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Dr. Gloria Fernández-Lázaro
1. Animal Welfare research Group AWSHEL-IAS and Friends of Thoreau Program, Franklin Institute, University of Alcalá, 28801 Alcalá de Henares, Madrid, Spain

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0 Animal Behavior
0 Animal Training
0 Animal Welfare
0 Environmental Education
0 Primatology

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Conference paper
Published: 21 August 2021 in Lecture Notes in Computer Science
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Central Pattern Generators (CPGs) are neural circuits that generate robust coordinated neural activity to control motor rhythms. Many CPGs are convenient neural circuits for locomotion control in autonomous robots. In this context, invertebrate CPGs are key networks to understand rhythm generation and coordination, as their cells and connections can be identified and mapped, like in the crustacean pyloric CPG. Experiments during the last decades have shown that mutual inhibition by chemical synapses together with electrical coupling underlie the timing of neuron activations that shape each rhythm cycle of this CPG. Due to the presence of inhibitory and electrical synapses, regular and irregular triphasic spiking-bursting activity can be found in the pyloric CPG, always preserving the same neuron activation sequence. In this study, we use a model of this well-known CPG to assess the role of electrical synapses in shaping the cycle-by-cycle period and individual cell burst duration. We show that electrical coupling strength asymmetrically affects the burst duration of each individual neuron, as well as the overall cycle-by-cycle duration. Our results support the view that electrical coupling largely contributes to shape the intervals that define functional sequences in CPGs, which can be applied in bioinspired autonomous robotic motor control.

ACS Style

Blanca Berbel; Alicia Garrido-Peña; Irene Elices; Roberto Latorre; Pablo Varona. Effect of Electrical Synapses in the Cycle-by-Cycle Period and Burst Duration of Central Pattern Generators. Lecture Notes in Computer Science 2021, 81 -92.

AMA Style

Blanca Berbel, Alicia Garrido-Peña, Irene Elices, Roberto Latorre, Pablo Varona. Effect of Electrical Synapses in the Cycle-by-Cycle Period and Burst Duration of Central Pattern Generators. Lecture Notes in Computer Science. 2021; ():81-92.

Chicago/Turabian Style

Blanca Berbel; Alicia Garrido-Peña; Irene Elices; Roberto Latorre; Pablo Varona. 2021. "Effect of Electrical Synapses in the Cycle-by-Cycle Period and Burst Duration of Central Pattern Generators." Lecture Notes in Computer Science , no. : 81-92.

Journal article
Published: 21 August 2021 in Animals
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Many articles have shown the benefits of operant conditioning training techniques in the care and welfare of several species of nonhuman primates; however, the information regarding their use in strepsirrhine species is scarce. We assessed the development and current status of training programs with these species in North American institutions. An online survey was distributed through members of the Association of Zoos and Aquariums using a multiple-choice format. We collected information related to training program details; animals, behaviors, and techniques; the evaluation process; and the impact of training. Seventy-one organizations completed the survey, with the results showing that 97% of respondents trained their strepsirrhines with the main objective of husbandry and veterinary care (around 80%). Sixty-eight percent of organizations did not report any risk in training these species. The benefits reported include increases in positive human–animal interactions (97%), psychological well-being (88%), and staff awareness of animal behaviors (90%). However, a multi-dimensional approach to measure the efficacy of training could provide a deeper understanding of its impact on the welfare of strepsirrhine primates. We hope that the data offered in this survey can help in this future assessment.

ACS Style

Gloria Fernández-Lázaro; Meg H. Dye; Christie Eddie; Gina M. Ferrie. Strepsirrhine Primate Training Programs in North American Institutions: Status and Implications for Future Welfare Assessment. Animals 2021, 11, 2462 .

AMA Style

Gloria Fernández-Lázaro, Meg H. Dye, Christie Eddie, Gina M. Ferrie. Strepsirrhine Primate Training Programs in North American Institutions: Status and Implications for Future Welfare Assessment. Animals. 2021; 11 (8):2462.

Chicago/Turabian Style

Gloria Fernández-Lázaro; Meg H. Dye; Christie Eddie; Gina M. Ferrie. 2021. "Strepsirrhine Primate Training Programs in North American Institutions: Status and Implications for Future Welfare Assessment." Animals 11, no. 8: 2462.

