A high-level cognitive process of decision making (DM) among desirable alternatives requires coordination of distinct cortical and subcortical areas. Computational models can be used to understand these processes, but many of the existing ones focus on simulating only one of the many parallel operations. The existing holistic spiking neural model (https://doi.org/10.5281/zenodo.4280963), which addresses the problem of simulation DM will be our starting point to examine emotional arousal on DM.
The aim of this project will be first to extend the existing model with a population representing the neuromodulatory locus coerelus (LC) functions and then to validate the correlation of its activity with the actual data of changes in pupil size collected using the eyetracker during behavioral test.
Participants will have the opportunity to work in a multidisciplinary team focused on several parallel areas: cloud computing, digital medical signal processing, building a spiking neural network model, and validating cognitive theories regarding decision-making and emotional impact. Our model will land on a supercomputer and we will find out if AI can have emotions!
List of materials:
The list of materials is intended to provide inspiration, but learning about these items will help you visualize the topic and get into the project faster.
People after biology or psychology degree:
Aston-Jones G, Cohen JD: An integrative theory of locus coeruleus-norepinephrine function: Adaptive gain and optimal performance Annual Review of Neuroscience (2005). DOI:10.1146/annurev.neuro.28.061604.135709
People with a technical background:
Nengo documentation - https://www.nengo.ai/nengo/,
Mariska E. Kret & Elio E. Sjak-Shie: Preprocessing pupil size data: Guidelines and code. Behavior Research Methods (2019). DOI: 10.3758/s13428-018-1075-y
Researcher role: (1-3) people with bio-med background (you will be a substantive support for technicians; your task will be to be able to define the functions of specific areas of the brain in the decision-making process). Knowledge in the field of statistics will also be appreciated.
DSP Engineer: (1-3) your role will be to digitally process the eye tracker signal. Get ready for a task in the field of filtration or implementation of the blink detection algorithm.
Python Dev: (1-3) your task will be to run the existing model on the cloud, implement the spiking neural network populations and give them functions defined by Researchers. Knowledge of the basics of nengo Python library is required.
Programming language of choice: Python!
Maximal allowed number of team members: 9
Added as an issue for book keeping
Source: https://www.brainhack-krakow.org/projects
Team Leader:
Bartek Król-Józaga / kroljoza@agh.edu.pl / github bartekkrol96
Abstract:
A high-level cognitive process of decision making (DM) among desirable alternatives requires coordination of distinct cortical and subcortical areas. Computational models can be used to understand these processes, but many of the existing ones focus on simulating only one of the many parallel operations. The existing holistic spiking neural model (https://doi.org/10.5281/zenodo.4280963), which addresses the problem of simulation DM will be our starting point to examine emotional arousal on DM.
The aim of this project will be first to extend the existing model with a population representing the neuromodulatory locus coerelus (LC) functions and then to validate the correlation of its activity with the actual data of changes in pupil size collected using the eyetracker during behavioral test.
Participants will have the opportunity to work in a multidisciplinary team focused on several parallel areas: cloud computing, digital medical signal processing, building a spiking neural network model, and validating cognitive theories regarding decision-making and emotional impact. Our model will land on a supercomputer and we will find out if AI can have emotions! List of materials:
The list of materials is intended to provide inspiration, but learning about these items will help you visualize the topic and get into the project faster.
People after biology or psychology degree: Aston-Jones G, Cohen JD: An integrative theory of locus coeruleus-norepinephrine function: Adaptive gain and optimal performance Annual Review of Neuroscience (2005). DOI:10.1146/annurev.neuro.28.061604.135709
People with a technical background: Nengo documentation - https://www.nengo.ai/nengo/, Mariska E. Kret & Elio E. Sjak-Shie: Preprocessing pupil size data: Guidelines and code. Behavior Research Methods (2019). DOI: 10.3758/s13428-018-1075-y
Both groups: Duggins Peter, Krzemiński Dominik, Eliasmith Chris, Wichary Szymon: A spiking neuron model of inferential decision making : urgency, uncertainty, and the speed-accuracy tradeoff. Proceedings of the 42nd Annual Conference of the Cognitive Science Society : developing a mind: learning in humans, animals, and machines (2020). https://ruj.uj.edu.pl/xmlui/bitstream/handle/item/266895/wichary_et-al_a_spiking_neuron_model_of_inferential_decision_making_2020.pdf?sequence=1&isAllowed=y Siddhartha Joshi, Yin Li, Rishi M. Kalwani, Joshua I. Gold: Relationships between Pupil Diameter and Neuronal Activity in the Locus Coeruleus, Colliculi, and Cingulate Cortex. Neuron (2016). DOI: https://doi.org/10.1016/j.neuron.2015.11.028 List of requirements for taking part in the project:
Researcher role: (1-3) people with bio-med background (you will be a substantive support for technicians; your task will be to be able to define the functions of specific areas of the brain in the decision-making process). Knowledge in the field of statistics will also be appreciated.
DSP Engineer: (1-3) your role will be to digitally process the eye tracker signal. Get ready for a task in the field of filtration or implementation of the blink detection algorithm.
Python Dev: (1-3) your task will be to run the existing model on the cloud, implement the spiking neural network populations and give them functions defined by Researchers. Knowledge of the basics of nengo Python library is required.
Programming language of choice: Python! Maximal allowed number of team members: 9