Modulation of neuronal dynamics by sustained and activity-dependent continuous-wave near-infrared laser stimulation.

Significance: Near-infrared laser illumination is a non-invasive alternative/complement to classical stimulation methods in neuroscience but the mechanisms underlying its action on neuronal dynamics remain unclear. Most studies deal with high-frequency pulsed protocols and stationary characterizations disregarding the dynamic modulatory effect of sustained and activity-dependent stimulation. The understanding of such modulation and its widespread dissemination can help to develop specific interventions for research applications and treatments for neural disorders. Aim: We quantified the effect of continuous-wave near-infrared (CW-NIR) laser illumination on single neuron dynamics using sustained stimulation and an open-source activity-dependent protocol to identify the biophysical mechanisms underlying this modulation and its time course. Approach: We characterized the effect by simultaneously performing long intracellular recordings of membrane potential while delivering sustained and closed-loop CW-NIR laser stimulation. We used waveform metrics and conductance-based models to assess the role of specific biophysical candidates on the modulation. Results: We show that CW-NIR sustained illumination asymmetrically accelerates action potential dynamics and the spiking rate on single neurons, while closed-loop stimulation unveils its action at different phases of the neuron dynamics. Our model study points out the action of CW-NIR on specific ionic-channels and the key role of temperature on channel properties to explain the modulatory effect. Conclusions: Both sustained and activity-dependent CW-NIR stimulation effectively modulate neuronal dynamics by a combination of biophysical mechanisms. Our open-source protocols can help to disseminate this non-invasive optical stimulation in novel research and clinical applications.

Automatized offline and online exploration to achieve a target dynamics in biohybrid neural circuits built with living and model neurons.

Biohybrid circuits of interacting living and model neurons are an advantageous means to study neural dynamics and to assess the role of specific neuron and network properties in the nervous system. Hybrid networks are also a necessary step to build effective artificial intelligence and brain hybridization. In this work, we deal with the automatized online and offline adaptation, exploration and parameter mapping to achieve a target dynamics in hybrid circuits and, in particular, those that yield dynamical invariants between living and model neurons. We address dynamical invariants that form robust cycle-by-cycle relationships between the intervals that build neural sequences from such interaction. Our methodology first attains automated adaptation of model neurons to work in the same amplitude regime and time scale of living neurons. Then, we address the automatized exploration and mapping of the synapse parameter space that lead to a specific dynamical invariant target. Our approach uses multiple configurations and parallel computing from electrophysiological recordings of living neurons to build full mappings, and genetic algorithms to achieve an instance of the target dynamics for the hybrid circuit in a short time. We illustrate and validate such strategy in the context of the study of functional sequences in neural rhythms, which can be easily generalized for any variety of hybrid circuit configuration. This approach facilitates both the building of hybrid circuits and the accomplishment of their scientific goal.

Modeling the Sequential Pattern Variability of the Electromotor Command System of Pulse Electric Fish.

Mormyridae, a family of weakly electric fish, use electric pulses for communication and for extracting information from the environment (active electroreception). The electromotor system controls the timing of pulse generation. Ethological studies have described several sequences of pulse intervals (SPIs) related to distinct behaviors (e.g., mating or exploratory behaviors). Accelerations, scallops, rasps, and cessations are four different SPI patterns reported in these fish, each showing characteristic stereotyped temporal structures. This article presents a computational model of the electromotor command circuit that reproduces a whole set of SPI patterns while keeping the same internal network configuration. The topology of the model is based on a simplified representation of the network with four neuron clusters (nuclei). An initial configuration was built to reproduce nucleus characteristics and network topology as described by detailed morphological and electrophysiological studies. Then, a methodology based on a genetic algorithm (GA) was developed and applied to tune the model connectivity parameters to automatically reproduce a whole set of patterns recorded from freely-behaving Gnathonemus petersii specimens. Robustness analyses of input variability were performed to discard overfitting and assess validity. Results show that the set of SPI patterns is consistently reproduced reaching a dynamic balance between synaptic properties in the network. This model can be used as a tool to test novel hypotheses regarding temporal structure in electrogeneration. Beyond the electromotor model itself, the proposed methodology can be adapted to fit models of other biological networks that also exhibit sequential patterns.

Intrinsic and environmental factors modulating autonomous robotic search under high uncertainty.

Autonomous robotic search problems deal with different levels of uncertainty. When uncertainty is low, deterministic strategies employing available knowledge result in most effective searches. However, there are domains where uncertainty is always high since information about robot location, environment boundaries or precise reference points is unattainable, e.g., in cave, deep ocean, planetary exploration, or upon sensor or communications impairment. Furthermore, latency regarding when search targets move, appear or disappear add to uncertainty sources. Here we study intrinsic and environmental factors that affect low-informed robotic search based on diffusive Brownian, naive ballistic, and superdiffusive strategies (Lévy walks), and in particular, the effectiveness of their random exploration. Representative strategies were evaluated considering both intrinsic (motion drift, energy or memory limitations) and extrinsic factors (obstacles and search boundaries). Our results point towards minimum-knowledge based modulation approaches that can adjust distinct spatial and temporal aspects of random exploration to lead to effective autonomous search under uncertainty.

