A model of autonomous interactions between hippocampus and neocortex driving sleep-dependent memory consolidation

Significance Sleep is known to be an important time for consolidating our memories, with memory systems in the brain replaying recent memories and stabilizing them for long-term storage. We present a computational model that simulates these interactions between hippocampal and neocortical memory systems, providing an account of how they can autonomously produce useful offline learning. We explain how information stored initially in the hippocampus can help to build neocortical representations during sleep, and how the alternation of different stages of sleep across the night can facilitate graceful integration of new information with existing knowledge.

Training for Simulation 1. Each satellite consisted of seven features (five visual parts, a class name, and a code name) of which some were shared across exemplars from a category and others were unique to each exemplar. Satellites were chosen randomly in each training trial and one feature was held out. Features were presented as one-hot inputs on the corresponding input/output feature layer and were held out at a ratio of unique:shared = 99:1 (to better match unique and shared learning speed, unique features need to be queried the vast majority of the time). Each training trial consisted of a minus and plus phase. In the minus phase the satellite was presented to the model with a feature held out and the model generated a prediction for the identity of the feature. In the plus phase, the full satellite was presented to the model. The model was trained to a criterion of 66% feature completion performance for shared and for unique features.
Training for Simulation 2. Each training trial minus phase involved clamping an Env 1 or Env 2 item on the input layer and requiring the model to reproduce that item on the output layer. Each environment had 10 items and each item consisted of seven units. Env 1 and Env 2 item units were chosen such that there was a 2/7 unit overlap between corresponding items from Env 1 and Env 2 (between Item 1 from Env 1 and Item 1 from Env 2, Item 2 from Env 1 and Item 2 from Env 2, and so on). There was no overlap between items from the same environment. The same set of items were used across all initializations of the model. In the plus phase, the item was clamped onto both the input and output layer.
Fig S1. Simulation 1 was run with a simple Hebbian learning rule (weights were updated based on plus phase coactivity exclusively) during sleep instead of Contrastive Hebbian Learning. The learning rate for the Hebbian simulations was lowered such that the average weight change magnitude per sleep trial was approximately matched between the two conditions. We found that the Hebbian learning rule results in substantial forgetting of unique information and less robust increases in shared feature memory.

Fig S2.
We ran a blocked NREM/REM schedule (5 epochs of NREM followed by 5 epochs of REM) to test whether REM can protect Env 1 memories without sleep stage alternations. This condition resulted in catastrophic forgetting of Env 1, indicating that REM cannot repair Env 1 memories after extensive damage and that the alternating schedule is necessary for Env 1 protection.

Fig S3.
We ran each of the three Simulation 2 conditions (NREM only, Alternating NREM/REM, and REM only) without oscillating inhibition. The REM only, oscillations off condition is plotted on top of the REM only condition. Both the NREM only, oscillations off and Alternating NREM/REM, oscillations off conditions are substantially worse at improving Env 2 performance relative to their counterparts with oscillations, indicating that oscillations play an important role in NREM learning. They also lead to substantially more damage to Env 1 performance relative to their counterparts with oscillations. The REM only and REM only, oscillations off conditions are nearly identical and cause neither Env 1 damage nor Env 2 improvement. Fig S4. The Alternating NREM/REM condition was run with oscillating inhibition selectively turned off in either REM or NREM epochs to further clarify the stage-specific contributions of oscillating inhibition to performance under alternating stages. Both conditions cause more damage to Env 1 performance relative to the alternating condition with oscillations. When NREM oscillations are turned off, there is substantially less Env 2 performance improvement. These simulations reveal that oscillations in both sleep stages facilitate better protection of Env 1, while oscillations in NREM facilitate better Env 2 learning (consistent with Fig. S3