of neurons is known to be dynamically regulated by activity-dependent plasticity and homeostatic mechanisms. Such processes are commonly analyzed in the context of input-output functions that describe how neurons fire in response to constant levels of current. However, it is not well understood how changes of excitability as observed under static inputs translate to the function of the same neurons in their natural synaptic environment. Here we performed a computational study and hybrid experiments on rat bed nucleus of stria terminalis neurons to compare the two scenarios. The inward rectifying Kir current (IKir) and the hyperpolarization-activated cation current (Ih) were found to be considerably more effective in regulating the firing under synaptic inputs than under static stimuli. This prediction was experimentally confirmed by dynamic-clamp insertion of a synthetic inwardly rectifying Kir current into the biological neurons. At the same time, ionic currents that activate with depolarization were more effective regulating the firing under static inputs. When two intrinsic currents are concurrently altered such as those under homeostatic regulation, the effects in firing responses under static vs. dynamic inputs can be even more contrasting. Our results show that plastic or homeostatic changes of intrinsic membrane currents can shape the current step responses of neurons and their firing under synaptic inputs in a differential manner.

Mechanism for Hypocretin-mediated sleep-to-wake transitions.

Current models of sleep/wake regulation posit that Hypocretin (Hcrt)-expressing neurons in the lateral hypothalamus promote and stabilize wakefulness by projecting to subcortical arousal centers. However, the critical downstream effectors of Hcrt neurons are unknown. Here we use optogenetic, pharmacological, and computational tools to investigate the functional connectivity between Hcrt neurons and downstream noradrenergic neurons in the locus coeruleus (LC) during nonrapid eye movement (NREM) sleep. We found that photoinhibiting LC neurons during Hcrt stimulation blocked Hcrt-mediated sleep-to-wake transitions. In contrast, when LC neurons were optically stimulated to increase membrane excitability, concomitant photostimulation of Hcrt neurons significantly increased the probability of sleep-to-wake transitions compared with Hcrt stimulation alone. We also built a conductance-based computational model of Hcrt-LC circuitry that recapitulates our behavioral results using LC neurons as the main effectors of Hcrt signaling. These results establish the Hcrt-LC connection as a critical integrator-effector circuit that regulates NREM sleep/wake behavior during the inactive period. This coupling of distinct neuronal systems can be generalized to other hypothalamic integrator nuclei with downstream effector/output populations in the brain.

A computational framework for understanding decision making through integration of basic learning rules.

Nonassociative and associative learning rules simultaneously modify neural circuits. However, it remains unclear how these forms of plasticity interact to produce conditioned responses. Here we integrate nonassociative and associative conditioning within a uniform model of olfactory learning in the honeybee. Honeybees show a fairly abrupt increase in response after a number of conditioning trials. The occurrence of this abrupt change takes many more trials after exposure to nonassociative trials than just using associative conditioning. We found that the interaction of unsupervised and supervised learning rules is critical for explaining latent inhibition phenomenon. Associative conditioning combined with the mutual inhibition between the output neurons produces an abrupt increase in performance despite smooth changes of the synaptic weights. The results show that an integrated set of learning rules implemented using fan-out connectivities together with neural inhibition can explain the broad range of experimental data on learning behaviors.

Gain control network conditions in early sensory coding.

Gain control is essential for the proper function of any sensory system. However, the precise mechanisms for achieving effective gain control in the brain are unknown. Based on our understanding of the existence and strength of connections in the insect olfactory system, we analyze the conditions that lead to controlled gain in a randomly connected network of excitatory and inhibitory neurons. We consider two scenarios for the variation of input into the system. In the first case, the intensity of the sensory input controls the input currents to a fixed proportion of neurons of the excitatory and inhibitory populations. In the second case, increasing intensity of the sensory stimulus will both, recruit an increasing number of neurons that receive input and change the input current that they receive. Using a mean field approximation for the network activity we derive relationships between the parameters of the network that ensure that the overall level of activity of the excitatory population remains unchanged for increasing intensity of the external stimulation. We find that, first, the main parameters that regulate network gain are the probabilities of connections from the inhibitory population to the excitatory population and of the connections within the inhibitory population. Second, we show that strict gain control is not achievable in a random network in the second case, when the input recruits an increasing number of neurons. Finally, we confirm that the gain control conditions derived from the mean field approximation are valid in simulations of firing rate models and Hodgkin-Huxley conductance based models.

