| Abstract: | ![]()
Neurocomputational models hold that sparse distributed coding is the most efficient way for hippocampal neurons to encode episodic memories rapidly. We investigated the representation of episodic memory in hippocampal neurons of nine epilepsy patients undergoing intracranial monitoring as they discriminated between recently studied words (targets) and new words (foils) on a recognition test. On average, single units and multiunits exhibited higher spike counts in response to targets relative to foils, and the size of this effect correlated with behavioral performance. Further analyses of the spike-count distributions revealed that (i) a small percentage of recorded neurons responded to any one target and (ii) a small percentage of targets elicited a strong response in any one neuron. These findings are consistent with the idea that in the human hippocampus episodic memory is supported by a sparse distributed neural code.The hippocampus is known to play a fundamental role in declarative memory (1–4), but it is not known how mnemonic information is coded by the activity of individual hippocampal neurons. At least three different coding schemes have been considered: a localist coding scheme, a fully distributed coding scheme, and a sparse distributed coding scheme (5). In a localist coding scheme, an individual neuron (sometimes referred to as a “grandmother cell”) codes only one memory, and each memory is coded by the activity of only one neuron. In a fully distributed coding scheme, each memory is coded instead by a pattern of activity across many hippocampal neurons. Falling between these two extremes is a sparse distributed coding scheme in which each memory is coded by the activity of a small proportion of hippocampal neurons, and each neuron contributes to the representation of only a few memories. Sparse distributed coding has long been hypothesized to be the most efficient way for hippocampal neurons to encode episodic memories (remembering events) in rapid succession without overwriting previously stored memories (6–8).Most prior work concerned with the coding of declarative memory in the human hippocampus has focused on the neural representation of semantic memories (remembering facts), such as memory for famous people or landmarks (9, 10). The results of these studies suggest that long-established semantic memories may be represented by fewer than 1% of neurons in the hippocampus (11). However, neurocomputational theories are concerned with the representation of episodic memories. The purpose of our study was to test predictions of these neurocomputational theories about how episodic memories are represented by neurons of the hippocampus.The representation of episodic memory in the hippocampus typically has been investigated using recognition procedures. In recognition, the task is to discriminate between familiar items presented earlier in the experimental session (targets) and novel items not previously presented (foils). An episodic memory signal is evident when neurons exhibit different levels of activity for targets (old items) vs. foils (new items). The first recognition studies with humans (12, 13) and monkeys (14–16) failed to detect evidence of episodic memory in neurons of the hippocampus, but more recent studies have identified hippocampal neurons that differentiate targets from foils (17–21). However, these studies did not investigate how the representation of individual targets is distributed across neurons of the hippocampus. Instead, the aim was to find cells that distinguish the class of targets from the class of foils.We investigated the representation of individual targets in neurons of the human hippocampus. The participants were nine patients with pharmaco-resistant epilepsy requiring the implantation of intracranial wire electrodes for clinical evaluation and localization of seizure foci for possible surgical resection. Among them, the patients completed a total of 18 recognition memory tasks in which they first studied 32 words and then attempted to distinguish between the 32 targets that had appeared on the study list and 32 foils that had not. Each of the 64 items on the recognition test was presented only once, a format that differs from many other neurophysiology studies that present individual stimuli multiple times to identify neurons with reliable stimulus-specific firing properties. The multiple-presentation method is well-suited to the study of semantic memory (e.g., a neuron that is found to respond reliably to six presentations of the word “baby” likely is responding to its long-established semantic meaning) but is not well-suited to the study of episodic memory. When targets and foils are presented only once on a recognition test, the targets, but not the foils, are represented by an episodic memory formed earlier at the time of learning. Under these conditions, any difference in neural activity associated with targets and foils would indicate episodic memory. Note that, if the test items were presented again, the targets and foils no longer would be clearly differentiated because even the foils would be represented by a recently formed, context-specific episodic memory. Accordingly, instead of using multiple stimulus presentations during the recognition test, we examined the distribution of activity associated with once-presented targets vs. once-presented foils across all recorded neurons. The different coding schemes under consideration here make distinct predictions about the expected distributions of neural activity. |
