7 Hot Trends In Cognitive Neuroscience:
Obviously oscillations have been around for a long time, but the rapid increase of technological sophistication for direct recordings (see for example high density cortical arrays and deep brain stimulation + recording) coupled with greater availability of MEG (plus rapid advance in MEG source reconstruction and analysis techniques) have placed large-scale neural oscillations at the forefront of cognitive neuroscience. Understanding how different frequency bands interact (e.g. phase coupling) has become a core topic of research in areas ranging from conscious awareness to memory and navigation.
Complex systems, dynamics, and emergence
Again, a concept as old as neuroscience itself, but this one seems to be piggy-backing on several trends towards a new resurgence. As neuroscience grows bored of blobology, and our analysis methods move increasingly towards modelling dynamical interactions (see above) and complex networks, our explanatory metaphors more frequently emphasize brain dynamics and emergent causation. This is a clear departure from the boxological approach that was so prevalent in the 80’s and 90’s.
Direct intervention and causal inference
Pseudo-invasive techniques like transcranial direct-current stimulation are on the rise, partially because they allow us to perform virtual lesion studies in ways not previously possible. Likewise, exponential growth of neurobiological and genetic techniques has ushered in the era of optogenetics, which allows direct manipulation of information processing at a single neuron level. Might this trend also reflect increased dissatisfaction with the correlational approaches that defined the last decade?
You could also include steadily increasing interest in pharmacological neuroimaging under this category.
Computational modelling and reinforcement learning
With the hype surrounding Google’s £200 million acquisition of Deep Mind, and the recent Nobel Prize award for the discovery of grid cells, computational approaches to neuroscience are hotter than ever. Hardly a day goes by without a reinforcement learning or similar paper being published in a glossy high-impact journal. This one takes many forms but it is undeniable that model-based approaches to cognitive neuroscience are all the rage. There is also a clear surge of interest in the Bayesian Brain approach, which could almost have it’s own bullet point. But that would be too self serving.
Gain control is a very basic mechanism found throughout the central nervous system. It can be understood as the neuromodulatory weighting of post-synaptic excitability, and is thought to play a critical role in contextualizing neural processing. Gain control might for example allow a neuron that usually encodes a positive prediction error to ‘flip’ its sign to encode negative prediction error under a certain context. Gain is thought to be regulated via the global interaction of neural modulators (e.g. dopamine, acetylcholine) and links basic information theoretic processes with neurobiology. This makes it a particularly desirable tool for understanding everything from perceptual decision making to basic learning and the stabilization of oscillatory dynamics. Gain control thus links computational, biological, and systems level work and is likely to continue to attract a lot of attention in the near future.
Hierarchies that are not really hierarchies
Neuroscience loves its hierarchies. For example, the Van Essen model of how visual feature detection proceeds through a hierarchy of increasingly abstract functional processes is one of the core explanatory tools used to understand vision in the brain. Currently however there is a great deal of connectomic and functional work pointing out interesting ways in which global or feedback connections can re-route and modulate processes from the ‘top’ directly to the ‘bottom’ or vice versa.
It’s worth noting this trend doesn’t do away with the old notions of hierarchies, but instead just renders them a bit more complex and circular. Put another way, it is currently quite trendy to show ‘the top is the bottom’ and ‘the bottom is the top’. This partially relates to the increased emphasis on emergence and complexity discussed above. A related trend is extension of what counts as the ‘bottom’, with low-level subcortical or even first order peripheral neurons suddenly being ascribed complex abilities typically reserved for cortical processes.
Primary sensations that are not so primary
Closely related to the previous point, there is a clear trend in the perceptual sciences of being increasingly liberal about how ‘primary’ sensory areas really are. I saw this first hand at last year’s Vision Sciences Society which featured at least a dozen posters showing how one could decode tactile shape from V1, or visual frequency from A1, and so on. Again this is probably related to the overall movement towards complexity and connectionism; as we lose our reliance on modularity, we’re suddenly open to a much more general role for core sensory areas.
Interestingly I didn’t include things like multi-modal or high resolution imaging as I think they are still actually emerging and have not quite fully arrived yet. But some of these – computational and connectomic modelling for example – are clearly part and parcel of contemporary zeitgeist. It’s also very interesting to look over this list, as there seems to be a clear trend towards complexity, connectionism, and dynamics. Are we witnessing a paradigm shift in the making? Or have we just forgotten all our first principles and started mangling any old thing we can get published? If it is a shift, what should we call it? Something like ‘computational connectionism’ comes to mind.