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| Content Provider | frontiers |
|---|---|
| Author | Sherif, Mohamed A. Khalil, Mostafa Z. Shukla, Rammohan Brown, Joshua C. Carpenter, Linda L. |
| Description | IntroductionSynapses 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.MethodsWe 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.ResultsDestroying 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.DiscussionThese findings ... |
| 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 relate 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% distinctly dropped the confidence in the predictions. 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. |
| ISSN | 16640640 |
| DOI | 10.3389/fpsyt.2023.976921 |
| Volume Number | 14 |
| Journal | Frontiers in Psychiatry |
| Language | English |
| Publisher Date | 2023-02-23 |
| Access Restriction | Open |
| Subject Keyword | Prediction error MDD Predictions Ketamine Psilocybin Hierarchical temporal memory (HTM) |
| Content Type | Text |
| Resource Type | Article |
| Subject | Psychiatry and Mental Health |
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