Place cells, spatial maps and the population code for memory
Introduction
It is generally accepted that the hippocampus has a fundamental role in the fast encoding of some types of associative long-term memory [1]. Attempts to understand the algorithms by which information is processed in hippocampal neurons began in the late 1950s and early 1960s [2, 3], only a few years after the mnemonic functions of the hippocampus had been uncovered [4]. For a long time, these physiological studies were motivated more by the structural simplicity of this area of cortex than by its potential role in the formation of memory; however, an important milestone was reached when it was reported in the early 1970s that neurons in the hippocampus have strong and reliable behavioural correlates of firing [5, 6]. By far, their most striking correlate was found to be the tendency to fire if, and only if, the subject is in a particular place in its environment [5, 7, 8].
Since their discovery, these ‘place cells’ have provided an important window into the representation of the physical world as discharge patterns of single hippocampal neurons. With the more recent invention of techniques for large-scale recordings in neuronal ensembles [9, 10], place cells have emerged as one of the most powerful tools for studying hippocampal population dynamics during distinct mnemonic operations.
In this review, we show how hippocampal place cells have been recently used as a model system to advance our understanding of how location and memory are represented and computed by the collective activity of cell populations in well-defined neural architectures of the hippocampal formation.
Section snippets
A spatial map outside the hippocampus
For more than three decades, place cells have been considered to be the key elements of a neural map of the spatial environment [5, 7, 11, 12]. The confined spatial nature of the place cell discharge renders it one of the most striking behavioural correlates among cortical neurons, but accumulating evidence has suggested that the functions of place cells extend well beyond a specific role in mapping the physical space [13, 14]. Place cells have been shown to respond to various nonspatial
Attractor networks in the hippocampus
Although important elements of the spatial map have moved out of the hippocampus, place cells are receiving increasing attention as a tool for understanding the dynamics of cell populations during memory processes (e.g. [10]). A key issue is how the brain identifies consistent patterns among never-identical physical inputs at the same time that it successfully prevents interference between patterns with common elements. Recent studies suggest that the solution might be based on attractor
Attractor networks and episodic memory
Several studies have indicated that the hippocampus is a key structure involved in the storage of sequentially organized episodic information [1, 66, 67]. Neuronal network models that allow quantitative estimates of the amount of stored information suggest that the recurrent networks of the hippocampus might have an enormous storage capacity [68], enabling them potentially to represent a very large number of event memories. The extensive number of hippocampal representations makes retrieval
Conclusions
The past two years have witnessed radical advances in our understanding of place cells and their function in spatial representation, navigation and memory. We are seeing the outline of a modularly organized network in the parahippocampal cortices, which might contain many of the algorithms for place computation that were previously thought to be located in the hippocampus. At the same time, the neural underpinnings of recent memory are being disclosed in the hippocampus, much as a result of
References and recommended reading
Papers of particular interest, published within the annual period of review, have been highlighted as:
• of special interest
•• of outstanding interest
Acknowledgements
This research is supported by a Centre of Excellence grant from the Norwegian Research Council. We are grateful to Alessandro Treves for helpful discussion.
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