Persistence of neuronal representations
Science 23 Aug 2019: Vol. 365, Issue 6455, pp. 821-825
Persistence of neuronal representations through time and damage in the hippocampus
Walter G. Gonzalez, et.al.
Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA.
[paraphrase]
How do neurons encode long-term memories? Bilateral imaging of neuronal activity in
the mouse hippocampus reveals that, from one day to the next, ~40% of neurons change
their responsiveness to cues, but thereafter only 1% of cells change per day. Despite
these changes, neuronal responses are resilient to a lack of exposure to a previously
completed task or to hippocampus lesions. Unlike individual neurons, the responses of
which change after a few days, groups of neurons with inter- and intrahemispheric
synchronous activity show stable responses for several weeks. The likelihood that a
neuron maintains its responsiveness across days is proportional to the number of
neurons with which its activity is synchronous. Information stored in individual neurons
is relatively labile, but it can be reliably stored in networks of synchronously active
neurons.
Memories are processed and stored by complex networks of neurons across several circuits
in the brain; however, little is known about how stable information is encoded in these
neurons. The hippocampus plays an essential role in the formation of memories, and neurons
in this brain area show robust responses to space (via place cells) or other cues relevant to the
task. Neuronal activity in the hippocampus changes during learning and reexposure to an
environment. However, what aspects of neuronal activity in the hippocampus persist during
future visits to a familiar environment, how information is encoded in groups of neurons, and
how lesions perturb the long-term maintenance of these neuronal patterns remain poorly
understood.
Neuronal representations in the hippocampus change over time as information is transferred
from other brain areas, but how these changes are regulated is unknown. Long-term imaging
of neuronal activity showed that during repeated visits to a familiar environment only 31% of
neurons are active in any given session and 2.8 ± 0.3% are active in all sessions.
To investigate the stability of neuronal representations in CA1, we studied both place cells
and end cells (cells firing during periods of immobility at the ends of the track)
Theoretical models have suggested that groups of neurons with synchronized activity may
form cell assemblies able to encode learned representations for long periods of time. Cell
assemblies that encode task-dependent cues have been observed in the hippocampus, but how
these groups of neurons develop during learning, persist across days, or encode stable
information is not known.
Neuron pairs encoded higher information content (as defined by calculation of mutual
information) than individual place or end cells, and the likelihood of a neuron maintaining its
responsiveness to the same or a different field was proportional to its correlation with the
other neuron in the pair.
Do synchronized neurons form part of a larger neuronal network storing stable task
information? Network graphs of correlated neuronal activity showed a clear behavior-
dependent topology, evolving through learning and reorganizing upon transition from the
home cage to the linear track. Graphs revealed dense clusters of neurons with preferences for
specific behaviors or cues.
Synchronization within cell groups was specific to the task performed.
Using graphs, we observed that the likelihood of a neuron to remain a place cell or an end
cell was proportional to the number of graph connections with other neurons. The cell group
participation of a neuron in the graph could also be used to classify a neuron as a place cell,
an end cell, or neither, without the need to observe the animal’s behavior. We could decode
the field centroid location of place and end cells on the basis of the properties of near
neighbors in the graph.
We observed that, although the firing rate changed across sessions and tasks, the majority of
neurons were active on most days.
Altogether, these results support a model with three complementary features. First, neuronal
representations in the hippocampus spontaneously change over time, such that it is rare to
find cells whose fields persist longer than 35 days. Second, attractor-like mechanisms ensure
the persistence of representations over short periods of time (days), even if the animals are
not exposed to the task or if the circuit is perturbed by lesions. Third, our results suggest that
information is dynamically stored in CA1.
Our results show that stable representations of a task are stored in groups of CA1 neurons
with synchronous activity. On the basis of the known connectivity of CA1 neurons, this
synchrony may arise from local inhibition within CA1 or common input from CA3. We
observe that the firing patterns of neurons that are neighbors in a graph can be used to decode
current and future drifts in the response field of a neuron, suggesting that future changes in
place cells are, to some extent, encoded during task performance. Overall, we demonstrate
that activity patterns of individual neurons gradually change over time, whereas the activity
of groups of synchronously active neurons ensures the persistence of representations in CA1.
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