Synapses, predictions, and prediction errors: a neocortical computational study of MDD using the temporal memory algorithm of HTM
OpenAlex – July 03, 2022
Source: OpenAlex
Summary
A compelling **Neuroscience** finding reveals that even a 25% loss of synapses in an **artificial neural network** model of the **neocortex** drastically reduces prediction confidence, even when accurate. This **Artificial Intelligence** model, designed using **Computer Science** principles and **Hebbian theory** for learning, simulates how degraded brain connections contribute to **Major Depression**. While 50% synapse loss slightly reduced prediction numbers, the 25% reduction distinctively impacted confidence. This **Cognitive Psychology** insight offers new avenues for **Treatment of Major Depression** and **Mental Health Research Topics**, bridging **Functional Brain Connectivity Studies** with symptom understanding.
Abstract
Abstract Background Synapses and spines are central in major depressive disorder (MDD) pathophysiology, recently highlighted by ketamine’s and psilocybin’s rapid antidepressant effects. The Bayesian brain and interoception perspectives formalize MDD as being “stuck” in affective states constantly predicting negative energy balance. We examined how synaptic atrophy relates to the predictive function of the neocortex and thus to symptoms, using temporal memory (TM), an unsupervised machine-learning algorithm. TM represents a single neocortical layer, learns in real-time using local Hebbian-learning rules, and extracts and predicts temporal sequences. 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. 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% dropped the confidence in the predictions distinctly. So even though the network was making accurate predictions, the network was no longer confident about these predictions. Conclusions These findings explain how interoceptive cortices could be stuck in limited affective states with high prediction error. Growth of new synapses, e.g., with ketamine and psilocybin, 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, making it possible to understand treatment mechanisms and possibly, develop new treatments. Graphical abstract