Automatic Adaptation of Model Neurons and Connections to Build Hybrid Circuits with Living Networks.

Hybrid circuits built by creating mono- or bi-directional interactions among living cells and model neurons and synapses are an effective way to study neuron, synaptic and neural network dynamics. However, hybrid circuit technology has been largely underused in the context of neuroscience studies mainly because of the inherent difficulty in implementing and tuning this type of interactions. In this paper, we present a set of algorithms for the automatic adaptation of model neurons and connections in the creation of hybrid circuits with living neural networks. The algorithms perform model time and amplitude scaling, real-time drift adaptation, goal-driven synaptic and model tuning/calibration and also automatic parameter mapping. These algorithms have been implemented in RTHybrid, an open-source library that works with hard real-time constraints. We provide validation examples by building hybrid circuits in a central pattern generator. The results of the validation experiments show that the proposed dynamic adaptation facilitates closed-loop communication among living and artificial model neurons and connections, and contributes to characterize system dynamics, achieve control, automate experimental protocols and extend the lifespan of the preparations.

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.

Algorithmic clustering based on string compression to extract P300 structure in EEG signals.

BACKGROUND AND OBJECTIVES: P300 is an Event Related Potential control signal widely used in Brain Computer Interfaces. Using the oddball paradigm, a P300 speller allows a human to spell letters through P300 events produced by his/her brain. One of the most common issues in the detection of this event is that its structure may differ between different subjects and over time for a specific subject. The main purpose of this work is to deal with this inherent variability and identify the main structure of P300 using algorithmic clustering based on string compression. METHODS: In this work, we make use of the Normalized Compression Distance (NCD) to extract the main structure of the signal regardless of its inherent variability. In order to apply compression distances, we carry out a novel signal-to-ASCII process that transforms and merges different events into suitable objects to be used by a compression algorithm. Once the ASCII objects are created, we use NCD-driven clustering as a tool to analyze if our object creation method suitably represents the information contained in the signals and to explore if compression distances are a valid tool for identifying P300 structure. With the purpose of increasing the level of generalization of our study, we apply two different clustering methods: a hierarchical clustering algorithm based on the minimum quartet tree method and a multidimensional projection method. RESULTS: Our experimental results show good clustering performance over different experiments, showing the structure extraction capabilities of our procedure. Two datasets with recordings in different scenarios were used to analyze the problem and validate our results, respectively. It has to be pointed out that when the clustering performance over individual electrodes is analyzed, higher P300 activity is found in similar regions to other articles using the same datasets. This suggests that our approach might be used as an electrode-selection criteria. CONCLUSIONS: The proposed NCD-driven clustering methodology can be used to discover the structural characteristics of EEG and thereby, it is suitable as a complementary methodology for the P300 analysis.

RTHybrid: A Standardized and Open-Source Real-Time Software Model Library for Experimental Neuroscience.

Closed-loop technologies provide novel ways of online observation, control and bidirectional interaction with the nervous system, which help to study complex non-linear and partially observable neural dynamics. These protocols are often difficult to implement due to the temporal precision required when interacting with biological components, which in many cases can only be achieved using real-time technology. In this paper we introduce RTHybrid (www.github.com/GNB-UAM/RTHybrid), a free and open-source software that includes a neuron and synapse model library to build hybrid circuits with living neurons in a wide variety of experimental contexts. In an effort to encourage the standardization of real-time software technology in neuroscience research, we compared different open-source real-time operating system patches, RTAI, Xenomai 3 and Preempt-RT, according to their performance and usability. RTHybrid has been developed to run over Linux operating systems supporting both Xenomai 3 and Preempt-RT real-time patches, and thus allowing an easy implementation in any laboratory. We report a set of validation tests and latency benchmarks for the construction of hybrid circuits using this library. With this work we want to promote the dissemination of standardized, user-friendly and open-source software tools developed for open- and closed-loop experimental neuroscience.

Delay-Dependent Response in Weakly Electric Fish under Closed-Loop Pulse Stimulation.

In this paper, we apply a real time activity-dependent protocol to study how freely swimming weakly electric fish produce and process the timing of their own electric signals. Specifically, we address this study in the elephant fish, Gnathonemus petersii, an animal that uses weak discharges to locate obstacles or food while navigating, as well as for electro-communication with conspecifics. To investigate how the inter pulse intervals vary in response to external stimuli, we compare the response to a simple closed-loop stimulation protocol and the signals generated without electrical stimulation. The activity-dependent stimulation protocol explores different stimulus delivery delays relative to the fish's own electric discharges. We show that there is a critical time delay in this closed-loop interaction, as the largest changes in inter pulse intervals occur when the stimulation delay is below 100 ms. We also discuss the implications of these findings in the context of information processing in weakly electric fish.

