Head direction maps remain stable despite grid map fragmentation

Areas encoding space in the brain contain both representations of position (place cells and grid cells) and representations of azimuth (head direction cells). Previous studies have already suggested that although grid cells and head direction cells reside in the same brain areas, the calculation of head direction is not dependent on the calculation of position. Here we demonstrate that realignment of grid cells does not affect head direction tuning. We analyzed head direction cell data collected while rats performed a foraging task in a multi-compartment environment (the hairpin maze) vs. an open-field environment, demonstrating that the tuning of head direction cells did not change when the environment was divided into multiple sub-compartments, in the hairpin maze. On the other hand, as we have shown previously (Derdikman et al., 2009), the hexagonal firing pattern expressed by grid cells in the open-field broke down into repeating patterns in similar alleys when rats traversed the multi-compartment hairpin maze. The grid-like firing of conjunctive cells, which express both grid properties and head direction properties in the open-field, showed a selective fragmentation of grid-like firing properties in the hairpin maze, while the head directionality property of the same cells remained unaltered. These findings demonstrate that head direction is not affected during the restructuring of grid cell firing fields as a rat actively moves between compartments, thus strengthening the claim that the head direction system is upstream from or parallel to the grid-place system

Pedestrian Trajectory Prediction with Structured Memory Hierarchies

This paper presents a novel framework for human trajectory prediction based on multimodal data (video and radar). Motivated by recent neuroscience discoveries, we propose incorporating a structured memory component in the human trajectory prediction pipeline to capture historical information to improve performance. We introduce structured LSTM cells for modelling the memory content hierarchically, preserving the spatiotemporal structure of the information and enabling us to capture both short-term and long-term context. We demonstrate how this architecture can be extended to integrate salient information from multiple modalities to automatically store and retrieve important information for decision making without any supervision. We evaluate the effectiveness of the proposed models on a novel multimodal dataset that we introduce, consisting of 40,000 pedestrian trajectories, acquired jointly from a radar system and a CCTV camera system installed in a public place. The performance is also evaluated on the publicly available New York Grand Central pedestrian database. In both settings, the proposed models demonstrate their capability to better anticipate future pedestrian motion compared to existing state of the art.Comment: To appear in ECML-PKDD 201

Challenges for identifying the neural mechanisms that support spatial navigation: the impact of spatial scale.

Spatial navigation is a fascinating behavior that is essential for our everyday lives. It involves nearly all sensory systems, it requires numerous parallel computations, and it engages multiple memory systems. One of the key problems in this field pertains to the question of reference frames: spatial information such as direction or distance can be coded egocentrically-relative to an observer-or allocentrically-in a reference frame independent of the observer. While many studies have associated striatal and parietal circuits with egocentric coding and entorhinal/hippocampal circuits with allocentric coding, this strict dissociation is not in line with a growing body of experimental data. In this review, we discuss some of the problems that can arise when studying the neural mechanisms that are presumed to support different spatial reference frames. We argue that the scale of space in which a navigation task takes place plays a crucial role in determining the processes that are being recruited. This has important implications, particularly for the inferences that can be made from animal studies in small scale space about the neural mechanisms supporting human spatial navigation in large (environmental) spaces. Furthermore, we argue that many of the commonly used tasks to study spatial navigation and the underlying neuronal mechanisms involve different types of reference frames, which can complicate the interpretation of neurophysiological data

Grid Cells Encode Local Positional Information

The brain has an extraordinary ability to create an internal spatial map of the external world [1]. This map-like representation of environmental surroundings is encoded through specific types of neurons, located within the hippocampus and entorhinal cortex, which exhibit spatially tuned firing patterns [2, 3]. In addition to encoding space, these neurons are believed to be related to contextual information and memory [4-7]. One class of such cells is the grid cells, which are located within the entorhinal cortex, presubiculum, and parasubiculum [3, 8]. Grid cell firing forms a hexagonal array of firing fields, a pattern that is largely thought to reflect the operation of intrinsic self-motion-related computations [9-12]. If this is the case, then fields should be relatively uniform in size, number of spikes, and peak firing rate. However, it has been suggested that this is not in fact the case [3, 13]. The possibility exists that local spatial information also influences grid cells, which-if true-would greatly change the way in which grid cells are thought to contribute to place coding. Accordingly, we asked how discriminable the individual fields of a given grid cell are by looking at the distribution of field firing rates and reproducibility of this distribution across trials. Grid fields were less uniform in intensity than expected, and the pattern of strong and weak fields was spatially stable and recurred across trials. The distribution remained unchanged even after arena rescaling, but not after remapping. This suggests that additional local information is being overlaid onto the global hexagonal pattern of grid cells

A flexible component-based robot control architecture for hormonal modulation of behaviour and affect