Control of sleep-to-wake transitions via fast aminoacid and slow neuropeptide transmission.

The Locus Coeruleus (LC) modulates cortical, subcortical, cerebellar, brainstem and spinal cord circuits and it expresses receptors for neuromodulators that operate in a time scale of several seconds. Evidences from anatomical, electrophysiological and optogenetic experiments have shown that LC neurons receive input from a group of neurons called Hypocretins (HCRTs) that release a neuropeptide called hypocretin. It is less known how these two groups of neurons can be coregulated using GABAergic neurons. Since the time scales of GABA A inhibition is several orders of magnitude faster than the hypocretin neuropeptide effect, we investigate the limits of circuit activity regulation using a realistic model of neurons. Our investigation shows that GABA A inhibition is insufficient to control the activity levels of the LCs. Despite slower forms of GABA A can in principle work, there is not much plausibility due to the low probability of the presence of slow GABA A and lack of robust stability at the maximum firing frequencies. The best possible control mechanism predicted by our modeling analysis is the presence of inhibitory neuropeptides that exert effects in a similar time scale as the hypocretin/orexin. Although the nature of these inhibitory neuropeptides has not been identified yet, it provides the most efficient mechanism in the modeling analysis. Finally, we present a reduced mean-field model that perfectly captures the dynamics and the phenomena generated by this circuit. This investigation shows that brain communication involving multiple time scales can be better controlled by employing orthogonal mechanisms of neural transmission to decrease interference between cognitive processes and hypothalamic functions.

Chemical discrimination in turbulent gas mixtures with MOX sensors validated by gas chromatography-mass spectrometry.

Chemical detection systems based on chemo-resistive sensors usually include a gas chamber to control the sample air flow and to minimize turbulence. However, such a kind of experimental setup does not reproduce the gas concentration fluctuations observed in natural environments and destroys the spatio-temporal information contained in gas plumes. Aiming at reproducing more realistic environments, we utilize a wind tunnel with two independent gas sources that get naturally mixed along a turbulent flow. For the first time, chemo-resistive gas sensors are exposed to dynamic gas mixtures generated with several concentration levels at the sources. Moreover, the ground truth of gas concentrations at the sensor location was estimated by means of gas chromatography-mass spectrometry. We used a support vector machine as a tool to show that chemo-resistive transduction can be utilized to reliably identify chemical components in dynamic turbulent mixtures, as long as sufficient gas concentration coverage is used. We show that in open sampling systems, training the classifiers only on high concentrations of gases produces less effective classification and that it is important to calibrate the classification method with data at low gas concentrations to achieve optimal performance.

Nonassociative plasticity alters competitive interactions among mixture components in early olfactory processing.

Experience-related plasticity is an essential component of networks involved in early olfactory processing. However, the mechanisms and functions of plasticity in these neural networks are not well understood. We studied nonassociative plasticity by evaluating responses to two pure odors (A and X) and their binary mixture using calcium imaging of odor-elicited activity in output neurons of the honey bee antennal lobe. Unreinforced exposure to A or X produced no change in the neural response elicited by the pure odors. However, exposure to one odor (e.g. A) caused the response to the mixture to become more similar to that of the other component (X). We also show in behavioral analyses that unreinforced exposure to A caused the mixture to become perceptually more similar to X. These results suggest that nonassociative plasticity modifies neural networks in such a way that it affects local competitive interactions among mixture components. We used a computational model to evaluate the most likely targets for modification. Hebbian modification of synapses from inhibitory local interneurons to projection neurons most reliably produced the observed shift in response to the mixture. These results are consistent with a model in which the antennal lobe acts to filter olfactory information according to its relevance for performing a particular task.

Multifrequency interrogation of nanostructured gas sensor arrays: a tool for analyzing response kinetics.

