Research article Emotional simulations and depression diagnostics Max Talanov b,⇑ , Jordi Vallverdú a , Bin Hu c , Philip Moore c , Alexander Toschev b , Diana Shatunova b , Anzhela Maganova b , Denis Sedlenko b , Alexey Leukhin b a Universitat Autònoma de Barcelona, Catalonia, Spain b Kazan Federal University, Russia c Lanzhou University, China article info Article history: Received 10 June 2016 Revised 22 September 2016 Accepted 25 September 2016 Available online xxxx Keywords: Dopamine Serotonin Fear Artificial intelligence Simulation Rat brain Affective computing Emotion modelling Neuromodulation abstract 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 rep- resent 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 biomimet- ically 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 cor- rectness of our hypothesis. Ó 2016 Elsevier B.V. All rights reserved. 1. Introduction The current rapid developments in neurocognitive sciences and new discoveries related to the core mechanisms of natural intelli- gence have triggered new insights and opportunities in the field of biologically inspired cognitive systems. There are new and reliable data related to a key aspect [previously undervalued or hidden] which builds the entire cognitive processes architecture: emotions (Minsky, 2007). Research has shown that emotions play a significant role in nat- ural intelligence and adaptive behaviour (Damasio, 1999; Picard, Vyzas, & Healey, 2001). Additionally, the intrinsic value of emo- tions in cognitive processes remains undervalued by researchers who take a behaviourist approach to artificial emotions based on basic observable actions; we term this: the ‘skinnerian’ approach to emotions. This approach basically considers the emotional per- formance as epiphenomenalist without considering the deep mech- anisms that are hidden under this black box. This view can be somehow useful for ‘real-time’ emotional detection during Human-Robot Interactions or Human-Computer Interactions. Attempts to design computer emotional architectures have been proposed, consider for example CogAff (Sloman, 1994) or LIDA (Franklin, Madl, D’Mello, & Snaider, 2014) which are architectoni- cally modulatory and whilst they simulate the homeostatic role of emotional mechanisms, they fail to provide an integrative way to implement emotional design into all areas of computational activity. As a departure point of our model, we consider a simple ‘‘fear”, which is necessary to evaluate ‘‘fly-or-fight” actions (Stevenson & Rillich, 2012). Our study focuses on two opposing and complimen- tary neuromodulators: dopamine and serotonin (Daw, Kakade, & Dayan, 2002). Dopamine is related to brain reward processes, whilst serotonin is implied into aversive or inhibitory processes; used in combination we may design a system that manages ‘fly- or-fight’ actions in which several learning procedures could be easily implemented. We argue that our proposal represents a milestone in the cre- ation of a new generation AI intelligence incorporating the capabil- ity to create neuromodulatory architectures which can run over several conceptual models, languages and systems. The posited http://dx.doi.org/10.1016/j.bica.2016.09.002 2212-683X/Ó 2016 Elsevier B.V. All rights reserved. ⇑ Corresponding author. E-mail addresses: max.talanov@gmail.com (M. Talanov), jordi.vallverdu@uab.cat (J. Vallverdú), bh@lzu.edu.cn (B. Hu), ptmbcu@gmail.com (P. Moore), atoschev@kp- fu.ru (A. Toschev), dianashatunova@gmail.com (D. Shatunova), anzhelamagano- va@gmail.com (A. Maganova), denis.sedlenko@gmail.com (D. Sedlenko), alexey. panzer@gmail.com (A. Leukhin). Biologically Inspired Cognitive Architectures xxx (2016) xxx–xxx Contents lists available at ScienceDirect Biologically Inspired Cognitive Architectures journal homepage: www.elsevier.com/locate/bica Please cite this article in press as: Talanov, M., et al. Emotional simulations and depression diagnostics. Biologically Inspired Cognitive Architectures (2016), http://dx.doi.org/10.1016/j.bica.2016.09.002