The right hippocampus leads the bilateral integration of gamma-parsed lateralized information

It is unclear whether the two hippocampal lobes convey similar or different activities and how they cooperate. Spatial discrimination of electric fields in anesthetized rats allowed us to compare the pathway-specific field potentials corresponding to the gamma-paced CA3 output (CA1 Schaffer potentials) and CA3 somatic inhibition within and between sides. Bilateral excitatory Schaffer gamma waves are generally larger and lead from the right hemisphere with only moderate covariation of amplitude, and drive CA1 pyramidal units more strongly than unilateral waves. CA3 waves lock to the ipsilateral Schaffer potentials, although bilateral coherence was weak. Notably, Schaffer activity may run laterally, as seen after the disruption of the connecting pathways. Thus, asymmetric operations promote the entrainment of CA3-autonomous gamma oscillators bilaterally, synchronizing lateralized gamma strings to converge optimally on CA1 targets. The findings support the view that interhippocampal connections integrate different aspects of information that flow through the left and right lobes.

Galanin interacts with Neuropeptide Y Y1 receptor in the dentate gyrus of the hippocampus through GALR2/NPYY1R heterodimers

We have shown Galanin(GAL) and Neuropeptide Y Y1(NPYY1) interactions at behavioural, cellular and receptor levels through GALR2/NPYY1R heterodimers in the amygdala. The aim of this work was to analyze GAL/NPYY1R interactions in the Dentate Gyrus(DG) of the Hippocampus, using autoradiographic, in situ hybridization and in situ proximity ligation assay(PLA). Rats(n=6) were sacrificed 15 minutes or 5 hours after icv injections of GAL(3nmol) and DG sections were incubated with NPYY1R agonist [I125]-[Leu31,Pro34]PYY(25 pM) or NPYY1R-33PdATP specific probe, for autoradiography and in situ hybridization respectively. Autoradiograms were analyzed using NIH image analysis system and Student’s unpaired t-test was used. For PLA, DG sections were incubated with anti-GALR2 Rabbit(1:100) and anti-NPYY1R Goat(1:200). PLA signals were detected with PLA PLUS or MINUS probes for rabbit or goat/mouse antibodies. PLA signals were visualized by using a confocal microscope Leica TCS-SL confocal microscope(Leica). We observed that GAL significant increased the NPYY1R agonist [I125]-[Leu31,Pro34]PYY binding in the DG by 20% (p<0,05) and the NPYY1R mRNA expression in the granular layer of DG by 31% (p<0,001). Moreover, PLA-positive red clusters were found specifically in the polymorphic layer and subgranular zone of the DG. No PLA clusters were observed neither in the molecular layer of the DG nor in the corpus callosum, an area that seems to lack of GALR2 receptor. These results demonstrate a novel mechanism of interaction between GAL and NPY1R in the DG at receptor level, probably involving the formation of GALR2/NPYY1R heteroreceptor complexes. Study supported by Junta de Andalucia CVI6476.

A Closed Loop Brain-machine Interface for Epilepsy Control Using Dorsal Column Electrical Stimulation

Although electrical neurostimulation has been proposed as an alternative treatment for drug-resistant cases of epilepsy, current procedures such as deep brain stimulation, vagus, and trigeminal nerve stimulation are effective only in a fraction of the patients. Here we demonstrate a closed loop brain-machine interface that delivers electrical stimulation to the dorsal column (DCS) of the spinal cord to suppress epileptic seizures. Rats were implanted with cortical recording microelectrodes and spinal cord stimulating electrodes, and then injected with pentylenetetrazole to induce seizures. Seizures were detected in real time from cortical local field potentials, after which DCS was applied. This method decreased seizure episode frequency by 44% and seizure duration by 38%. We argue that the therapeutic effect of DCS is related to modulation of cortical theta waves, and propose that this closed-loop interface has the potential to become an effective and semi-invasive treatment for refractory epilepsy and other neurological disorders.

Cordectomía por sustracción pedicular en la columna dorsal y lumbar en fracturas traumáticas: experiencia en el hospital universitario mayor Mederi y revisión sistemática de la literatura

Objetivo: Presentar la experiencia con la técnica de corpectomia por sustracción pedicular en fracturas traumática a nivel de la columna dorsal y Lumbar en el Hospital Universitario Mayor en Bogotá y hacer una revisión sistemática de la literatura de esta técnica quirúrgica. Material y métodos: Se realizó un análisis retrospectivo de las historias clínicas de pacientes que consultaron al servicio de neurocirugía entre los años 2013 y 2015 con fracturas traumáticas a nivel de la columna dorsal y lumbosacra. Se realizó un análisis de déficit neurológico pre y posoperatorio por medio de la Clasificación neurológica estándar de lesión medular (ASIA), al igual que tiempos de cirugía, sangrado intraoperatorio y complicaciones. A su vez se realiza una revisión sistemática de la literatura sobre esta técnica quirúrgica. Resultados: El total de pacientes que se sometieron a cirugía fue de 32, de los cuales el tiempo quirúrgico promedio fue de 396 min, se obtuvo un ASIA prequirúrgico B: 50% C: 47% y E: 3%. El ASIA post operatorio fue de B: 9% C: 47% D: 38% E: 6%. Se obtuvo una mejoría del déficit neurológico en el 75% los pacientes intervenidos. El promedio de sangrado fue de 1,223 cc. Se tuvieron 4 complicaciones, 2 hematomas en lecho quirúrgico que requirió re intervención y dos fistulas de líquido cefalorraquídeo las cuales se manejaron con vendaje compresivo y reposo absoluto. Conclusiones: La corpectomia por sustracción pedicular requiere de un adecuado entrenamiento y un grupo multidisciplinario dentro de los que se incluye neuroanestesiologo, a su vez de neurocirujanos entrenados en columna. Este abordaje presenta grandes beneficios como disminución de tiempos quirúrgicos, disminución de sangrado intraoperatorio y disminución de morbilidad entre otras.