Journal article
Published: 01 August 2020 in Neurocomputing
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The interaction between synaptic and intrinsic dynamics can efficiently shape neuronal input–output relationships in response to temporally structured spike trains. We use a neuron model with subthreshold oscillations receiving inputs through a synapse with short-term depression and facilitation to show that the combination of intrinsic subthreshold and synaptic dynamics leads to channel-specific nontrivial responses and recognition of specific temporal structures. Our study employs the Generalized Integrate-and-Fire (GIF) model, which can be subjected to analytical characterization. We map the temporal structure of spike input trains to the type of spike response, and show how the emergence of nontrivial input–output preferences is modulated by intrinsic and synaptic parameters in a synergistic manner. We demonstrate that these temporal input discrimination properties are robust to noise and to variations in synaptic strength. Furthermore, we also illustrate the presence of these input–output relationships in conductance-based models. Our results suggest a widespread computationally economic and easily tunable mechanism for temporal information discrimination in single neurons.

ACS Style

Joaquín J. Torres; Fabiano Baroni; Roberto Latorre; Pablo Varona. Temporal discrimination from the interaction between dynamic synapses and intrinsic subthreshold oscillations. Neurocomputing 2020, 417, 543 -557.

AMA Style

Joaquín J. Torres, Fabiano Baroni, Roberto Latorre, Pablo Varona. Temporal discrimination from the interaction between dynamic synapses and intrinsic subthreshold oscillations. Neurocomputing. 2020; 417 ():543-557.

Chicago/Turabian Style

Joaquín J. Torres; Fabiano Baroni; Roberto Latorre; Pablo Varona. 2020. "Temporal discrimination from the interaction between dynamic synapses and intrinsic subthreshold oscillations." Neurocomputing 417, no. : 543-557.

Preprint content
Published: 06 August 2019
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The interaction between synaptic and intrinsic dynamics can efficiently shape neuronal input-output relationships in response to temporally structured spike trains. We use a neuron model with subthreshold oscillations receiving inputs through a synapse with short-term depression and facilitation to show that the combination of intrinsic subthreshold and synaptic dynamics leads to channel-specific nontrivial responses and recognition of specific temporal structures. We employ the Generalized Integrate-and-Fire (GIF) model, which can be subjected to analytical characterization. We map the temporal structure of spike input trains to the type of spike response, and show how the emergence of nontrivial input-output preferences is modulated by intrinsic and synaptic parameters in a synergistic manner. We demonstrate that these temporal input discrimination properties are robust to noise and to variations in synaptic strength, suggesting that they likely contribute to neuronal computation in biological circuits. Furthermore, we also illustrate the presence of these input-output relationships in conductance-based models.Author summaryNeuronal subthreshold oscillations underlie key aspects of information processing in single neuron and network dynamics. Dynamic synapses provide a channel-specific temporal modulation of input information. We combine a neuron model that displays subthreshold oscillations and a dynamic synapse to analytically assess their interplay in processing trains of spike-mediated synaptic currents. Our results show that the co-action of intrinsic and synaptic dynamics builds nontrivial input-output relationships, which are resistant to noise and to changes in synaptic strength. The discrimination of a precise temporal structure of the input signal is shaped as a function of the joint interaction of intrinsic oscillations and synaptic dynamics. This interaction can result in channel-specific recognition of precise temporal patterns, hence greatly expanding the flexibility and complexity in information processing achievable by individual neurons with respect to temporal discrimination mechanisms based on intrinsic neuronal dynamics alone.

ACS Style

Joaquin J. Torres; Fabiano Baroni; Roberto Latorre; Pablo Varona. Temporal discrimination from the interaction between dynamic synapses and intrinsic subthreshold oscillations. 2019, 1 .

AMA Style

Joaquin J. Torres, Fabiano Baroni, Roberto Latorre, Pablo Varona. Temporal discrimination from the interaction between dynamic synapses and intrinsic subthreshold oscillations. . 2019; ():1.

Chicago/Turabian Style

Joaquin J. Torres; Fabiano Baroni; Roberto Latorre; Pablo Varona. 2019. "Temporal discrimination from the interaction between dynamic synapses and intrinsic subthreshold oscillations." , no. : 1.

Journal article
Published: 02 July 2019 in Behavioural Processes
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Measuring personality is being used to improved nonhuman primate welfare. To expand its use, it is important to identify traits that are shared between species and that measures are reliable, easy to use and less time consuming. Combining personality and other indicators strong validation of the results can be obtained. In the present study, we sought to determine if there is a link between physiological stress response (fecal cortisol metabolites), personality (ratings made by animal keepers and reaction to novel objects) and lateralization of the brain (hand preferences) on eight species of nonhuman primates: Callithrix jacchus, Callithrix geoffroyi, Cebuella Pygmaea, Saguinus imperator, Saguinus oedipus, Leontopithecus rosalia, Pithecia pithecia and Nycticebus pygmaeus. Personality assessments achieved good levels of interrater reliability and revealed three components of personality in our sample: fearfulness, activeness and aggressiveness. More exploratory individuals were more active, aggressive and showed higher cortisol metabolite levels. Right-handed subjects inspected novel objects sooner and the strength of the lateralization was linked with individual stress and the aggressiveness component. Our results highlight that there is a relation between personality, lateralization and physiological indicators in nonhuman primates, but although some aspects can be generalized across species and/or sexes others are species/sex dependent.

ACS Style

Gloria Fernández-Lázaro; Roberto Latorre; Enrique Alonso-García; Isabel Barja Núñez. Nonhuman primate welfare: Can there be a relationship between personality, lateralization and physiological indicators? Behavioural Processes 2019, 166, 103897 .

AMA Style

Gloria Fernández-Lázaro, Roberto Latorre, Enrique Alonso-García, Isabel Barja Núñez. Nonhuman primate welfare: Can there be a relationship between personality, lateralization and physiological indicators? Behavioural Processes. 2019; 166 ():103897.

Chicago/Turabian Style

Gloria Fernández-Lázaro; Roberto Latorre; Enrique Alonso-García; Isabel Barja Núñez. 2019. "Nonhuman primate welfare: Can there be a relationship between personality, lateralization and physiological indicators?" Behavioural Processes 166, no. : 103897.

Journal article
Published: 23 November 2018 in Neurocomputing
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Cognitive/behavioral brain functions are implemented through temporary correlated sequential activity of many brain elements that form universal anatomical and functional motifs, i.e., characteristic functional interactions among brain nodes, at different levels of the neural hierarchy. Such motif dynamics is determined by both the interconnections among nodes and their intrinsic oscillations. This paper focuses on heteroclinic motifs, i.e., those built in networks of oscillatory nodes that interact through asymmetric inhibitory coupling in a winnerless competitive way. We introduce a basic rate-phase motif model – based on a generalization of the well-known ecological Lotka-Volterra model – for the analysis and prediction of control processes that emerge in interacting heteroclinic motifs under periodic stimulation. This approach describes both intensity and phase in each node. We study how a rhythmic signal, which can be linked to internal or external sources, can functionally change the heteroclinic network and produce a rich gallery of motifs in the form of coordinated sequential activations. In computer simulations of the model in a “master-slave” approximation, we report phenomena such as dynamical filtering, encoding enhancement and transition to chaos. Our results are relevant in the context of several experimental protocols related to the role of brain rhythms and/or the use of external rhythmic stimulation, in particular in the context of transcranial control and evoked potentials, to assess cognitive functions and their associated pathologies.

ACS Style

Roberto Latorre; Pablo Varona; Mikhail I. Rabinovich. Rhythmic control of oscillatory sequential dynamics in heteroclinic motifs. Neurocomputing 2018, 331, 108 -120.

AMA Style

Roberto Latorre, Pablo Varona, Mikhail I. Rabinovich. Rhythmic control of oscillatory sequential dynamics in heteroclinic motifs. Neurocomputing. 2018; 331 ():108-120.

Chicago/Turabian Style

Roberto Latorre; Pablo Varona; Mikhail I. Rabinovich. 2018. "Rhythmic control of oscillatory sequential dynamics in heteroclinic motifs." Neurocomputing 331, no. : 108-120.

Journal article
Published: 13 November 2018 in Scientific Reports
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Bursting activity is present in many cells of different nervous systems playing important roles in neural information processing. Multiple assemblies of bursting neurons act cooperatively to produce coordinated spatio-temporal patterns of sequential activity. A major goal in neuroscience is unveiling the mechanisms underlying neural information processing based on this sequential dynamics. Experimental findings have revealed the presence of precise cell-type-specific intraburst firing patterns in the activity of some bursting neurons. This characteristic neural signature coexists with the information encoded in other aspects of the spiking-bursting signals, and its functional meaning is still unknown. We investigate the ability of a neuron conductance-based model to detect specific presynaptic activation sequences taking advantage of intraburst fingerprints identifying the source of the signals building up a sequential pattern of activity. Our simulations point out that a reader neuron could use this information to contextualize incoming signals and accordingly compute a characteristic response by relying on precise phase relationships among the activity of different emitters. This would provide individual neurons enhanced capabilities to control and negotiate sequential dynamics. In this regard, we discuss the possible implications of the proposed contextualization mechanism for neural information processing.

