A computational model of parallel navigation systems in rodents

Several studies in rats support the idea of multiple neural systems competing to select the best action for reaching a goal or food location. Locale navigation strategies, necessary for reaching invisible goals, seem to be mediated by the hippocampus and the ventral and dorsomedial striatum whereas taxon strategies, applied for approaching goals in the visual field, are believed to involve the dorsolateral striatum. A computational model of action selection is presented, in which different experts, implementing locale and taxon strategies, compete in order to select the appropriate behavior for the current task. The model was tested in a simulated robot using an experimental paradigm that dissociates the use of cue and spatial informatio

Spatial navigation in geometric mazes:a computational model of rodent behavior

Navigation is defined as the capability of planning and performing a path from the current position towards a desired location. Different types, or strategies, of navigation are used by animals depending on the task they are trying to solve. Visible goals can be approached directly, while navigation to a hidden goal usually requires a memorized representation of relative positions of the goal and surrounding landmarks. Neurophysiological and behavioral experiments on rodents suggest that different brain areas are responsible for the expression of different navigation strategies. Specifically, dorsal striatum has been related to storage and recall of stimulus-response associations underlying simple goal-approaching behaviors, whereas hippocampus is thought to store the spatial representation of the environment. Such a representation is built during an unrewarded spatial exploration and appears to be employed in cases when simple stimulus-response strategies fail. Discovery of neurons with spatially correlated activity, i.e. place cells and grid cells, in the hippocampal formation complements behavioral and lesion data suggesting its role for spatial orientation. The overall objective of this work is to study the neurophysiological mechanisms underlying rodent spatial behavior, in particular those that are responsible for the implementation of different navigational strategies. Special attention is devoted to the question of how various types of sensory cues influence goal-oriented behavior. The model of a navigating rat described in this work is based on functional and anatomical properties of brain regions involved in encoding and storage of space representation and action generation. In particular, place and grid cells are modeled by two interconnected populations of artificial neurons. Together, they form a network for spatial learning, capable of combining different types of sensory inputs to produce a distributed representation of location. Goal-directed actions can be generated in the model via two different neural pathways: the first one drives stimulus-response behavior and associates visual input directly to motor responses; the second one associates motor actions with places and hence depends on the representation of location. The visual input is represented by responses of a large number of orientation-sensitive filters to visual images generated according to the position and orientation of the simulated rat in a virtual three-dimensional world. The model was tested in a large array of tasks designed by analogy to experimental studies on animal behavior. Results of several experimental studies, behavioral as wells as neurophysiological, were reproduced. Based on these results we formulated a hypothesis about the influence that the rat's perception of surrounding environment exerts on goal-oriented behavior. This hypothesis may provide an insight into several issues in animal behavior research that were not addressed by theoretical models until now

Robust self-localisation and navigation based on hippocampal place cells

A computational model of the hippocampal function in spatial learning is presented. A spatial representation is incrementally acquired during exploration. Visual and self-motion information is fed into a network of rate-coded neurons. A consistent and stable place code emerges by unsupervised Hebbian learning between place- and head direction cells. Based on this representation, goal-oriented navigation is learnt by applying a reward-based learning mechanism between the hippocampus and nucleus accumbens. The model, validated on a real and simulated robot, successfully localises itself by recalibrating its path integrator using visual input. A navigation map is learnt after about 20 trials, comparable to rats in the water maze. In contrast to previous works, this system processes realistic visual input. No compass is needed for localisation and the reward-based learning mechanism extends discrete navigation models to continuous space. The model reproduces experimental findings and suggests several neurophysiological and behavioural predictions in the rat. (c) 2005 Elsevier Ltd

Modelling Path Integrator Recalibration Using Hippocampal Place cells

The firing activities of place cells in the rat hippocampus exhibit strong correlations to the animal's location. External (e.g. visual) as well as internal (proprioceptive and vestibular) sensory information take part in controlling hippocampal place fields. Previously it has been observed that when rats shuttle between a movable origin and a fixed target the hippocampus encodes position in two different frames of reference. This paper presents a new model of hippocampal place cells that explains place coding in multiple reference frames by continuous interaction between visual and self-motion information. The model is tested using a simulated mobile robot in a real-world experimental paradigm

Minimal model of strategy switching in the plus-maze navigation task

International audiencePrefrontal cortex (PFC) has been implicated in the ability to switch behavioral strategies in response to changes in reward contingencies. A recent experimental study has shown that separate subpopulations of neurons in the prefrontal cortex were activated when rats switched between allocentric place strategies and egocentric response strategies in the plus maze. In this paper we propose a simple neural-network model of strategy switching, in which the learning of the two strategies as well as learning to select between those strategies is governed by the same temporal-difference (TD) learning algorithm. We show that the model reproduces the experimental data on both behavioral and neural levels. On the basis of our results we derive testable prediction concerning a spatial dynamics of the phasic dopamine signal in the PFC, which is thought to encode reward-prediction error in the TD-learning theory