This paper presents a unique perspective on enhancing the physicochemical mechanisms of two distinct highly sensitive nanostructured metal oxide micro hot plate gas sensors by utilizing an innovative multifrequency interrogation method. The two types of sensors evaluated here employ an identical silicon transducer geometry but with a different morphological structure of the sensitive film. While the first sensing film consists of self-ordered tungsten oxide nanodots, limiting the response kinetics of the sensor-chemical species pair only to the reaction phenomena occurring at the sensitive film surface, the second modality is a three-dimensional array of tungsten oxide nanotubes, which in turn involves both the diffusion and adsorption of the gas during its reaction kinetics with the sensitive film itself. By utilizing the proposed multifrequency interrogation methodology, we demonstrate that the optimal temperature modulation frequencies employed for the nanotubes-based sensors to selectively detect hydrogen, carbon monoxide, ethanol, and dimethyl methyl phosphonate (DMMP) are significantly higher than those utilized for the nanodot-based sensors. This finding helps understand better the amelioration in selectivity that temperature modulation of metal oxides brings about, and, most importantly, it sets the grounds for the nanoengineering of gas-sensitive films to better exploit their practical usage.

Inhibition in multiclass classification.

The role of inhibition is investigated in a multiclass support vector machine formalism inspired by the brain structure of insects. The so-called mushroom bodies have a set of output neurons, or classification functions, that compete with each other to encode a particular input. Strongly active output neurons depress or inhibit the remaining outputs without knowing which is correct or incorrect. Accordingly, we propose to use a classification function that embodies unselective inhibition and train it in the large margin classifier framework. Inhibition leads to more robust classifiers in the sense that they perform better on larger areas of appropriate hyperparameters when assessed with leave-one-out strategies. We also show that the classifier with inhibition is a tight bound to probabilistic exponential models and is Bayes consistent for 3-class problems. These properties make this approach useful for data sets with a limited number of labeled examples. For larger data sets, there is no significant comparative advantage to other multiclass SVM approaches.

Transient cognitive dynamics, metastability, and decision making.

The idea that cognitive activity can be understood using nonlinear dynamics has been intensively discussed at length for the last 15 years. One of the popular points of view is that metastable states play a key role in the execution of cognitive functions. Experimental and modeling studies suggest that most of these functions are the result of transient activity of large-scale brain networks in the presence of noise. Such transients may consist of a sequential switching between different metastable cognitive states. The main problem faced when using dynamical theory to describe transient cognitive processes is the fundamental contradiction between reproducibility and flexibility of transient behavior. In this paper, we propose a theoretical description of transient cognitive dynamics based on the interaction of functionally dependent metastable cognitive states. The mathematical image of such transient activity is a stable heteroclinic channel, i.e., a set of trajectories in the vicinity of a heteroclinic skeleton that consists of saddles and unstable separatrices that connect their surroundings. We suggest a basic mathematical model, a strongly dissipative dynamical system, and formulate the conditions for the robustness and reproducibility of cognitive transients that satisfy the competing requirements for stability and flexibility. Based on this approach, we describe here an effective solution for the problem of sequential decision making, represented as a fixed time game: a player takes sequential actions in a changing noisy environment so as to maximize a cumulative reward. As we predict and verify in computer simulations, noise plays an important role in optimizing the gain.

Techniques for temporal detection of neural sensitivity to external stimulation.

We propose a simple measure of neural sensitivity for characterizing stimulus coding. Sensitivity is defined as the fraction of neurons that show positive responses to n stimuli out of a total of N. To determine a positive response, we propose two methods: Fisherian statistical testing and a data-driven Bayesian approach to determine the response probability of a neuron. The latter is non-parametric, data-driven, and captures a lower bound for the probability of neural responses to sensory stimulation. Both methods are compared with a standard test that assumes normal probability distributions. We applied the sensitivity estimation based on the proposed method to experimental data recorded from the mushroom body (MB) of locusts. We show that there is a broad range of sensitivity that the MB response sweeps during odor stimulation. The neurons are initially tuned to specific odors, but tend to demonstrate a generalist behavior towards the end of the stimulus period, meaning that the emphasis shifts from discrimination to feature learning.

Neuronal synchrony: peculiarity and generality.

Synchronization in neuronal systems is a new and intriguing application of dynamical systems theory. Why are neuronal systems different as a subject for synchronization? (1) Neurons in themselves are multidimensional nonlinear systems that are able to exhibit a wide variety of different activity patterns. Their "dynamical repertoire" includes regular or chaotic spiking, regular or chaotic bursting, multistability, and complex transient regimes. (2) Usually, neuronal oscillations are the result of the cooperative activity of many synaptically connected neurons (a neuronal circuit). Thus, it is necessary to consider synchronization between different neuronal circuits as well. (3) The synapses that implement the coupling between neurons are also dynamical elements and their intrinsic dynamics influences the process of synchronization or entrainment significantly. In this review we will focus on four new problems: (i) the synchronization in minimal neuronal networks with plastic synapses (synchronization with activity dependent coupling), (ii) synchronization of bursts that are generated by a group of nonsymmetrically coupled inhibitory neurons (heteroclinic synchronization), (iii) the coordination of activities of two coupled neuronal networks (partial synchronization of small composite structures), and (iv) coarse grained synchronization in larger systems (synchronization on a mesoscopic scale).

