Predictive coding constitutes the brain theory of generation and updating of neural representations of the environment through higher-level information in an attempt to predict lower-level information. Predictive coding is expected to generate sparse and less redundant population activity. More generally, other methods of generating sparse codes for sensory information have been developed. Similarly, population coding constitutes the theory of understanding how neuronal populations encode environmental stimuli and how this combined activation pattern assists in input determination. By identifying what the brain is trying to accomplish e.g. make a decision, the plausible computational steps needed to achieve that, are then reconstructed. Through such theories, we can attempt to further understand how biological and artificial neuronal networks may be organized to help us process sensory information about the world. Consequently, we can deepen our understanding of the interplay between neuronal communication, decision making, input processing, and behavioral outcomes.
Their utility has been implemented across various areas from how visual stimuli are processed to cortical interactions. However, it remains unclear how early models can be adapted to account for the vast scale of the brain’s neuronal networks. Additionally, not every proposed framework and model will have biological plausibility due to the nature of neural connections. Lastly, we have yet to fully uncover which coding and algorithmic strategies are the most suitable and the best at explaining certain brain functionalities. Hence, this Research Topic aims to shed light on the recent computational neuroscience advancements in the fields of population and predictive coding.
We welcome submissions in the form of original research, systematic reviews, method articles, and perspective articles. Areas of focus include but are not limited to:
• Algorithmic Sensory Input Processing
• Deep Network Architectures
• Computational Models/Frameworks of Cortical Interaction and Functioning
• Neural Network Spiking Coding/Decoding
• Applications of models in Clinical/Non-Clinical Populations
Predictive coding constitutes the brain theory of generation and updating of neural representations of the environment through higher-level information in an attempt to predict lower-level information. Predictive coding is expected to generate sparse and less redundant population activity. More generally, other methods of generating sparse codes for sensory information have been developed. Similarly, population coding constitutes the theory of understanding how neuronal populations encode environmental stimuli and how this combined activation pattern assists in input determination. By identifying what the brain is trying to accomplish e.g. make a decision, the plausible computational steps needed to achieve that, are then reconstructed. Through such theories, we can attempt to further understand how biological and artificial neuronal networks may be organized to help us process sensory information about the world. Consequently, we can deepen our understanding of the interplay between neuronal communication, decision making, input processing, and behavioral outcomes.
Their utility has been implemented across various areas from how visual stimuli are processed to cortical interactions. However, it remains unclear how early models can be adapted to account for the vast scale of the brain’s neuronal networks. Additionally, not every proposed framework and model will have biological plausibility due to the nature of neural connections. Lastly, we have yet to fully uncover which coding and algorithmic strategies are the most suitable and the best at explaining certain brain functionalities. Hence, this Research Topic aims to shed light on the recent computational neuroscience advancements in the fields of population and predictive coding.
We welcome submissions in the form of original research, systematic reviews, method articles, and perspective articles. Areas of focus include but are not limited to:
• Algorithmic Sensory Input Processing
• Deep Network Architectures
• Computational Models/Frameworks of Cortical Interaction and Functioning
• Neural Network Spiking Coding/Decoding
• Applications of models in Clinical/Non-Clinical Populations
