Neurobiology: Caught in the act
News and Views
Nature 424, 377 - 378 (24 July 2003); doi:10.1038/424377a
Neurobiology: Caught in the act
YADIN DUDAI
Yadin Dudai is in the Department of Neurobiology, Weizmann Institute of Science, Rehovot 76100, Israel.
e-mail: yadin.dudai@weizmann.ac.il
Researchers may have seen the signature of a memory in the making. In monkeys that learnt to
associate two stimuli, single neurons changed their responses before, during or after learning
became evident.
When Captain Lemuel Gulliver visited Balnibarbi, he was fortunate enough to be allowed into the hall
of Speculative Learning at the Academy of Lagado, where, with great admiration, he watched how the
nuts and bolts of a knowledge machine generated new sentences of great wisdom in real time
1
.
Neurobiologists can only envy him to this day. How nice it would be to watch biological learning
machines in action, and see how experience is converted into a memory trace. In the real world, this is
more easily wished for than done. Writing in Science, however, Wirth and colleagues
2
have moved a
step closer to this goal. Their report is of particular interest because it gives fresh insight into the activity
of the hippocampus -- part of the brain machinery that forms and stores declarative (consciously
accessible) memories, and a focus of attention for experimenters, clinicians and theoreticians alike
3
.
Wirth et al.
2
trained their monkeys in an instrumental conditioning task, in which the subject learns, by
trial-and-error, the impact of its actions on the world
4
. Briefly, the monkey was placed in front of a
computer screen, and each trial started with the animal fixating on a central spot for 0.3 seconds. The
monkey, continuing to fixate, was then presented with four identical visual targets simultaneously,
superimposed for half a second on a complex, coloured visual scene. The background scene, but not the
four targets, then disappeared, and after a delay of 0.7 seconds the fixation spot also disappeared, cueing
the animal to move its eyes towards one of the targets. Only one of these targets was associated with a
reward -- a squirt of fruit juice. An experienced monkey required about a dozen trials to learn the
correct position of the rewarded target in each new scene. On their road to fame, the two monkeys
involved in this study saw a total of 378 new scenes over the course of 18 months, of which they learned
290 as required.
And as the monkeys' eye muscles worked for the juice, so too did the neurons in their hippocampus.
Wirth et al. recorded the electrical activity of these neurons in order to get a handle on their role in
learning. This was made feasible by the fact that the associative task is, on the one hand, not too easy, so
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Neurobiology: Caught in the act
that it takes a monkey some time to master it -- enough time for the experimenter to follow neuronal
dynamics. On the other hand, however, the task is not too difficult, meaning that the monkey can still
succeed while the experimenter 'holds' a given nerve cell (in practice, not more than 30 to 50 minutes).
So this is an example of cross-level analysis, tapping into behavioural and cellular processes
concurrently -- virtually essential for studies that aim to analyse the biology of memory.
Wirth et al.
2
recorded from 145 neurons in total, and found that 89 responded in a scene-specific manner
at one or another phase of the session; of these, 25 changed their activity in close association with the
animal's behavioural learning curve. It is this latter subset, dubbed 'changing cells', that attracted the
authors' attention. Each of the 25 neurons showed robust changes in firing rate at some point following
the presentation of a new scene, but little or no response to a highly familiar scene.
Furthermore, these changing cells fell into two categories. One category consisted of neurons that
showed little or no response when a new background scene was presented or during the delay period of
the task. But they then signalled that the animal had learned the location of the reward-associated target
in that scene by significantly increasing or decreasing their firing rate. This altered activity was
maintained throughout the recording session, suggesting that the cells were engaged in storing
memories, or in monitoring their storage. The second category comprised neurons that responded to new
scenes by either increasing or decreasing their activity relative to the baseline, and then signalled
learning by returning to baseline firing rates. Using analytical tools developed in modelling the response
of cortical neurons
5
, Wirth et al. determined that both types of change reflected a modification of the
selectivity of the neuronal response to learned stimuli compared with new stimuli.
The authors also compared the number of the trial in the recording session (trial 1, trial 2, and so on) in
which behaviourally detected learning occurred, with the number of the trial in which neural activity
changed. The results showed that the change took place in individual cells before, at the same time as, or
after behavioural learning became evident. Wirth et al. took this to mean that hippocampal nerve cells
that subserve the new associative memory were recruited into action progressively.
The hippocampus is part of the mediotemporal lobe of the brain, and Wirth and colleagues' study is not
the first to report experience-dependent alterations in this overall region as a consequence of associative
learning. For example, such phenomena have been seen in the perirhinal cortex
6, 7
. What distinguishes
the new study
2
is the focus on the hippocampus, combined with the improved ability to monitor, in real
time, the changes in activity of individual hippocampal neurons in correlation with the development of
the learned behavioural response.
Aficionados of the hippocampus, learning, or both, might be tempted by the new data to pose additional
questions. For example, predominant theories of learning consider the hippocampus and neocortex to be
complementary memory systems that together subserve the acquisition and consolidation of long-term
memories
8
. So, what is the relationship between the changes seen in the hippocampus and the learning-
correlated changes that have previously been observed in neocortical neurons
9, 10
?
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Neurobiology: Caught in the act
And how do we interpret the observation that the neuronal changes occurred at different time points
relative to the emergence of behavioural learning? Does this, as Wirth et al. suggest, reflect an
incremental learning process -- a continuous time- and experience-dependent recruitment of pieces of
the memory puzzle -- which must cross a certain quantitative threshold to become behaviourally
noticeable? Or are we on the trail of qualitatively different phases in learning, such as the encoding of
'online' information versus its registration as a new memory
4
? Do the cells indeed store the new
memory, or instead only take notice of, or index, memories stored elsewhere? And what are the types of
nerve cells involved and the modulatory inputs they receive from other brain areas?
These and other questions notwithstanding, this study
2
reflects the fact that we are at an interesting stage
in memory research. The paper epitomizes our ability to eavesdrop on neuronal conversations in the
brain. Alas, no matter how skilfully the antennas are operated, without deciphering the semantics of the
language we are unlikely to come up with definitive answers to some of the key questions. Yet the
approach itself is also an attempt to produce a dictionary of neuronal language, and may in due course
generate the tools it needs to succeed.
------------------
References
1. Swift, J. Gulliver's Travels. Part III (Norton, New York, 1727/1970).
2. Wirth, S. et al. Science 300, 1578-1581 (2003). | Article | PubMed | ChemPort |
3. Cohen, N. J. & Eichenbaum, H. Memory, Amnesia, and the Hippocampal System (MIT Press,
Cambridge, Massachusetts, 1995).
4. Dudai, Y. Memory from A to Z: Keywords, Concepts and Beyond (Oxford Univ. Press, 2002).
5. Moody, S. L. et al. J. Neurosci. 18, 399-410 (1998). | PubMed | ChemPort |
6. Erickson, C. A. et al. Nature Neurosci. 3, 1143-1148 (2000). | Article | PubMed | ChemPort |
7. Messinger, A. et al. Proc. Natl Acad. Sci. USA 98, 12239-12244
(2001). | Article | PubMed | ChemPort |
8. McClelland, J. L. et al. Psychol. Rev. 102, 419-457 (1995). | Article | PubMed | ChemPort |
9. Chen, L. L. & Wise, S. P. J. Neurophysiol. 73, 1122-1134 (1995). | PubMed | ChemPort |
10. Assad, W. F. et al. Neuron 21, 1399-1407 (1998). | PubMed |
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