Retrieval of noisy fingerprint patterns using metric attractor networks.

This work experimentally analyzes the learning and retrieval capabilities of the diluted metric attractor neural network when applied to collections of fingerprint images. The computational cost of the network decreases with the dilution, so we can increase the region of interest to cover almost the complete fingerprint. The network retrieval was successfully tested for different noisy configurations of the fingerprints, and proved to be robust with a large basin of attraction. We showed that network topologies with a 2D-Grid arrangement adapt better to the fingerprints spatial structure, outperforming the typical 1D-Ring configuration. An optimal ratio of local connections to random shortcuts that better represent the intrinsic spatial structure of the fingerprints was found, and its influence on the retrieval quality was characterized in a phase diagram. Since the present model is a set of nonlinear equations, it is possible to go beyond the naïve static solution (consisting in matching two fingerprints using a fixed distance threshold value), and a crossing evolution of similarities was shown, leading to the retrieval of the right fingerprint from an apparently more distant candidate. This feature could be very useful for fingerprint verification to discriminate between fingerprints pairs.

Neural sensitivity to odorants in deprived and normal olfactory bulbs.

Early olfactory deprivation in rodents is accompanied by an homeostatic regulation of the synaptic connectivity in the olfactory bulb (OB). However, its consequences in the neural sensitivity and discrimination have not been elucidated. We compared the odorant sensitivity and discrimination in early sensory deprived and normal OBs in anesthetized rats. We show that the deprived OB exhibits an increased sensitivity to different odorants when compared to the normal OB. Our results indicate that early olfactory stimulation enhances discriminability of the olfactory stimuli. We found that deprived olfactory bulbs adjusts the overall excitatory and inhibitory mitral cells (MCs) responses to odorants but the receptive fields become wider than in the normal olfactory bulbs. Taken together, these results suggest that an early natural sensory stimulation sharpens the receptor fields resulting in a larger discrimination capability. These results are consistent with previous evidence that a varied experience with odorants modulates the OB's synaptic connections and increases MCs selectivity.

Assisted closed-loop optimization of SSVEP-BCI efficiency.

We designed a novel assisted closed-loop optimization protocol to improve the efficiency of brain-computer interfaces (BCI) based on steady state visually evoked potentials (SSVEP). In traditional paradigms, the control over the BCI-performance completely depends on the subjects' ability to learn from the given feedback cues. By contrast, in the proposed protocol both the subject and the machine share information and control over the BCI goal. Generally, the innovative assistance consists in the delivery of online information together with the online adaptation of BCI stimuli properties. In our case, this adaptive optimization process is realized by (1) a closed-loop search for the best set of SSVEP flicker frequencies and (2) feedback of actual SSVEP magnitudes to both the subject and the machine. These closed-loop interactions between subject and machine are evaluated in real-time by continuous measurement of their efficiencies, which are used as online criteria to adapt the BCI control parameters. The proposed protocol aims to compensate for variability in possibly unknown subjects' state and trait dimensions. In a study with N = 18 subjects, we found significant evidence that our protocol outperformed classic SSVEP-BCI control paradigms. Evidence is presented that it takes indeed into account interindividual variabilities: e.g., under the new protocol, baseline resting state EEG measures predict subjects' BCI performances. This paper illustrates the promising potential of assisted closed-loop protocols in BCI systems. Probably their applicability might be expanded to innovative uses, e.g., as possible new diagnostic/therapeutic tools for clinical contexts and as new paradigms for basic research.

Generalization of the dynamic clamp concept in neurophysiology and behavior.

The idea of closed-loop interaction in in vitro and in vivo electrophysiology has been successfully implemented in the dynamic clamp concept strongly impacting the research of membrane and synaptic properties of neurons. In this paper we show that this concept can be easily generalized to build other kinds of closed-loop protocols beyond (or in addition to) electrical stimulation and recording in neurophysiology and behavioral studies for neuroethology. In particular, we illustrate three different examples of goal-driven real-time closed-loop interactions with drug microinjectors, mechanical devices and video event driven stimulation. Modern activity-dependent stimulation protocols can be used to reveal dynamics (otherwise hidden under traditional stimulation techniques), achieve control of natural and pathological states, induce learning, bridge between disparate levels of analysis and for a further automation of experiments. We argue that closed-loop interaction calls for novel real time analysis, prediction and control tools and a new perspective for designing stimulus-response experiments, which can have a large impact in neuroscience research.