Spontaneous Reorientation Is Guided by Perceived Surface Distance, Not by Image Matching Or Comparison

Humans and animals recover their sense of position and orientation using properties of the surface layout, but the processes underlying this ability are disputed. Although behavioral and neurophysiological experiments on animals long have suggested that reorientation depends on representations of surface distance, recent experiments on young children join experimental studies and computational models of animal navigation to suggest that reorientation depends either on processing of any continuous perceptual variables or on matching of 2D, depthless images of the landscape. We tested the surface distance hypothesis against these alternatives through studies of children, using environments whose 3D shape and 2D image properties were arranged to enhance or cancel impressions of depth. In the absence of training, children reoriented by subtle differences in perceived surface distance under conditions that challenge current models of 2D-image matching or comparison processes. We provide evidence that children’s spontaneous navigation depends on representations of 3D layout geometry.National Institutes of Health (U.S.) (Grant HD 23103

Contribution of Cerebellar Sensorimotor Adaptation to Hippocampal Spatial Memory

Complementing its primary role in motor control, cerebellar learning has also a bottom-up influence on cognitive functions, where high-level representations build up from elementary sensorimotor memories. In this paper we examine the cerebellar contribution to both procedural and declarative components of spatial cognition. To do so, we model a functional interplay between the cerebellum and the hippocampal formation during goal-oriented navigation. We reinterpret and complete existing genetic behavioural observations by means of quantitative accounts that cross-link synaptic plasticity mechanisms, single cell and population coding properties, and behavioural responses. In contrast to earlier hypotheses positing only a purely procedural impact of cerebellar adaptation deficits, our results suggest a cerebellar involvement in high-level aspects of behaviour. In particular, we propose that cerebellar learning mechanisms may influence hippocampal place fields, by contributing to the path integration process. Our simulations predict differences in place-cell discharge properties between normal mice and L7-PKCI mutant mice lacking long-term depression at cerebellar parallel fibre-Purkinje cell synapses. On the behavioural level, these results suggest that, by influencing the accuracy of hippocampal spatial codes, cerebellar deficits may impact the exploration-exploitation balance during spatial navigation

Spatial Learning and Action Planning in a Prefrontal Cortical Network Model

The interplay between hippocampus and prefrontal cortex (PFC) is fundamental to spatial cognition. Complementing hippocampal place coding, prefrontal representations provide more abstract and hierarchically organized memories suitable for decision making. We model a prefrontal network mediating distributed information processing for spatial learning and action planning. Specific connectivity and synaptic adaptation principles shape the recurrent dynamics of the network arranged in cortical minicolumns. We show how the PFC columnar organization is suitable for learning sparse topological-metrical representations from redundant hippocampal inputs. The recurrent nature of the network supports multilevel spatial processing, allowing structural features of the environment to be encoded. An activation diffusion mechanism spreads the neural activity through the column population leading to trajectory planning. The model provides a functional framework for interpreting the activity of PFC neurons recorded during navigation tasks. We illustrate the link from single unit activity to behavioral responses. The results suggest plausible neural mechanisms subserving the cognitive “insight” capability originally attributed to rodents by Tolman & Honzik. Our time course analysis of neural responses shows how the interaction between hippocampus and PFC can yield the encoding of manifold information pertinent to spatial planning, including prospective coding and distance-to-goal correlates

Out of the box: how bees orient in an ambiguous environment

Dittmar L, Stürzl W, Jetzschke S, Mertes M, Boeddeker N. Out of the box: how bees orient in an ambiguous environment. Animal Behaviour. 2014;89:13-21.How do bees employ multiple visual cues for homing? They could either combine the available cues using a view-based computational mechanism or pick one cue. We tested these strategies by training honeybees, Apis mellifera carnica, and bumblebees, Bombus terrestris, to locate food in one of the four corners of a box-shaped flight arena, providing multiple and also ambiguous cues. In tests, bees confused the diagonally opposite corners, which looked the same from the inside of the box owing to its rectangular shape and because these corners carried the same local colour cues. These 'rotational errors' indicate that the bees did not use compass information inferred from the geomagnetic field under our experimental conditions. When we then swapped cues between corners, bees preferred corners that had local cues similar to the trained corner, even when the geometric relations were incorrect. Apparently, they relied on views, a finding that we corroborated by computer simulations in which we assumed that bees try to match a memorized view of the goal location with the current view when they return to the box. However, when extra visual cues outside the box were provided, bees were able to resolve the ambiguity and locate the correct corner. We show that this performance cannot be explained by view matching from inside the box. Indeed, the bees adapted their behaviour and actively acquired information by leaving the arena and flying towards the cues outside the box. From there they re-entered the arena at the correct corner, now ignoring local cues that previously dominated their choices. All individuals of both species came up with this new behavioural strategy for solving the problem provided by the local ambiguity within the box. Thus both species seemed to be solving the ambiguous task by using their route memory, which is always available during their natural foraging behaviour. (C) 2014 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved