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., the agent's decisions depend on the decisions made by humans that in turn depend on the agent's decisions. To deal with this high-level cognitive skill, we propose a neural network architecture implementing Prediction-for-CompAction paradigm. The network predicts possible human-agent collisions and compacts the time dimension by projecting a given dynamic situation into a static map. Thereby emerging compact cognitive map can be readily used as a "dynamic GPS" for planning actions or mental evaluation of the convenience of cooperation in a given context. We provide numerical evidence that cooperation yields additional room for more efficient navigation in cluttered pedestrian flows, and the agent can choose path to the target significantly shorter than a robot treated by humans as a functional machine. Moreover, the navigation safety, i.e., the chances to avoid accidental collisions, increases under cooperation. Remarkably, these benefits yield no additional load to the mean society effort. Thus, the proposed strategy is socially compliant, and the humanoid agent can behave as "one of us."

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. Though, the biological processes underlying synchronization in the brain are not fully understood there have been reported a variety of mechanisms supporting different types of synchronization both at theoretical and experimental level. One of the more intriguing of these phenomena is the anticipating synchronization, which has been recently reported in a pair of unidirectionally coupled artificial neurons under simple conditions (Pyragiene and Pyragas, 2013), where the slave neuron is able to anticipate in time the behavior of the master one. In this paper, we explore the effect of spike anticipation over the information processing performed by a neural network at functional and structural level. We show that the introduction of intermediary neurons in the network enhances spike anticipation and analyse how these variations in spike anticipation can significantly change the firing regime of the neural network according to its functional and structural properties. In addition we show that the interspike interval (ISI), one of the main features of the neural response associated with the information coding, can be closely related to spike anticipation by each spike, and how synaptic plasticity can be modulated through that relationship. This study has been performed through numerical simulation of a coupled system of Hindmarsh-Rose neurons.

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. These neurons do not display asymmetries between sides in basic metric and topologic parameters (global number of trees, nodes, spines, or dendritic ends), although neurons on the left tend to have longer terminal segments. Ipsilaterally, both deafferentation (IoN transection) and deprivation (whisker trimming) reduced the density of spines, and the former also caused a global increase in total dendritic length and a relative increase in more complex arbors. Contralaterally, deafferentation reduced more complex dendritic trees, and caused a moderate decline in dendritic length and spatial reach, and a loss of spines in number and density. Deprivation caused a similar, but more profound, effect on spines. Our findings provide original quantitative descriptions of a scarcely known cell population, and show that denervation- or deprivation-derived plasticity is expressed not only by neurons at higher levels of the sensory pathways, but also by neurons in key subcortical circuits for sensory processing.