Mapping a suburb with a single camera using a biologically inspired SLAM system

This paper describes a biologically inspired approach to vision-only simultaneous localization and mapping (SLAM) on ground-based platforms. The core SLAM system, dubbed RatSLAM, is based on computational models of the rodent hippocampus, and is coupled with a lightweight vision system that provides odometry and appearance information. RatSLAM builds a map in an online manner, driving loop closure and relocalization through sequences of familiar visual scenes. Visual ambiguity is managed by maintaining multiple competing vehicle pose estimates, while cumulative errors in odometry are corrected after loop closure by a map correction algorithm. We demonstrate the mapping performance of the system on a 66 km car journey through a complex suburban road network. Using only a web camera operating at 10 Hz, RatSLAM generates a coherent map of the entire environment at real-time speed, correctly closing more than 51 loops of up to 5 km in length.

Hippocampal models for simultaneous localisation and mapping on an autonomous robot

To navigate successfully in a novel environment a robot needs to be able to Simultaneously Localize And Map (SLAM) its surroundings. The most successful solutions to this problem so far have involved probabilistic algorithms, but there has been much promising work involving systems based on the workings of part of the rodent brain known as the hippocampus. In this paper we present a biologically plausible system called RatSLAM that uses competitive attractor networks to carry out SLAM in a probabilistic manner. The system can effectively perform parameter self-calibration and SLAM in one dimension. Tests in two dimensional environments revealed the inability of the RatSLAM system to maintain multiple pose hypotheses in the face of ambiguous visual input. These results support recent rat experimentation that suggest current competitive attractor models are not a complete solution to the hippocampal modelling problem.

Simultaneous localisation and mapping from natural landmarks using RatSLAM

This paper describes the current state of RatSLAM, a Simultaneous Localisation and Mapping (SLAM) system based on models of the rodent hippocampus. RatSLAM uses a competitive attractor network to fuse visual and odometry information. Energy packets in the network represent pose hypotheses, which are updated by odometry and can be enhanced or inhibited by visual input. This paper shows the effectiveness of the system in real robot tests in unmodified indoor environments using a learning vision system. Results are shown for two test environments; a large corridor loop and the complete floor of an office building.

Experience mapping : producing spatially continuous environment representations using RatSLAM

RatSLAM is a system for vision-based Simultaneous Localisation and Mapping (SLAM) inspired by models of the rodent hippocampus. The system can produce stable representations of large complex environments during robot experiments in both indoor and outdoor environments. These representations are both topological and metric in nature, and can involve multiple representations of the same place as well as discontinuities. In this paper we describe a new technique known as experience mapping that can be used online with the RatSLAM system to produce world representations known as experience maps. These maps group together multiple place representations and are spatially continuous. A number of experiments have been conducted in simulation and a real world office environment. These experiments demonstrate the high degree to which experience maps are representative of the spatial arrangement of the environment.

Effect of representation size and visual ambiguity on RatSLAM system performance

RatSLAM is a vision-based SLAM system based on extended models of the rodent hippocampus. RatSLAM creates environment representations that can be processed by the experience mapping algorithm to produce maps suitable for goal recall. The experience mapping algorithm also allows RatSLAM to map environments many times larger than could be achieved with a one to one correspondence between the map and environment, by reusing the RatSLAM maps to represent multiple sections of the environment. This paper describes experiments investigating the effects of the environment-representation size ratio and visual ambiguity on mapping and goal navigation performance. The experiments demonstrate that system performance is weakly dependent on either parameter in isolation, but strongly dependent on their joint values.

The implementation of a novel, bio-inspired, robotic security system

The implementation of a robotic security solution generally requires one algorithm to route the robot around the environment and another algorithm to perform anomaly detection. Solutions to the routing problem require the robot to have a good estimate of its own pose. We present a novel security system that uses metrics generated by the localisation algorithm to perform adaptive anomaly detection. The localisation algorithm is a vision-based SLAM solution called RatSLAM, based on mechanisms within the hippocampus. The anomaly detection algorithm is based on the mechanisms used by the immune system to identify threats to the body. The system is explored using data gathered within an unmodified office environment. It is shown that the algorithm successfully reacts to the presence of people and objects in areas where they are not usually present and is tolerised against the presence of people in environments that are usually dynamic.

RatSLAM on the edge : revealing a coherent representation from an overloaded rat brain

The RatSLAM system can perform vision based SLAM using a computational model of the rodent hippocampus. When the number of pose cells used to represent space in RatSLAM is reduced, artifacts are introduced that hinder its use for goal directed navigation. This paper describes a new component for the RatSLAM system called an experience map, which provides a coherent representation for goal directed navigation. Results are presented for two sets of real world experiments, including comparison with the original goal memory system's performance in the same environment. Preliminary results are also presented demonstrating the ability of the experience map to adapt to simple short term changes in the environment.

RatSLAM : a hippocampal model for simultaneous localization and mapping

The work presents a new approach to the problem of simultaneous localization and mapping - SLAM - inspired by computational models of the hippocampus of rodents. The rodent hippocampus has been extensively studied with respect to navigation tasks, and displays many of the properties of a desirable SLAM solution. RatSLAM is an implementation of a hippocampal model that can perform SLAM in real time on a real robot. It uses a competitive attractor network to integrate odometric information with landmark sensing to form a consistent representation of the environment. Experimental results show that RatSLAM can operate with ambiguous landmark information and recover from both minor and major path integration errors.