This document is the Accepted Manuscritpt of a paper published in Proceedings of 18th Annual Conference, TAROS 2017, Guildford, UK, July 19–21, 2017. Under embargo. Embargo end date: 20 July 2018. The final publication is available at Springer via https://link.springer.com/chapter/10.1007%2F978-3-319-64107-2_36. © 2017 Springer, Cham.In this paper we present the foundations of an architecture that will support the wider context of our work, which is to explore the link between affect, perception and behaviour from an embodied perspective and assess their relevance to Human Robot Interaction (HRI). Our approach builds upon existing affect-based architectures by combining artificial hormones with discrete abstract components that are designed with the explicit consideration of influencing, and being receptive to, the wider affective state of the robot

Selective Adaptation in Networks of Heterogeneous Populations: Model, Simulation, and Experiment

Biological systems often change their responsiveness when subject to persistent stimulation, a phenomenon termed adaptation. In neural systems, this process is often selective, allowing the system to adapt to one stimulus while preserving its sensitivity to another. In some studies, it has been shown that adaptation to a frequent stimulus increases the system's sensitivity to rare stimuli. These phenomena were explained in previous work as a result of complex interactions between the various subpopulations of the network. A formal description and analysis of neuronal systems, however, is hindered by the network's heterogeneity and by the multitude of processes taking place at different time-scales. Viewing neural networks as populations of interacting elements, we develop a framework that facilitates a formal analysis of complex, structured, heterogeneous networks. The formulation developed is based on an analysis of the availability of activity dependent resources, and their effects on network responsiveness. This approach offers a simple mechanistic explanation for selective adaptation, and leads to several predictions that were corroborated in both computer simulations and in cultures of cortical neurons developing in vitro. The framework is sufficiently general to apply to different biological systems, and was demonstrated in two different cases

Prolonged dopamine signalling in striatum signals proximity and value of distant rewards

Predictions about future rewarding events have a powerful influence on behaviour. The phasic spike activity of dopamine-containing neurons, and corresponding dopamine transients in the striatum, are thought to underlie these predictions, encoding positive and negative reward prediction errors. However, many behaviours are directed towards distant goals, for which transient signals may fail to provide sustained drive. Here we report an extended mode of reward-predictive dopamine signalling in the striatum that emerged as rats moved towards distant goals. These dopamine signals, which were detected with fast-scan cyclic voltammetry (FSCV), gradually increased or—in rare instances—decreased as the animals navigated mazes to reach remote rewards, rather than having phasic or steady tonic profiles. These dopamine increases (ramps) scaled flexibly with both the distance and size of the rewards. During learning, these dopamine signals showed spatial preferences for goals in different locations and readily changed in magnitude to reflect changing values of the distant rewards. Such prolonged dopamine signalling could provide sustained motivational drive, a control mechanism that may be important for normal behaviour and that can be impaired in a range of neurologic and neuropsychiatric disorders.National Institutes of Health (U.S.) (Grant R01 MH060379)National Parkinson Foundation (U.S.)Cure Huntington’s Disease Initiative, Inc. (Grant A-5552)Stanley H. and Sheila G. Sydney Fun

Parallel Thalamic Pathways for Whisking and Touch Signals in the Rat

In active sensation, sensory information is acquired via movements of sensory organs; rats move their whiskers repetitively to scan the environment, thus detecting, localizing, and identifying objects. Sensory information, in turn, affects future motor movements. How this motor-sensory-motor functional loop is implemented across anatomical loops of the whisker system is not yet known. While inducing artificial whisking in anesthetized rats, we recorded the activity of individual neurons from three thalamic nuclei of the whisker system, each belonging to a different major afferent pathway: paralemniscal, extralemniscal (a recently discovered pathway), or lemniscal. We found that different sensory signals related to active touch are conveyed separately via the thalamus by these three parallel afferent pathways. The paralemniscal pathway conveys sensor motion (whisking) signals, the extralemniscal conveys contact (touch) signals, and the lemniscal pathway conveys combined whisking–touch signals. This functional segregation of anatomical pathways raises the possibility that different sensory-motor processes, such as those related to motion control, object localization, and object identification, are implemented along different motor-sensory-motor loops

Electrophysiological characterization of texture information slip-resistance dependent in the rat vibrissal nerve

<p>Abstract</p> <p>Background</p> <p>Studies in tactile discrimination agree that rats are able to learn a rough-smooth discrimination task by actively touching (whisking) objects with their vibrissae. In particular, we focus on recent evidence of how neurons at different levels of the sensory pathway carry information about tactile stimuli. Here, we analyzed the multifiber afferent discharge of one vibrissal nerve during active whisking. Vibrissae movements were induced by electrical stimulation of motor branches of the facial nerve. We used sandpapers of different grain size as roughness discrimination surfaces and we also consider the change of vibrissal slip-resistance as a way to improve tactile information acquisition. The amplitude of afferent activity was analyzed according to its Root Mean Square value (RMS). The comparisons among experimental situation were quantified by using the information theory.</p> <p>Results</p> <p>We found that the change of the vibrissal slip-resistance is a way to improve the roughness discrimination of surfaces. As roughness increased, the RMS values also increased in almost all cases. In addition, we observed a better discrimination performance in the retraction phase (maximum amount of information).</p> <p>Conclusions</p> <p>The evidence of amplitude changes due to roughness surfaces and slip-resistance levels allows to speculate that texture information is slip-resistance dependent at peripheral level.</p