Winnerless competition principle and prediction of the transient dynamics in a Lotka-Volterra model.

Predicting the evolution of multispecies ecological systems is an intriguing problem. A sufficiently complex model with the necessary predicting power requires solutions that are structurally stable. Small variations of the system parameters should not qualitatively perturb its solutions. When one is interested in just asymptotic results of evolution (as time goes to infinity), then the problem has a straightforward mathematical image involving simple attractors (fixed points or limit cycles) of a dynamical system. However, for an accurate prediction of evolution, the analysis of transient solutions is critical. In this paper, in the framework of the traditional Lotka-Volterra model (generalized in some sense), we show that the transient solution representing multispecies sequential competition can be reproducible and predictable with high probability.

Task allocation and site fidelity jointly influence foraging regulation in honeybee colonies.

Variation in behaviour among group members often impacts collective outcomes. Individuals may vary both in the task that they perform and in the persistence with which they perform each task. Although both the distribution of individuals among tasks and differences among individuals in behavioural persistence can each impact collective behaviour, we do not know if and how they jointly affect collective outcomes. Here, we use a detailed computational model to examine the joint impact of colony-level distribution among tasks and behavioural persistence of individuals, specifically their fidelity to particular resource sites, on the collective trade-off between exploring for new resources and exploiting familiar ones. We developed an agent-based model of foraging honeybees, parametrized by data from five colonies, in which we simulated scouts, who search the environment for new resources, and individuals who are recruited by the scouts to the newly found resources, i.e. recruits. We varied the persistence of returning to a particular food source of both scouts and recruits and found that, for each value of persistence, there is a different optimal ratio of scouts to recruits that maximizes resource collection by the colony. Furthermore, changes to the persistence of scouts induced opposite effects from changes to the persistence of recruits on the collective foraging of the colony. The proportion of scouts that resulted in the most resources collected by the colony decreased as the persistence of recruits increased. However, this optimal proportion of scouts increased as the persistence of scouts increased. Thus, behavioural persistence and task participation can interact to impact a colony's collective behaviour in orthogonal directions. Our work provides new insights and generates new hypotheses into how variations in behaviour at both the individual and colony levels jointly impact the trade-off between exploring for new resources and exploiting familiar ones.

Chemical gas sensor array dataset.

To address drift in chemical sensing, an extensive dataset was collected over a period of three years. An array of 16 metal-oxide gas sensors was exposed to six different volatile organic compounds at different concentration levels under tightly-controlled operating conditions. Moreover, the generated dataset is suitable to tackle a variety of challenges in chemical sensing such as sensor drift, sensor failure or system calibration. The data is related to "Chemical gas sensor drift compensation using classifier ensembles", by Vergara et al. [1], and "On the calibration of sensor arrays for pattern recognition using the minimal number of experiments", by Rodriguez-Lujan et al. [2] The dataset can be accessed publicly at the UCI repository upon citation of: http://archive.ics.uci.edu/ml/datasets/Gas+Sensor+Array+Drift+Dataset+at+Different+Concentrations.

A Framework for Quantitative Modeling of Neural Circuits Involved in Sleep-to-Wake Transition.

Identifying the neuronal circuits and dynamics of sleep-to-wake transition is essential to understanding brain regulation of behavioral states, including sleep-wake cycles, arousal, and hyperarousal. Recent work by different laboratories has used optogenetics to determine the role of individual neuromodulators in state transitions. The optogenetically driven data do not yet provide a multi-dimensional schematic of the mechanisms underlying changes in vigilance states. This work presents a modeling framework to interpret, assist, and drive research on the sleep-regulatory network. We identify feedback, redundancy, and gating hierarchy as three fundamental aspects of this model. The presented model is expected to expand as additional data on the contribution of each transmitter to a vigilance state becomes available. Incorporation of conductance-based models of neuronal ensembles into this model and existing models of cortical excitability will provide more comprehensive insight into sleep dynamics as well as sleep and arousal-related disorders.