Place cell | EPFL Graph Search

A place cell is a kind of pyramidal neuron in the hippocampus that becomes active when an animal enters a particular place in its environment, which is known as the place field. Place cells are thought to act collectively as a cognitive representation of a specific location in space, known as a cognitive map. Place cells work with other types of neurons in the hippocampus and surrounding regions to perform this kind of spatial processing. They have been found in a variety of animals, including rodents, bats, monkeys and humans. Place-cell firing patterns are often determined by stimuli in the environment such as visual landmarks, and olfactory and vestibular stimuli. Place cells have the ability to suddenly change their firing pattern from one pattern to another, a phenomenon known as remapping. This remapping may occur in either some of the place cells or in all place cells at once. It may be caused by a number of changes, such as in the odor of the environment. Place cells are th

A connectionist model of spatial learning in the rat

Thomas Strösslin

When animals explore an environment, they store useful spatial information in their brains. In subsequent visits, they can recall this information and thus avoid dangerous places or find again a food location. This ability, which may be crucial for the animal's survival, is termed "spatial learning". In the late 1940s, theoretical considerations have led researchers to the conclusion that rats establish a "cognitive map" of their environment. This spatial representation can then be used by the animal in order to navigate towards a rewarding location. In 1971, researchers have for the first time found direct evidence that the hippocampus, a brain area in the limbic system, may contain such a cognitive map. The activity of neurons in the hippocampus of rats tends to be highly correlated with the animal's position within the environment. These "place cells" have since been the target of a large body of research. Apart from spatial learning, the hippocampus seems to be involved in a more general type of learning, namely in the formation of so-called "episodic memories". Models of hippocampal function could thus provide valuable insights for the understanding of memory processes in general. Insights from animal navigation could also prove beneficial for the design of autonomous mobile robots. constructing a consistent map of the environment from experience, and using it for solving navigation problems are difficult tasks. Incorporating principles borrowed from animal navigation may help building more robust and autonomous robots. The main objective of this thesis is to develop a neural network model of spatial learning in the rat. The system should be capable of learning how to navigate to a hidden reward location based on realistic sensory input. The system is validated on a mobile robot. Our model consists of several interconnected brain regions, each represented by a population of neurons. The model closely follows experimental results on functional, anatomical and neurophysiological properties of these regions. One population, for instance, models the hippocampal place cells. A head-direction system closely interacts with the place cells and endows the robot with a sense of direction. A population of motor-related cells codes for the direction of the next movement. Associations are learnt between place cells and motor cells in order to navigate towards a goal location. This study allows us to make experimental predictions on functional and neurophysiological properties of the modelled brain regions. In our validation experiments, the robot successfully establishes a spatial representation. The robot can localise itself in the environment and quickly learns to navigate to the hidden goal location.

EPFL2004

Rhythmic Modulation of Theta Oscillations Supports Encoding of Spatial and Behavioral Information in the Rat Hippocampus

Oscillatory patterns of activity in various frequency ranges are ubiquitously expressed in cortical circuits. While recent studies in humans emphasized rhythmic modulations of neuronal oscillations ("second-order" rhythms), their potential involvement in information coding remains an open question. Here, we show that a rhythmic (similar to 0.7 Hz) modulation of hippocampal theta power, unraveled by second-order spectral analysis, supports encoding of spatial and behavioral information. The phase preference of neuronal discharge within this slow rhythm significantly increases the amount of information carried by action potentials in various motor/cognitive behaviors by (1) distinguishing between the spikes fired within versus outside the place field of hippocampal place cells, (2) disambiguating place firing of neurons having multiple place fields, and (3) predicting between alternative future spatial trajectories. This finding demonstrates the relevance of second-order spectral components of brain rhythms for decoding neuronal information.

Elsevier2012

Spatial navigation in geometric mazes

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.

EPFL2007

Spatial navigation in geometric mazes

EPFL2007

A connectionist model of spatial learning in the rat

Thomas Strösslin

EPFL2004