ACS Style

José Luis Carrillo-Medina; Roberto Latorre. Detection of Activation Sequences in Spiking-Bursting Neurons by means of the Recognition of Intraburst Neural Signatures. Scientific Reports 2018, 8, 1 -15.

AMA Style

José Luis Carrillo-Medina, Roberto Latorre. Detection of Activation Sequences in Spiking-Bursting Neurons by means of the Recognition of Intraburst Neural Signatures. Scientific Reports. 2018; 8 (1):1-15.

Chicago/Turabian Style

José Luis Carrillo-Medina; Roberto Latorre. 2018. "Detection of Activation Sequences in Spiking-Bursting Neurons by means of the Recognition of Intraburst Neural Signatures." Scientific Reports 8, no. 1: 1-15.

Journal article
Published: 07 February 2018 in Scientific Reports
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Physiological stress response is a crucial adaptive mechanism for prey species survival. This paper aims to identify the main environmental and/or individual factors better explaining the stress response in Wood mice, Apodemus sylvaticus. We analyzed alterations in fecal glucocorticoid metabolite (FCM) concentration - extensively used as an accurate measure of the physiological stress response - of wild mice fecal samples seasonally collected during three years. Then, support vector machines were built to predict said concentration according to different stressors. These statistical tools appear to be particularly suitable for small datasets with substantial number of dimensions, corroborating that the stress response is an extremely complex process in which multiple factors can simultaneously partake in a context-dependent manner, i.e., the role of each potential stressor varies in time depending on other stressors. However, air-humidity, temperature and body-weight allowed us to explain the FCM fluctuation in 98% of our samples. The relevance of air-humidity and temperature altering FCM level could be linked to the presence of an abundant vegetation cover and, therefore, to food availability and predation risk perception. Body-weight might be related to the stress produced by reproduction and other intraspecific relationships such as social dominance or territorial behavior.

ACS Style

Beatriz Sánchez-González; Isabel Barja; Ana Piñeiro; M. Carmen Hernández-González; Gema Silván; Juan Carlos Illera; Roberto Latorre. Support vector machines for explaining physiological stress response in Wood mice (Apodemus sylvaticus). Scientific Reports 2018, 8, 2562 .

AMA Style

Beatriz Sánchez-González, Isabel Barja, Ana Piñeiro, M. Carmen Hernández-González, Gema Silván, Juan Carlos Illera, Roberto Latorre. Support vector machines for explaining physiological stress response in Wood mice (Apodemus sylvaticus). Scientific Reports. 2018; 8 (1):2562.

Chicago/Turabian Style

Beatriz Sánchez-González; Isabel Barja; Ana Piñeiro; M. Carmen Hernández-González; Gema Silván; Juan Carlos Illera; Roberto Latorre. 2018. "Support vector machines for explaining physiological stress response in Wood mice (Apodemus sylvaticus)." Scientific Reports 8, no. 1: 2562.

Journal article
Published: 01 December 2017 in Journal of Systems and Software
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ACS Style

Roberto Latorre; Javier Suárez. Measuring social networks when forming information system project teams. Journal of Systems and Software 2017, 134, 304 -323.

AMA Style

Roberto Latorre, Javier Suárez. Measuring social networks when forming information system project teams. Journal of Systems and Software. 2017; 134 ():304-323.

Chicago/Turabian Style

Roberto Latorre; Javier Suárez. 2017. "Measuring social networks when forming information system project teams." Journal of Systems and Software 134, no. : 304-323.

Original research article
Published: 20 December 2016 in Frontiers in Computational Neuroscience
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Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. Spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm – i.e., neural signatures to identify each unit in the network, local information contextualization during the processing and multicoding strategies for information propagation regarding the origin and the content of the data – to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence of inhibitory connections. These parameters also modulate the memory capabilities of the network. The dynamical modes observed in the different informational dimensions in a given moment are independent and they only depend on the parameters shaping the information processing in this dimension. We argue that plasticity mechanisms inside individual cells and multicoding strategies can provide additional computational properties to spiking neural networks, which could enhance their capacity and performance in a wide variety of real-world tasks.

ACS Style

José Luis Carrillo-Medina; Roberto Latorre. Implementing Signature Neural Networks with Spiking Neurons. Frontiers in Computational Neuroscience 2016, 10, 1 .

