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BMC Neuroscience

, 16:P146 | Cite as

A biologically based neural network model for decision making

  • Azadeh Hassanejad Nazir
  • Hans Liljenstrom
Open Access
Poster presentation

Keywords

Neural Network Model Cosine Similarity Declarative Memory Rational Attitude Emotional Memory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
We present a neural network model, describing an adaptive decision making process (DM), under varying internal and external contexts [1]. The model includes the three most crucial structures in both emotional and rational aspects of DM: amygdala, orbitofrontal cortex (OFC) and lateral prefrontal cortex (LPFC) [2, 3, 4]. Neural activities in these structures represent emotional attitude, expectancy value, and rules towards the options (Figure 1). The DM is modeled at a level of mesoscopic neurodynamics, using biologically inspired neural networks [5]. Rational and emotional associations are encoded with cell assembly oscillations in all three structures, determining the value of an option, V(opt), as a product of the number of activated cells and the mean frequency and amplitude of their oscillations (Figure 2). A decision is based on the competition among stored patterns, using cosine similarity of the frequency vectors f Open image in new window. The option with highest value will win the competition in each system:
Figure 1

Interactions of the main neural structures in the decision making process: Amygdala, orbitofrontal cortex (OFC) and lateral prefrontal cortex (LPFC). Inputs from sensory modalities and from rational and emotional feedback.

Figure 2

Activity of a cell assembly representing one of possible options. Red and green traces show the activity of the inhibitory neurons and the blue one the activity of all excitatory neurons.

V ( opt ) | s opt | . f opt , A opt = | s opt | . f opt , A opt , opt = 1 , . . . , n Open image in new window

The emotional system is goal directed on the predicted value of the options. Emotional memory of various options are formed and stored in the amygdala. A prediction signal is generated in OFC and an emotional response is measured as a level of satisfaction.

LPFC is a pivotal structure in the rational analysis and is based on habitual DM. Declarative and procedural memories, as the main components of this system are activated by external stimuli. The bidirectional interactions between maintained rules in procedural memory, rational attitudes towards different options, and stored factual information in declarative memory, lead to the selection of an option as a rational decision.

The integration of emotional and rational activities results in a final decision. When an action is executed as a result of a decision its experienced value is compared with the expected one, and stored in memory. Depending on the sign and the magnitude of the prediction error (expected value - real value), the stored emotional and rational attitude might be updated.

The experience of our decisions/choices are learnt and may influence future decisions. For any particular input signal, the final decision could shift, depending on internal and external context. Large delayed rewards have a lower value, compared to small immediate rewards. This fact can be included with the help of a hyperbolic discounting function, which models the exponential reduction of rewards in terms of time.

References

  1. 1.
    Doyay K: Modulators of decision making. Nature Neuroscience. 2008, 11: 410-416.CrossRefGoogle Scholar
  2. 2.
    Whalen PJ, Phelps EA: The Human Amygdala. 2009, The Guilford PressGoogle Scholar
  3. 3.
    Zald D, Rauch S: The Orbitofrontal Cortex. 2006, Oxford University PressCrossRefGoogle Scholar
  4. 4.
    Dixon ML, Christoff KE: The lateral prefrontal cortex and complex value-based learning and decision making. Neurosci Biobehav Rev. 2014, 45C: 9-18.CrossRefGoogle Scholar
  5. 5.
    Liljenström H: Modeling the dynamics of olfactory cortex using simplified network units and realistic architecture. International Journal of Neural Systems. 1991, 2: l-15.CrossRefGoogle Scholar

Copyright information

© Nazir and Liljenstrom 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  1. 1.Department of Energy and TechnologySLUUppsalaSweden
  2. 2.Agora for BiosystemsSigtunaSweden

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