Year: 2017
Type: article
Abstract: Many of us played computer games during our childhood and youth, and some of us still do, while others decided to do something seemingly more useful – like trying to better understand the mammalian brain. Many computer games have a linear progression; individual levels are subdivisions of a larger, more complex world. The practical advantage of having levels is that they divide a game into manageable sections. Upon completion of the easy-to-master entry levels, difficulty and complexity increase and often, prior knowledge and acquired skills are necessary to advance further. A computer game analogy can be applied quite easily to the task of understanding the mammalian brain. Obviously, in this ‘game’, we enter a multiplayer environment; prior knowledge has been and is acquired by many ‘players’ in all areas of neuroscience. While physically coexisting within the same structure, each level presents its own themes, rewards and challenges. In the areas of molecular and cellular, cognitive, developmental, behavioural and clinical neuroscience, ‘players’ progress towards prevailing milestones and research goals. In many cases, it is while we are dealing with the difficulties and specifics that come with our individual entry level that we realize for the first time the complexities and begin to grasp the dimension and complexity of the games’ world map. We find out how little we know as individuals and realize how important it is to join forces with other ‘players’ to cross-link different areas and to link different levels of understanding. This is where theory becomes important. It can provide the quantitative and intellectual framework for linking different areas and different levels of understanding of the nervous system. Models that recapitulate biology on one level can help to generate testable predictions from empirical data and motivate experiments on other levels and in other areas. It can be used, for example, to predict how individual proteins like gap-junction channels may affect the synchrony of networks of many neurons within disease-relevant brain regions such as the basal ganglia. Schwab et al. (2016) use such an approach in this issue and predict that dopamine-regulated gap-junction conductance could be involved in the development of synchrony in the basal ganglia in Parkinson's disease. The spatially localized collective activity of neuronal populations is described by the current-source density. Gratiy et al. (2017) challenge the basic assumptions of the current-source density analysis and show that the extracellular potential is determined not only by the transmembrane currents, but also by extracellular diffusive currents. The authors further estimate the effect of extracellular diffusion in in vivo LFP recordings from the visual cortex and show an effect only at low frequencies. At the single neuron level, empirical data on how ion channels determine the excitability of neurons are robust for some neurons such as the CA1 pyramidal neuron of the hippocampus and detailed biophysical models are already available. The study by Migliore et al. (2016) uses such a full-morphology biophysical model of single neurons to predict the effect of electrical fields, for example those generated by power lines, on neuronal excitability. This theoretical framework could now be extended to the network and cognitive levels to predict potential influences of external electric fields on higher brain functions. One plasticity mechanism that has been extensively integrated into circuit models and models of learning and memory is synaptic modification. While bidirectional changes in synaptic strength have been convincingly demonstrated to occur at many different types of synapses involving different signalling pathways and induction mechanisms in vitro, the relevance of particular synaptic learning rules for learning and memory is still quite unclear. In this issue, Wilmes et al. (2016) simulate how synaptic plasticity of inhibitory synapses may impact the directed flow of information within single neurons and elegantly link the single neuron functional level with the circuit level. Jędrzejewska-Szmek et al. (2016) develop a calcium-based plasticity rule in a model of spiny projection neurons of the striatum. This model integrates calcium dynamics including diffusion, buffering and extrusion to predict the direction of synaptic plasticity. It is a perfect example of how a computational model could link the molecular level (calcium concentration) to network dynamics and memory processes. These levels are also linked computationally by the work of Sweeney & Clopath (2016). They provide a computational approach to a phenomenon, for which the empirical data are clearly not complete, because it is hard to test experimentally in reduced preparations and even harder in vivo. However, the consequences on network organization caused by neurotransmitter diffusion through cellular membranes and the distance-dependent effects on the induction of synaptic plasticity could be of more general relevance for many other networks. Overall, theory is an essential tool for interpreting experiments. It is also allowing us to generate cross-level experimentally testable predictions and thus leads us to greater understanding of brain function. In neuroscience and in computer games, access to new levels often requires learning and progression gained from other levels. Only in rare cases does a single area provide direct access point to many other areas. Understanding the mammalian brain is a ‘game’ too complex to be completed and solved by individual ‘players’. Now, in exciting times where models and data are more and more actively shared between scientists, theory and the computational models that built on it could serve as such a ‘hub’. It will provide individual players with the opportunity to gain access to different levels of functional brain organization and greatly advance our understanding of brain function and dysfunction. We are pleased to say that the European Journal of Neuroscience is Open for Business when it comes to papers in this field.
Source: European Journal of Neuroscience
DOI: 10.1111/ejn.13562
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: bronze
OpenAlex ID: W2600906290