AMA Style

José Luis Carrillo-Medina, Roberto Latorre. Implementing Signature Neural Networks with Spiking Neurons. Frontiers in Computational Neuroscience. 2016; 10 ():1.

Chicago/Turabian Style

José Luis Carrillo-Medina; Roberto Latorre. 2016. "Implementing Signature Neural Networks with Spiking Neurons." Frontiers in Computational Neuroscience 10, no. : 1.

Research article
Published: 05 January 2016 in PLOS ONE
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In this paper we analyze the interplay between the subthreshold oscillations of a single neuron conductance-based model and the short-term plasticity of a dynamic synapse with a depressing mechanism. In previous research, the computational properties of subthreshold oscillations and dynamic synapses have been studied separately. Our results show that dynamic synapses can influence different aspects of the dynamics of neuronal subthreshold oscillations. Factors such as maximum hyperpolarization level, oscillation amplitude and frequency or the resulting firing threshold are modulated by synaptic depression, which can even make subthreshold oscillations disappear. This influence reshapes the postsynaptic neuron’s resonant properties arising from subthreshold oscillations and leads to specific input/output relations. We also study the neuron’s response to another simultaneous input in the context of this modulation, and show a distinct contextual processing as a function of the depression, in particular for detection of signals through weak synapses. Intrinsic oscillations dynamics can be combined with the characteristic time scale of the modulatory input received by a dynamic synapse to build cost-effective cell/channel-specific information discrimination mechanisms, beyond simple resonances. In this regard, we discuss the functional implications of synaptic depression modulation on intrinsic subthreshold dynamics.

ACS Style

Roberto Latorre; Joaquín J. Torres; Pablo Varona. Interplay between Subthreshold Oscillations and Depressing Synapses in Single Neurons. PLOS ONE 2016, 11, e0145830 .

AMA Style

Roberto Latorre, Joaquín J. Torres, Pablo Varona. Interplay between Subthreshold Oscillations and Depressing Synapses in Single Neurons. PLOS ONE. 2016; 11 (1):e0145830.

Chicago/Turabian Style

Roberto Latorre; Joaquín J. Torres; Pablo Varona. 2016. "Interplay between Subthreshold Oscillations and Depressing Synapses in Single Neurons." PLOS ONE 11, no. 1: e0145830.

Conference paper
Published: 01 July 2015 in 2015 International Joint Conference on Neural Networks (IJCNN)
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Experimental evidence has revealed that different living neural systems can “sign” their output signals with some specific neural signature. Although experimental and modeling results suggest that these neural signatures can have significant implications for the activity of the neural circuits where they are present, the functional meaning of these neural fingerprints is still unclear. The existence of cellular mechanisms to identify the source of individual signals and contextualize incoming messages as a function of this identification can be a powerful information processing strategy for the nervous system. We have recently built different models to study the ability of a neural network to encode and process information based on the emission and recognition of specific neural fingerprints. In this paper, we further analyze the features that can influence on the information processing ability of this kind of networks. In particular, we focus on the role that the duration of a refractory period in each neuron after emitting a signed message can play in the network collective dynamics.

ACS Style

José Luis Carrillo-Medina; Roberto Latorre. Influence of the refractory period on neural networks based on the recognition of neural signatures. 2015 International Joint Conference on Neural Networks (IJCNN) 2015, 1 -9.

AMA Style

José Luis Carrillo-Medina, Roberto Latorre. Influence of the refractory period on neural networks based on the recognition of neural signatures. 2015 International Joint Conference on Neural Networks (IJCNN). 2015; ():1-9.

Chicago/Turabian Style

José Luis Carrillo-Medina; Roberto Latorre. 2015. "Influence of the refractory period on neural networks based on the recognition of neural signatures." 2015 International Joint Conference on Neural Networks (IJCNN) , no. : 1-9.

Original research article
Published: 24 March 2015 in Frontiers in Computational Neuroscience
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Experimental evidence has revealed the existence of characteristic spiking features in different neural signals, e.g. individual neural signatures identifying the emitter or functional signatures characterizing specific tasks. These neural fingerprints may play a critical role in neural information processing, since they allow receptors to discriminate or contextualize incoming stimuli. This could be a powerful strategy for neural systems that greatly enhances the encoding and processing capacity of these networks. Nevertheless, the study of information processing based on the identification of specific neural fingerprints has attracted little attention. In this work, we study (i) the emerging collective dynamics of a network of neurons that communicate with each other by exchange of neural fingerprints and (ii) the influence of the network topology on the self-organizing properties within the network. Complex collective dynamics emerge in the network in the presence of stimuli. Predefined inputs, i.e. specific neural fingerprints, are detected and encoded into coexisting patterns of activity that propagate throughout the network with different spatial organization. The patterns evoked by a stimulus can survive after the stimulation is over, which provides memory mechanisms to the network. The results presented in this paper suggest that neural information processing based on neural fingerprints can be a plausible, flexible and powerful strategy.

