Emotional simulations and depression diagnostics

Jordi Vallverdu Segura, Max Talanov, Jordi Vallverdú, Bin Hu, Philip Moore, Alexander Toschev, Diana Shatunova, Anzhela Maganova, Denis Sedlenko, Alexey Leukhin

Research output: Contribution to journalArticleResearchpeer-review

5 Citations (Scopus)


© 2016 Elsevier B.V. All rights reserved. In this work we propose the following hypothesis: the neuromodulatory mechanisms that control the emotional states of mammals can be translated and re-implemented in a computer by controlling the computational performance of a hosted computational system. In our specific implementation, we represent the simulation of the 'fear-like' state based on the three dimensional neuromodulatory model of affects, in this paper 'affects' refer to the basic emotional inborn states, inherited from works of Hugo Lövheim. Whilst dopamine controls attention, serotonin is the key for inhibition, and fear is a elicitator for inhibitory and protective processes. This inhibition can promote [in a cognitive system] to blocking behaviour which can be labelled as 'depression'. Therefore, our interest is how to reimplement biomimetically both action-regulators without the computational system to resulting in a 'failed' scenario. We have simulated 1000 ms of the dopamine system using NEST Neural Simulation Tool with the rat brain as the model. The results of the simulation experiments are reported with an evaluation to demonstrate the correctness of our hypothesis.
Original languageEnglish
Pages (from-to)41-50
JournalBiologically Inspired Cognitive Architectures
Publication statusPublished - 1 Oct 2016


  • Affective computing
  • Artificial intelligence
  • Dopamine
  • Emotion modelling
  • Fear
  • Neuromodulation
  • Rat brain
  • Serotonin
  • Simulation


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