Synapses, predictions, and prediction errors: A neocortical computational study of MDD using the temporal memory algorithm of HTM

Destroying synapses in a machine-learning model of the neocortex reduces the confidence of its predictions before reducing their number. The model, based on the temporal memory algorithm, was trained on random letter sequences representing affective states. Removing 50% of synapses only slightly lowered the number of predictions, but a 25% reduction distinctly dropped prediction confidence. This suggests that in major depressive disorder, synaptic loss in interoceptive cortices could trap the brain in limited affective states with high prediction error. The growth of new synapses, as proposed for ketamine and psilocybin, would allow more confident and futuristic predictions.

Study at a glance

Abstract

Introduction Synapses and spines play a significant role in major depressive disorder (MDD) pathophysiology, recently highlighted by the rapid antidepressant effect of ketamine and psilocybin. According to the Bayesian brain and interoception perspectives, MDD is formalized as being stuck in affective states constantly predicting negative energy balance. To understand how spines and synapses relate to the predictive function of the neocortex and thus to symptoms, we used the temporal memory (TM), an unsupervised machine-learning algorithm. TM models a single neocortical layer, learns in real-time, and extracts and predicts temporal sequences. TM exhibits neocortical biological features such as sparse firing and continuous online learning using local Hebbian-learning rules. Methods We trained a TM model on random sequences of upper-case alphabetical letters, representing sequences of affective states. To model depression, we progressively destroyed synapses in the TM model and examined how that affected the predictive capacity of the network. We found that the number of predictions decreased non-linearly. Results Destroying 50% of the synapses slightly reduced the number of predictions, followed by a marked drop with further destruction. However, reducing the synapses by 25% distinctly dropped the confidence in the predictions. Therefore, even though the network was making accurate predictions, the network was no longer confident about these predictions. Discussion These findings explain how interoceptive cortices could be stuck in limited affective states with high prediction error. Connecting ketamine and psilocybin’s proposed mechanism of action to depression pathophysiology, the growth of new synapses would allow representing more futuristic predictions with higher confidence. To our knowledge, this is the first study to use the TM model to connect changes happening at synaptic levels to the Bayesian formulation of psychiatric symptomatology. Linking neurobiological abnormalities to symptoms will allow us to understand the mechanisms of treatments and possibly, develop new ones.