ACS Style

Josã© Luis Carrillo-Medina; Roberto Latorre; José Luis Carrillo-Medina. Neural dynamics based on the recognition of neural fingerprints. Frontiers in Computational Neuroscience 2015, 9, 33 .

AMA Style

Josã© Luis Carrillo-Medina, Roberto Latorre, José Luis Carrillo-Medina. Neural dynamics based on the recognition of neural fingerprints. Frontiers in Computational Neuroscience. 2015; 9 ():33.

Chicago/Turabian Style

Josã© Luis Carrillo-Medina; Roberto Latorre; José Luis Carrillo-Medina. 2015. "Neural dynamics based on the recognition of neural fingerprints." Frontiers in Computational Neuroscience 9, no. : 33.

Journal article
Published: 20 December 2013 in IEEE Transactions on Software Engineering
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Unit test-driven development (UTDD) is a software development practice where unit test cases are specified iteratively and incrementally before production code. In the last years, researchers have conducted several studies within academia and industry on the effectiveness of this software development practice. They have investigated its utility as compared to other development techniques, focusing mainly on code quality and productivity. This quasi-experiment analyzes the influence of the developers' experience level on the ability to learn and apply UTDD. The ability to apply UTDD is measured in terms of process conformance and development time. From the research point of view, our goal is to evaluate how difficult is learning UTDD by professionals without any prior experience in this technique. From the industrial point of view, the goal is to evaluate the possibility of using this software development practice as an effective solution to take into account in real projects. Our results suggest that skilled developers are able to quickly learn the UTDD concepts and, after practicing them for a short while, become as effective in performing small programming tasks as compared to more traditional test-last development techniques. Junior programmers differ only in their ability to discover the best design, and this translates into a performance penalty since they need to revise their design choices more frequently than senior programmers.

ACS Style

Roberto Latorre. Effects of Developer Experience on Learning and Applying Unit Test-Driven Development. IEEE Transactions on Software Engineering 2013, 40, 381 -395.

AMA Style

Roberto Latorre. Effects of Developer Experience on Learning and Applying Unit Test-Driven Development. IEEE Transactions on Software Engineering. 2013; 40 (4):381-395.

Chicago/Turabian Style

Roberto Latorre. 2013. "Effects of Developer Experience on Learning and Applying Unit Test-Driven Development." IEEE Transactions on Software Engineering 40, no. 4: 381-395.

Journal article
Published: 27 September 2013 in Empirical Software Engineering
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Unit Test-Driven Development (UTDD) and Acceptance Test-Driven Development (ATDD) are software development techniques to incrementally develop software where the test cases, unit or acceptance tests respectively, are specified before the functional code. There are little empirical evidences supporting or refuting the utility of these techniques in an industrial context. Just a few case studies can be found in literature within the industrial environment and they show conflicting results (positive, negative and neutral). In this report, we present a successful application of UTDD in combination with ATDD in a commercial project. By successful we mean that the project goals are reached without an extra economic cost. All the UTDD and ATDD implementations are based on the same basic concepts, but they may differ in specific adaptations to each project or team. In the implementation presented here, the business requirements are specified by means of executable acceptance tests, which then are the input of a development process where the functional code is written in response to specific unit tests. Our goal is to share our successful experience in a specific project from an empirical point of view. We highlight the advantages and disadvantages of adopting UTDD and ATDD and identify some conditions that facilitate success. The main conclusions we draw from this project are that ATDD contributes to clearly capture and validate the business requirements, but it requires an extensive cooperation from the customer; and that UTDD has not a significant impact neither on productivity nor on software quality. These results cannot be generalized, but they point out that under some circumstances a test-driven development strategy can be a possible option to take into account by software professionals.

ACS Style

Roberto Latorre. A successful application of a Test-Driven Development strategy in the industrial environment. Empirical Software Engineering 2013, 19, 753 -773.

AMA Style

Roberto Latorre. A successful application of a Test-Driven Development strategy in the industrial environment. Empirical Software Engineering. 2013; 19 (3):753-773.

