Place cells are hippocampal neurons whose discharge is strongly related to a rat's location in its environment. The existence of place cells has led to the proposal that they are part of an integrated neural system dedicated to spatial navigation, an idea supported by the discovery of strong relationships between place cell activity and spatial problem solving. To further understand such relationships, we examined the discharge of place cells recorded while rats solved a place navigation task. We report that, in addition to having widely distributed firing fields, place cells also discharge selectively while the hungry rat waits in an unmarked goal location to release a food pellet. Such firing is not duplicated in other locations outside the main firing field even when the rat's behavior is constrained to be extremely similar to the behavior at the goal. We therefore propose that place cells provide both a geometric representation of the current environment and a reflection of the rat's expectancy that it is located correctly at the goal. This on-line feedback about a critical aspect of navigational performance is proposed to be signaled by the synchronous activity of the large fraction of place cells active at the goal. In combination with other (prefrontal) cells that provide coarse encoding of goal location, hippocampal place cells may therefore participate in a neural network allowing the rat to plan accurate trajectories in space.

Finding one's way in space requires a distributed neural network to support accurate spatial navigation. In the rat, this network likely includes the hippocampus and its place cells. Although such cells allow the organism to locate itself in the environment, an additional mechanism is required to specify the animal's goal. Here, we show that firing activity of neurons in medial prefrontal cortex (mPFC) reflects the motivational salience of places. We recorded mPFC neurons from rats performing a place navigation task, and found that a substantial proportion of cells in the prelimbic͞infralimbic area had place fields. A much smaller proportion of cells with such properties was found in the dorsal anterior cingulate area. Furthermore, the distribution of place fields in prelimbic͞infralimbic cells was not homogeneous: goal locations were overrepresented. Because such locations were spatially dissociated from rewards, we suggest that mPFC neurons might be responsible for encoding the rat's goals, a process necessary for path planning.goal coding ͉ place navigation ͉ unit recording

Place cells are hippocampal neurons whose discharge is strongly related to a rat's location in the environment. The existence of such cells, combined with the reliable impairments seen in spatial tasks after hippocampal damage, has led to the proposal that place cells form part of an integrated neural system dedicated to spatial navigation. This hypothesis is supported by the strong relationships between place cell activity and spatial problem solving, which indicate that the place cell representation must be both functional and in register with the surroundings for the animal to perform correctly in spatial tasks. The place cell system nevertheless requires other essential elements to be competent, such as a component that specifies the overall goal of the animal and computes the path required to take the rat from its current location to the goal. Here, we propose a model of the neural network responsible for spatial navigation that includes goal coding and path selection. In this model, the hippocampal formation allows for place recognition, and stores the set of places that can be accessed from each position in the environment. The prefrontal cortex is responsible for encoding goal location and for route planning. The nucleus accumbens translates paths in neural space into appropriate locomotor activity that moves the animal towards the goal in real space. The complete model assumes that the hippocampal output to nucleus accumbens and prefrontal cortex provides information for generating solutions to spatial problems. In support of this model, we finally present preliminary evidence that the goal representation necessary for path planning might be encoded in the prelimbic/infralimbic region of the medial prefrontal cortex.

This study presents a new automatic spike sorting method based on feature extraction by Laplacian eigenmaps combined with k-means clustering. The performance of the proposed method was compared against previously reported algorithms such as principal component analysis (PCA) and amplitude-based feature extraction. Two types of classifier (namely k-means and classification expectation-maximization) were incorporated within the spike sorting algorithms, in order to find a suitable classifier for the feature sets. Simulated data sets and in-vivo tetrode multichannel recordings were employed to assess the performance of the spike sorting algorithms. The results show that the proposed algorithm yields significantly improved performance with mean sorting accuracy of 73% and sorting error of 10% compared to Principal Component Analysis which combined with k-means had a sorting accuracy of 58% and sorting error of 10%. PACS