The use of artificial neural networks in electrostatic force microscopy.

The use of electrostatic force microscopy (EFM) to characterize and manipulate surfaces at the nanoscale usually faces the problem of dealing with systems where several parameters are not known. Artificial neural networks (ANNs) have demonstrated to be a very useful tool to tackle this type of problems. Here, we show that the use of ANNs allows us to quantitatively estimate magnitudes such as the dielectric constant of thin films. To improve thin film dielectric constant estimations in EFM, we first increase the accuracy of numerical simulations by replacing the standard minimization technique by a method based on ANN learning algorithms. Second, we use the improved numerical results to build a complete training set for a new ANN. The results obtained by the ANN suggest that accurate values for the thin film dielectric constant can only be estimated if the thin film thickness and sample dielectric constant are known.PACS: 07.79.Lh; 07.05.Mh; 61.46.Fg.

Characterization of a clinical olfactory test with an artificial nose.

Clinical olfactory tests are used to address hyposmia/anosmia levels in patients with different types of olfactory impairments. Typically, a given test is employed clinically and then replaced by a new one after a certain period of use which can range from days to several months. There is a need to assess control quality of these tests and also for a procedure to quantify their degradation over time. In this paper we propose a protocol to employ low-cost artificial noses for the quantitative characterization of olfactory tests used in clinical studies. In particular, we discuss a preliminary study on the Connecticut Chemosensorial Clinical Research Center Test kit which shows that some odorants, as sensed by an artificial nose, seem to degrade while others are potentiated as the test ages. We also discuss the need to establish a map of correspondence between human and machine olfaction when artificial noses are used to characterize or compare human smell performance in research and clinical studies.

Structured information in small-world neural networks.

The retrieval abilities of spatially uniform attractor networks can be measured by the global overlap between patterns and neural states. However, we found that nonuniform networks, for instance, small-world networks, can retrieve fragments of patterns (blocks) without performing global retrieval. We propose a way to measure the local retrieval using a parameter that is related to the fluctuation of the block overlaps. Simulation of neural dynamics shows a competition between local and global retrieval. The phase diagram shows a transition from local retrieval to global retrieval when the storage ratio increases and the topology becomes more random. A theoretical approach confirms the simulation results and predicts that the stability of blocks can be improved by dilution.

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.

Real-time control of stepper motors for mechano-sensory stimulation.

Mechanical stimulation is widely used to study sensory encoding in the nervous system of living organisms. The stimulation of mechano-receptor neurons is achieved through a large variety of devices that generate movement or vibration. In many situations, a hard real-time (RT) control of the device (in the millisecond time scale) is needed to produce realistic mechanical stimuli. The real-time control can be required to achieve the desired precision in the device or to implement activity-dependent stimulation protocols that imply the detection of physiological events to drive the stimulus in real time. In this paper we show that real-time software technology can be used to control stepper motors for mechano-receptor stimulation, and to implement artificial closed-loops to address the sensory-motor transformation. We illustrate this using as an example the control of a stepper motor to precisely move gravimetric organs in in vitro preparations.

Neural signatures: multiple coding in spiking-bursting cells.

Recent experiments have revealed the existence of neural signatures in the activity of individual cells of the pyloric central pattern generator (CPG) of crustacean. The neural signatures consist of cell-specific spike timings in the bursting activity of the neurons. The role of these intraburst neural fingerprints is still unclear. It has been reported previously that some muscles can reflect small changes in the spike timings of the neurons that innervate them. However, it is unclear to what extent neural signatures contribute to the command message that the muscles receive from the motoneurons. It is also unknown whether the signatures have any functional meaning for the neurons that belong to the same CPG or to other interconnected CPGs. In this paper, we use realistic neural models to study the ability of single cells and small circuits to recognize individual neural signatures. We show that model cells and circuits can respond distinctly to the incoming neural fingerprints in addition to the properties of the slow depolarizing waves. Our results suggest that neural signatures can be a general mechanism of spiking-bursting cells to implement multicoding.

Analysis of perfect mappings of the stimuli through neural temporal sequences.

The analysis of an optimal neural system that maps stimuli into unique sequences of activations of fundamental atoms or functional clusters (FCs) is carried out. We say that it is perfect because the system maps with an injective function every stimulus in minimum time with the least number of FCs, such that every FC is activated only once. The neural system has the possibility to sustain several sequences in parallel. In this framework, we study the capacity achievable by the system, minimal completion time and complexity in terms of the number of parallel sequences. We show that the maximum capacity of the system is achieved without using parallel sequences at the expense of long completion times. However, when the capacity value is fixed, the largest possible number of parallel sequences is optimal because it requires short completion times. The complexity measure adds to important points: (i) the largest complexity of the system is achieved without parallel sequences, and (ii) the capacity estimation is a good estimation of the complexity of the system.