Stability and variability of place cell activity during behavior: functional implications for dynamic coding of spatial information.

In addition to their discharge strongly related to a rat's location in the environment, hippocampal place cells have recently been discovered to carry other more subtle signals. For instance, place cells exhibit overdispersion, i.e., a tendency to have highly variable firing rates across successive passes in the firing field, which may reflect the processing of different classes of cues. In addition, the place cell population tends to fire synchronously during specific phases of place navigation, presumably signaling the animal's arrival at the goal location, or to be reactivated during either sleep or wakefulness following exposure to a new environment, a process thought to be important for memory consolidation. Although these various phenomena are expressed at different timescales, it is very likely that they can occur at the same time during an animal's exposure to a spatial environment. The advantage of such simultaneous processing is that it permits the organism both to be aware of its own location in the environment, and to attend to other environmental features and to store multiple experiences. However its pitfall is that it may result in noisy signals that are difficult to decipher by output structures. Therefore the question is asked of how the information carried by each process can be disentangled. We provide some examples from recent research work showing that this problem is far from being trivial and we propose an explanatory framework in which place cell activity at different timescales could be viewed as a series of dynamic attractors nested within each other.

Automated spike sorting algorithm based on Laplacian eigenmaps and k-means clustering.

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 PCA which combined with k-means had a sorting accuracy of 58% and sorting error of 10%.A correction was made to this article on 22 February 2011. The spacing of the title was amended on the abstract page. No changes were made to the article PDF and the print version was unaffected.

Coding for spatial goals in the prelimbic/infralimbic area of the rat frontal cortex.

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 prelimbic/infralimbic 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 prelimbic/infralimbic 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.

Spatial navigation and hippocampal place cell firing: the problem of goal encoding.

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.