Chicago/Turabian Style

Roberto Latorre. 2013. "A successful application of a Test-Driven Development strategy in the industrial environment." Empirical Software Engineering 19, no. 3: 753-773.

Journal article
Published: 08 July 2013 in BMC Neuroscience
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ACS Style

Roberto Latorre; Rafael Levi; Pablo Varona. Sensory dynamics transformation into effective motor behavior. BMC Neuroscience 2013, 14, F2 -F2.

AMA Style

Roberto Latorre, Rafael Levi, Pablo Varona. Sensory dynamics transformation into effective motor behavior. BMC Neuroscience. 2013; 14 (S1):F2-F2.

Chicago/Turabian Style

Roberto Latorre; Rafael Levi; Pablo Varona. 2013. "Sensory dynamics transformation into effective motor behavior." BMC Neuroscience 14, no. S1: F2-F2.

Journal article
Published: 27 April 2013 in Journal of the Franklin Institute
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The generation of coordinated patterns of activity in the nervous system is essential to drive complex behavior in animals, both vertebrates and invertebrates. In many cases rhythmic patterns of activity are the result of the cooperation between groups of small number of neurons bearing overall network dynamics. These patterns encode information in different spatio-temporal scales based on the history-dependent capabilities of neuronal dynamics. In this work we analyze a simple neural network, a Central Pattern Generator, by identifying and characterizing the dynamical patterns sustaining the coordination among the constituent neurons. The description of the corresponding coordination states is performed with the guidance of the theory of applied symbolic dynamics. We show that symbolic dynamics enables the automatic detection of meaningful events with low computational cost, endorsing the analysis of both individual and global neuronal dynamics. Furthermore, symbolic dynamics can be used to compute entropy and distinguish between networks with the same topology but different dynamics for the underlying nodes. The results obtained along the paper are not restricted to simple systems, and the proposed methodology can be applied to the generalization of closed-loop observation and control of complex biological systems.

ACS Style

David Arroyo; Roberto Latorre; Pablo Varona; Francisco De Borja Rodriguez. Application of symbolic dynamics to characterize coordinated activity in the context of biological neural networks. Journal of the Franklin Institute 2013, 350, 2967 -2981.

AMA Style

David Arroyo, Roberto Latorre, Pablo Varona, Francisco De Borja Rodriguez. Application of symbolic dynamics to characterize coordinated activity in the context of biological neural networks. Journal of the Franklin Institute. 2013; 350 (10):2967-2981.

Chicago/Turabian Style

David Arroyo; Roberto Latorre; Pablo Varona; Francisco De Borja Rodriguez. 2013. "Application of symbolic dynamics to characterize coordinated activity in the context of biological neural networks." Journal of the Franklin Institute 350, no. 10: 2967-2981.

Research article
Published: 14 February 2013 in PLOS Computational Biology
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The intrinsic dynamics of sensory networks play an important role in the sensory-motor transformation. In this paper we use conductance based models and electrophysiological recordings to address the study of the dual role of a sensory network to organize two behavioral context-dependent motor programs in the mollusk Clione limacina. We show that: (i) a winner take-all dynamics in the gravimetric sensory network model drives the typical repetitive rhythm in the wing central pattern generator (CPG) during routine swimming; (ii) the winnerless competition dynamics of the same sensory network organizes the irregular pattern observed in the wing CPG during hunting behavior. Our model also shows that although the timing of the activity is irregular, the sequence of the switching among the sensory cells is preserved whenever the same set of neurons are activated in a given time window. These activation phase locks in the sensory signals are transformed into specific events in the motor activity. The activation phase locks can play an important role in motor coordination driven by the intrinsic dynamics of a multifunctional sensory organ. How sensory information is transformed into effective motor action is one of the most fundamental questions in neuroscience. As this question is difficult to assess experimentally, biophysical models of sensory, central and motor systems contribute to understand the information processing mechanisms involved in this transformation. Biophysical models can be informed by electrophysiological data in those situations where it is possible to record neural activity at all stages of sensory-motor processing. In this paper we use this approach to describe the dual dynamics of a multifunctional sensory organ in the mollusk Clione limacina and its transformation into two different motor programs. Our experimental and modeling results indicate that the sensory signals are modified to fit the changing behavioral context, and they are readily interpreted by the rest of the nervous system to produce the correct motor output.

