Entorhinal mismatch: A model of self-supervised learning in the hippocampus

Abstract

The hippocampal formation displays a wide range of physiological responses to different spatial manipulations of the environment. However, very few attempts have been made to identify core computational principles underlying those hippocampal responses. Here, we capitalize on the observation that the entorhinal-hippocampal complex (EHC) forms a closed loop and projects inhibitory signals "countercurrent" to the trisynaptic pathway to build a self-supervised model that learns to reconstruct its own inputs by error backpropagation. The EHC is then abstracted as an autoencoder, with the hidden layers acting as an information bottleneck. With the inputs mimicking the firing activity of lateral and medial entorhinal cells, our model is shown to generate place cells and to respond to environmental manipulations as observed in rodent experiments. Altogether, we propose that the hippocampus builds conjunctive compressed representations of the environment by learning to reconstruct its own entorhinal inputs via gradient descent.

Keywords: Cognitive Neuroscience; Neural Networks; Systems Neuroscience.

Graphical abstract

Figure 1

Hippocampal model and training paradigm (A) Hippocampal trisynaptic circuit with feedback loop through the EC. (B) Proposed mechanism to learn from the hippocampal loop in the form of a self-supervised model. (C) Input rate maps mimicking the ones from grid cells (MEC) and sensory cells (LEC). See transparent methods. (D) Diagram representation of the model input every time (t) as a function of the spatial location. (E) (Left and center) Loss and mean absolute error (MAE) of the model during learning (epochs). (Right) The correlation between the real input (EC) to the network and the outcome (Pearson-r = 0.998).

Figure 2

Place cell modulation after environmental morphing (A) Example of hidden layers (DG, CA3, CA1) activity resembling the ones of hippocampal place cells. (B) Distribution of number of place fields per cell in the EC input layer (LEC and MEC) and hidden layers. (C) Procedure to study the model response to environmental modifications. (Left) The EC input used to train the model. (Right) Replacing a percentage of the sensory cells rate maps mimics environmental modifications. Environmental modifications of different degrees (levels) could go from slightly distinct (10%) to a completely novel environment (100%). The population vector (PV) at each spatial bin is fed to the model. (D) Modifying the environment leads to an increase of place fields size (mean ± standard deviation) in CA3 and CA1 layers but not in the DG (Barry et al., 2012). (E) The DG layer responds with rate modulation (Leutgeb et al., 2007). (F) Spatial correlation decreases with increased environmental modifications, less drastically for CA layers, as expected from later hippocampal subregions.

Figure 3

Grid cells firing field expansion after environmental modification (A) Hypothesized modulation of grid cell (MEC) reconstruction activity in EC′ after altering 10% LEC input (sensory cells) (see transparent methods). (B) Grid cells' firing field size in response to SPECIFIY MANIPULATION. Px = pixels.

Figure 4

Place field elongation after environmental stretching (A) Procedure to test the model during environmental stretching as in (O’ Keefe and Burgess, 1996). (B) Example cell with a stretched place field after environmental manipulation. (C) Difference in the number of place fields between original and stretched environment, suggesting that more place fields emerge. (D) As in (C), place fields tend to increase their size after environmental stretch. Px = pixels.

Figure 5

Novelty detection and relearning (A) Novelty can be simulated by manipulating the rate maps of the sensory cells (LEC) at specific locations. (B) MSE for the model output (left) and for individual stages (right) showing that error magnitude decreases along the DG to CA1 layers. (C and D) The error increases as the environmental modification increases and is stage specific. (E) Example of novelty detection during navigation showing that the model can detect environmental modifications by monitoring its reconstruction error. By altering the activity of the sensory cells within a portion of the environment (hotspot in the left plot), the model increases its error at that same location (center plot). Thresholding the model's output error allows us to detect modified locations, threshold set to > 0.03. (F) Number of epochs require to XYZ versus the degree of environmental morphing. The number of epochs needed for learning (stabilization) increases with the environmental modification level. Notably, the model converges quicker for largely different, novel environments. (G) The number of epochs needed for learning (stabilization) increases with the environmental modification level. Notably, the model converges quicker for largely different, novel environments.