Biotic interactions, crucial for understanding the ecology and evolution of species, are often conceptualized as ecological networks. However, the complexity of real ecosystems poses challenges for empirical inference, and theoretical interaction models, while informative, frequently fail to undergo empirical validation. This dual limitation creates a gap between theoretical and empirical approaches in portraying ecosystem dynamics and identifying (and protecting) key species, which are critical for conservation efforts and ecosystem management. In order to bridge this operational gap, we present a novel automated protocol capable of generating realistic trophic networks, including multilayer ones, using non-curated, freely-available species lists from real ecosystems as input data. As a proof-of-concept, we applied this method to the species lists contained in the RAMSAR database of wetland ecosystems. Our data mining algorithm enriches these species lists with functional traits, such as body size, habitat, and diet, by integrating information directly sourced from online biodiversity databases. Subsequently, a modified version of the Allometric Niche Model is used to sort species within the trophic network according to their functional traits and ecological roles. After demonstrating the algorithmic robustness of our method and the biological plausibility of the resulting ecological networks, we illustrate its potential to characterize, in a cost-efficient manner, the structure of real-world ecosystems and to identify the organisms that are crucial for maintaining that structure. In this case study, our findings indicate that the robustness of wetland ecosystems often depends on medium-sized, highly mobile organisms occupying intermediate trophic levels.

Functional neural rhythms arise from the excitability of individual neurons and the collective dynamics of the circuits in which they are embedded. The self-organization of neural networks can be best investigated in circuits where these two aspects can be elucidated. In this context, Central Pattern Generators (CPGs) are an ideal model system where the interplay between synaptic and ionic currents shapes robust yet flexible rhythmic sequences underlying a wide range of motor functions. CPGs often integrate both chemical and electrical synapses, forming closed-loop network topologies that autonomously generate and coordinate functional rhythms. Recent findings in CPGs show the existence of sequential dynamical invariants, which are robust relationships between specific time intervals that build the rhythmic sequence constraining their cycle-by-cycle variability. However, the precise mechanisms by which electrical and chemical synapses interact to modulate sequential neural dynamics remain largely unknown. This modeling study examines the role of electrical currents in rhythm modulation within CPGs, with a particular focus on how these currents influence neural excitability and, in turn, modulate the variability of the rhythmic pattern on a cycle-by-cycle basis. We show that changes in neuronal excitability induced by electrical currents can modulate dynamical invariants, leading to the emergence of distinct sequence interval relationships that facilitate the rhythm adaptability. Synchronization between electrically coupled neurons is thus a key factor in regulating these effects. Finally, our results show that sequential dynamical invariants sustain the balance between excitability, variability, and synchronization, underscoring their essential role in the adaptive coordination of functional neural rhythmic patterns.

Motor coordination by the nervous system involves generating and coordinating muscle sequential activity. Such activity must be robust to produce effective motion that relies on patterned motor events ordered in a specific sequence and, at the same time, flexible in timing and duration to adapt to intrinsic and environmental circumstances. Motor neural circuits produce the signaling that muscles use for their sequential action. Even in rhythmic repetitive motion such as walking, breathing and chewing, variability in the timing of the events that build the sequence can be observed within the overall ordered rhythm of the motion. In this context, central pattern generators (CPGs) are key neural circuits to study the cycle-by-cycle balance between the robustness of the sequential order of neural events and the irregularity of the intracycle intervals that shape a rhythmic sequence. Recently, the presence of sequential dynamical invariants in the form of robust cycle-by-cycle relationships between specific sequence time intervals was unveiled in living CPGs. Variability of other sequence intervals remains unrelated and coexists with the presence of the invariants. Sequential dynamical invariants have been proposed to underlie the cycle-by-cycle CPG coordination. In this work, we used a minimal CPG circuit building block of two interacting inhibitory model neurons to assess the emergence of coordinated variability in sequential neural activity. We studied the conditions that produce sequential dynamical invariants in the circuit using intrinsically irregular neuron models and symmetric and asymmetric network topologies. We quantified the circuit irregularity and related it to the presence of invariants under different configurations. We found the conditions to propagate and sustain coordinated variability between time intervals, and thus our study illustrates the auto-organized constraint-based mechanisms for motor sequence coordination that shape sequential dynamical invariants. This is the first model study in which sequential dynamical invariants are observed considering only the intrinsic variability of CPG neurons without external stimuli. Finally, we discuss possible applications of this research in the context of autonomous robotics and artificial intelligence.

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.

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.

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.

In this paper we present a self-organizing neural network paradigm that is able to discriminate information locally using a strategy for information coding and processing inspired in recent findings in living neural systems. The proposed neural network uses: 1) neural signatures to identify each unit in the network; 2) local discrimination of input information during the processing; and 3) 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 the context of artificial neural networks none of these mechanisms has been analyzed in detail, and our goal is to demonstrate that they can be used to efficiently solve some specific problems. To illustrate the proposed paradigm, we apply it to the problem of multidimensional sorting, which can take advantage of the local information discrimination. In particular, we compare the results of this new approach with traditional methods to solve jigsaw puzzles and we analyze the situations where the new paradigm improves the performance.

This paper presents a mechanism to optimize the access to a database for Java 2 Platform Enterprise Edition (J2EE) applications. This mechanism was used in the migration of a PHP application to the J2EE platform to solve the limitations found in the standard database access. The proposed mechanism is optimal for applications with complex database queries and/or a high number of them per time unit. It presents an alternative to the traditional access, which can produce a database overhead in applications with these characteristics. The proposed database access mechanism is based on the idea of sharing precompiled statements and connections between different users. When a part of the application wants to execute an SQL statement, either a new object is created representing it, or an existing one, created previously, is reused. Copyright © 2004 John Wiley & Sons, Ltd.