We do not yet fully understand why and how the brain works and which are the underlying essential properties giving rise to a variety of higher cognitive functions, such as perception, memory, attention, decision making, motivation, imagination, creativity, social cooperation, etc.
At present, the available technologies aiming to measure, map, manipulate, or monitor the Spatio-temporal activity of neurons, at various levels, are still limited.
The bottom-up approach aiming to simulate the human brain, starting from a single neuron or from an ensemble of neurons, has had limited success, due to the increasing mathematical and computational complexity, increasing as we pass from the microscopic neuronal scale to the macroscopic cortical network layer scale, as evident from the simulations using software such as NIST, BRIAN, NEURON, etc.
The top-down approach, aiming to understand the cognitive functions of the brain, also encounters its limitations; while aiming to model the abstract features, the microscopic intricacies at neuron level are lost.
There are various computational cognitive architectures that have been developed lately, such as ACT-R, CLARION, OpenCog, etc. These have yielded some task-specific results, but are still not able to simulate general-purpose, constantly learning, self-organizing, and self-healing cognitive machines.
Computational neuroscientists, cognitive scientists, and AI researchers have recently simulated complex data-driven learning machines, which have been claimed to behave as intelligently as human beings. However, these learning machines are still far from simulating higher cognitive functions, such as attention and cognitive control, explicit knowledge acquisition and application, creative tasks, and so on, and they hardly incorporate any computational aspect concerning microcircuits that synergistically create complex human brain functionality.
Another nature-inspired trend is to model and simulate a social brain by using complex networks, to explore the brain’s properties.
These attempts claim good accuracy or performance on carefully created and favorably cleaned data sets, or specific tasks in a structured and controlled environment, these approaches are though too brittle to be generalized across changing situations in real environments.
The purpose of this Research Topic is to review the understanding of the human brain functionalities and compare and contrast this understanding, with the work of brain-related modeling and simulations, underlining gaps, analyzing the current techniques’ weaknesses, and explore new ones.
To develop a cognitive computation framework, we need to study cognitive functionalities mechanisms and model/simulate them, starting from neuronal circuits up to the social brain.
Our focus is on cognitive, computational, and system neuroscience, cognitive science, and formal (mathematical/computational) analysis of brain and cognitive functionalities.
With this perspective in mind, we welcome original research or review works for submission.
Contributions may include, but are not limited to, the following topics:
• Simulating Neuronal Circuits for Higher Brain Functions
• Whole Brain Simulations: Successes and Disappointments
• Experiments with Cognitive Architectures and Other Generic and Global Models
• Modeling and Simulation of Attention, Learning, Memory, Decision Making, and Other Cognitive Functions
• Cognitive Social Simulation
• Models of Language Production and Comprehension
• Computational Models of Emotion, Motivation, Imagination, Creativity, etc.
• Brain-Machine Collaboration for Cognitive Computation
We do not yet fully understand why and how the brain works and which are the underlying essential properties giving rise to a variety of higher cognitive functions, such as perception, memory, attention, decision making, motivation, imagination, creativity, social cooperation, etc.
At present, the available technologies aiming to measure, map, manipulate, or monitor the Spatio-temporal activity of neurons, at various levels, are still limited.
The bottom-up approach aiming to simulate the human brain, starting from a single neuron or from an ensemble of neurons, has had limited success, due to the increasing mathematical and computational complexity, increasing as we pass from the microscopic neuronal scale to the macroscopic cortical network layer scale, as evident from the simulations using software such as NIST, BRIAN, NEURON, etc.
The top-down approach, aiming to understand the cognitive functions of the brain, also encounters its limitations; while aiming to model the abstract features, the microscopic intricacies at neuron level are lost.
There are various computational cognitive architectures that have been developed lately, such as ACT-R, CLARION, OpenCog, etc. These have yielded some task-specific results, but are still not able to simulate general-purpose, constantly learning, self-organizing, and self-healing cognitive machines.
Computational neuroscientists, cognitive scientists, and AI researchers have recently simulated complex data-driven learning machines, which have been claimed to behave as intelligently as human beings. However, these learning machines are still far from simulating higher cognitive functions, such as attention and cognitive control, explicit knowledge acquisition and application, creative tasks, and so on, and they hardly incorporate any computational aspect concerning microcircuits that synergistically create complex human brain functionality.
Another nature-inspired trend is to model and simulate a social brain by using complex networks, to explore the brain’s properties.
These attempts claim good accuracy or performance on carefully created and favorably cleaned data sets, or specific tasks in a structured and controlled environment, these approaches are though too brittle to be generalized across changing situations in real environments.
The purpose of this Research Topic is to review the understanding of the human brain functionalities and compare and contrast this understanding, with the work of brain-related modeling and simulations, underlining gaps, analyzing the current techniques’ weaknesses, and explore new ones.
To develop a cognitive computation framework, we need to study cognitive functionalities mechanisms and model/simulate them, starting from neuronal circuits up to the social brain.
Our focus is on cognitive, computational, and system neuroscience, cognitive science, and formal (mathematical/computational) analysis of brain and cognitive functionalities.
With this perspective in mind, we welcome original research or review works for submission.
Contributions may include, but are not limited to, the following topics:
• Simulating Neuronal Circuits for Higher Brain Functions
• Whole Brain Simulations: Successes and Disappointments
• Experiments with Cognitive Architectures and Other Generic and Global Models
• Modeling and Simulation of Attention, Learning, Memory, Decision Making, and Other Cognitive Functions
• Cognitive Social Simulation
• Models of Language Production and Comprehension
• Computational Models of Emotion, Motivation, Imagination, Creativity, etc.
• Brain-Machine Collaboration for Cognitive Computation