Check out our highlights from the PLOS Computational Biology October issue:
Functional Connectivity’s Degenerate View of Brain Computation
The human brain is characterized by both the way its neurons are connected (the anatomy) and the way they emit signals to interact (the dynamics). At the typical scale of measurements, anatomy is expected to remain relatively stable over time and reproducible from person to person, while neuronal dynamics propagating in the network can be expected to vary widely. But, paradoxically, patterns of functional connectivity have been found to be reproducible both within and between subjects across several studies, while other studies have featured variable functional connectivity patterns, often in relation to cognitive processes. Guillaume Marrelec and colleagues investigate this unsettled issue using generative models of brain activity.
How Do Efficient Coding Strategies Depend on Origins of Noise in Neural Circuits?
For decades the efficient coding hypothesis has been a guiding principle in determining how neural systems can most efficiently represent their inputs. However, conclusions about whether neural circuits are performing optimally depend on assumptions about the noise sources encountered by neural signals as they are transmitted. Braden Brinkman and colleagues provide a coherent picture of how optimal encoding strategies depend on noise strength, type, location, and correlations.
Forgetting in Reinforcement Learning Links Sustained Dopamine Signals to Motivation
Previous research has shown that forgetting could potentially aid certain forms of learning. In order to better understand how forgetting might aid the pursuit of a goal, Ayaka Kato and Kenji Morita conduct mathematical modeling of brain reward circuits. The model involves a series of decisions that could eventually lead to a goal, and incorporates forgetting in the form of gradual weakening of reward-related connections between neurons.
Header image credit: Shinkai et al.