ACS Style

Roberto Latorre; Rafael Levi; Pablo Varona. Transformation of Context-dependent Sensory Dynamics into Motor Behavior. PLOS Computational Biology 2013, 9, e1002908 .

AMA Style

Roberto Latorre, Rafael Levi, Pablo Varona. Transformation of Context-dependent Sensory Dynamics into Motor Behavior. PLOS Computational Biology. 2013; 9 (2):e1002908.

Chicago/Turabian Style

Roberto Latorre; Rafael Levi; Pablo Varona. 2013. "Transformation of Context-dependent Sensory Dynamics into Motor Behavior." PLOS Computational Biology 9, no. 2: e1002908.

Journal article
Published: 01 January 2013 in Frontiers in Neural Circuits
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The inferior olive (IO) is a neural network belonging to the olivo-cerebellar system whose neurons are coupled with electrical synapses and display subthreshold oscillations and spiking activity. The IO generates complex spatio-temporal patterns. The generation and modulation of coherent spiking activity in the IO is one key issue in cerebellar research. In this work, we build a large scale IO network model of electrically coupled conductance-based neurons to study the emerging spatio-temporal patterns of its transient neuronal activity. The main factors studied governing the collective dynamics of the IO network were: the degree of electrical coupling, the extent of the electrotonic connections, the presence of stimuli or regions with different excitability levels and the modulatory effect of an inhibitory loop. The spatio-temporal patterns were analyzed using a discrete wavelet transform to provide a quantitative characterization. Our results show that the electrotonic coupling produces quasi-synchronized subthreshold oscillations over a wide dynamical range. The synchronized oscillatory activity plays the role of a timer for a coordinated representation of spiking rhythms with different frequencies. The encoding and coexistence of several coordinated rhythms is related to the different clusterization and coherence of transient spatio-temporal patterns in the network, where the spiking activity is commensurate with the quasi-synchronized subthreshold oscillations. In the presence of stimuli, different rhythms are encoded in the spiking activity of IO neurons that nevertheless remains constrained to a commensurate value of the subthreshold frequency. The stimuli induced spatio-temporal patterns can reverberate for long periods, which contributes to the computational properties of the IO. We also show that the presence of regions with different excitability levels creates sinks and sources of coordinated activity which shape the propagation of spike wave fronts.

ACS Style

Roberto Latorre; Carlos Aguirre; Mikhail I. Rabinovich; Pablo Varona. Transient dynamics and rhythm coordination of inferior olive spatio-temporal patterns. Frontiers in Neural Circuits 2013, 7, 138 .

AMA Style

Roberto Latorre, Carlos Aguirre, Mikhail I. Rabinovich, Pablo Varona. Transient dynamics and rhythm coordination of inferior olive spatio-temporal patterns. Frontiers in Neural Circuits. 2013; 7 ():138.

Chicago/Turabian Style

Roberto Latorre; Carlos Aguirre; Mikhail I. Rabinovich; Pablo Varona. 2013. "Transient dynamics and rhythm coordination of inferior olive spatio-temporal patterns." Frontiers in Neural Circuits 7, no. : 138.

Conference paper
Published: 01 January 2011 in Transactions on Petri Nets and Other Models of Concurrency XV
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Bio-inspiration in traditional artificial neural networks (ANN) relies on knowledge about the nervous system that was available more than 60 years ago. Recent findings from neuroscience research provide novel elements of inspiration for ANN paradigms. We have recently proposed a Signature Neural Network that uses: (i) neural signatures to identify each unit in the network, (ii) local discrimination of input information during the processing, and (iii) a multicoding mechanism for information propagation regarding the who and the what of the information. The local discrimination implies a distinct processing as a function of the neural signature recognition and a local transient memory. In this paper we further analyze the role of this local context memory to efficiently solve jigsaw puzzles.

ACS Style

Roberto Latorre; Francisco B. Rodríguez; Pablo Varona. Local Context Discrimination in Signature Neural Networks. Transactions on Petri Nets and Other Models of Concurrency XV 2011, 6687, 400 -408.

AMA Style

Roberto Latorre, Francisco B. Rodríguez, Pablo Varona. Local Context Discrimination in Signature Neural Networks. Transactions on Petri Nets and Other Models of Concurrency XV. 2011; 6687 ():400-408.

Chicago/Turabian Style

Roberto Latorre; Francisco B. Rodríguez; Pablo Varona. 2011. "Local Context Discrimination in Signature Neural Networks." Transactions on Petri Nets and Other Models of Concurrency XV 6687, no. : 400-408.