Benchmarking of tools for axon length measurement in individually-labeled projection neurons.

Projection neurons are the commonest neuronal type in the mammalian forebrain and their individual characterization is a crucial step to understand how neural circuitry operates. These cells have an axon whose arborizations extend over long distances, branching in complex patterns and/or in multiple brain regions. Axon length is a principal estimate of the functional impact of the neuron, as it directly correlates with the number of synapses formed by the axon in its target regions; however, its measurement by direct 3D axonal tracing is a slow and labor-intensive method.

Static internal representation of dynamic situations reveals time compaction in human cognition.

Introduction: The human brain has evolved under the constraint of survival in complex dynamic situations. It makes fast and reliable decisions based on internal representations of the environment. Whereas neural mechanisms involved in the internal representation of space are becoming known, entire spatiotemporal cognition remains a challenge.

Semantic Knowledge Representation for Strategic Interactions in Dynamic Situations.

Evolved living beings can anticipate the consequences of their actions in complex multilevel dynamic situations. This ability relies on abstracting the meaning of an action. The underlying brain mechanisms of such semantic processing of information are poorly understood.

Effective centrality and explosive synchronization in complex networks.

Synchronization of networked oscillators is known to depend fundamentally on the interplay between the dynamics of the graph's units and the microscopic arrangement of the network's structure. We here propose an effective network whose topological properties reflect the interplay between the topology and dynamics of the original network. On that basis, we are able to introduce the effective centrality, a measure that quantifies the role and importance of each network's node in the synchronization process.

Effects of Spike Anticipation on the Spiking Dynamics of Neural Networks.

Synchronization is one of the central phenomena involved in information processing in living systems. It is known that the nervous system requires the coordinated activity of both local and distant neural populations. Such an interplay allows to merge different information modalities in a whole processing supporting high-level mental skills as understanding, memory, abstraction, etc.

Effects of degree correlations on the explosive synchronization of scale-free networks.

We study the organization of finite-size, large ensembles of phase oscillators networking via scale-free topologies in the presence of a positive correlation between the oscillators' natural frequencies and the network's degrees. Under those circumstances, abrupt transitions to synchronization are known to occur in growing scale-free networks, while the transition has a completely different nature for static random configurations preserving the same structure-dynamics correlation. We show that the further presence of degree-degree correlations in the network structure has important consequences on the nature of the phase transition characterizing the passage from the phase-incoherent to the phase-coherent network state.

Prediction-for-CompAction: navigation in social environments using generalized cognitive maps.

The ultimate navigation efficiency of mobile robots in human environments will depend on how we will appraise them: merely as impersonal machines or as human-like agents. In the latter case, an agent may take advantage of the cooperative collision avoidance, given that it possesses recursive cognition, i.e.

Neural network architecture for cognitive navigation in dynamic environments.

Navigation in time-evolving environments with moving targets and obstacles requires cognitive abilities widely demonstrated by even simplest animals. However, it is a long-standing challenging problem for artificial agents. Cognitive autonomous robots coping with this problem must solve two essential tasks: 1) understand the environment in terms of what may happen and how I can deal with this and 2) learn successful experiences for their further use in an automatic subconscious way.

Trigeminal intersubnuclear neurons: morphometry and input-dependent structural plasticity in adult rats.

Intersubnuclear neurons in the caudal division of the spinal trigeminal nucleus that project to the principal nucleus (Pr5) play an active role in shaping the receptive fields of other neurons, at different levels in the ascending sensory system that processes information originating from the vibrissae. By using retrograde labeling and digital reconstruction, we investigated the morphometry and topology of the dendritic trees of these neurons and the changes induced by long-term experience-dependent plasticity in adult male rats. Primary afferent input was either eliminated by transection of the right infraorbital nerve (IoN), or selectively altered by repeated whisker clipping on the right side.

Wave-processing of long-scale information by neuronal chains.

Investigation of mechanisms of information handling in neural assemblies involved in computational and cognitive tasks is a challenging problem. Synergetic cooperation of neurons in time domain, through synchronization of firing of multiple spatially distant neurons, has been widely spread as the main paradigm. Complementary, the brain may also employ information coding and processing in spatial dimension.

Alteration and reorganization of functional networks: a new perspective in brain injury study.

Plasticity is the mechanism underlying the brain's potential capability to compensate injury. Recently several studies have shown how functional connections among the brain areas are severely altered by brain injury and plasticity leading to a reorganization of the networks. This new approach studies the impact of brain injury by means of alteration of functional interactions.

Compact internal representation of dynamic situations: neural network implementing the causality principle.

Animals for survival in complex, time-evolving environments can estimate in a "single parallel run" the fitness of different alternatives. Understanding of how the brain makes an effective compact internal representation (CIR) of such dynamic situations is a challenging problem. We propose an artificial neural network capable of creating CIRs of dynamic situations describing the behavior of a mobile agent in an environment with moving obstacles.