The author has declared that no competing interests exist.
Experiments in systems neuroscience can be seen as consisting of three steps: (1) selecting the signals we are interested in, (2) probing the system with carefully chosen stimuli, and (3) getting data out of the brain. Here I discuss how emerging techniques in molecular biology are starting to improve these three steps. To estimate its future impact on experimental neuroscience, I will stress the analogy of ongoing progress with that of microprocessor production techniques. These techniques have allowed computers to simplify countless problems; because they are easier to use than mechanical timers, they are even built into toasters. Molecular biology may advance even faster than computer speeds and has made immense progress in understanding and designing molecules. These advancements may in turn produce impressive improvements to each of the three steps, ultimately shifting the bottleneck from obtaining data to interpreting it.
Moore's law has characterized progress in microprocessor techniques (see
Remarkably, the steepness of the curves is worth comparing while the offset on the
Meanwhile, simple computers have gotten cheaper over time. This decrease in costs was so dramatic that many of today's toasters contain microprocessors for time-keeping, switching on or off heat, light feedback of heating state, and the handling of key presses. This makes building toasters simpler and ultimately cheaper. When computers were invented, toasters were certainly not an expected application of computing.
Importantly, the speed of computers has increased dramatically faster than the number of neurons that can be simultaneously recorded
Molecular biology has seen accelerating progress over the last decades. One readily quantifiable cost in molecular biology is that of sequencing DNA. The development of a host of different methods has allowed the cost of sequencing each base pair to dramatically decrease over time (
From a computational perspective, a central objective of neuroscience is to understand how neurons convert their inputs into outputs and collectively produce action based on stimuli and internal processes, such as memory and attention. This leads to what I would call the three central steps of experimental approaches in systems neuroscience. (1) Select the signals that are important for a given neuroscience question. As long as we cannot approach understanding the entire brain at the same time, it is highly useful to select what to stimulate and what to measure. (2) Get stimuli into the brain. To understand what neurons do, inputs need to be defined or known. (3) Get data out of the brain. Only large amounts of data allow meaningful statistical inferences. Virtually all experimental approaches to systems neuroscience can be phrased in these terms.
It is interesting to ponder a few well-known examples. In a typical single-cell visual cortex experiment
Each of these steps has its own criteria for being maximally useful. For the selection step (1), we would like to select all the relevant signals and nothing else. For the stimulation step (2), we want to be able to get in large amounts of well defined data with high information bandwidth and low noise
Each of the currently used approaches has limitations along the three steps. For example, fMRI has a huge number of channels, and can select essentially any brain area. Yet, it seems unlikely that it could be used to record only from certain cell types, such as interneurons
Molecular biology is starting to offer powerful solutions to overcome limitations of the three steps
(1) The selection step. There are many different levels of selection. We might want to select individuals that have certain diseases for which there are genetic markers. In this case, we can select these individuals through genetic tests
The question mark denotes areas where the author expects exciting developments. (A) Methods for selecting where neural signals come from. (B) Methods for stimulating neurons. (C) Methods for reading out the data. See text for detail.
(2) The stimulation step. Neurons are traditionally probed with external stimuli (e.g., images on computer monitors), or through electrical or magnetic stimulation methods. Molecular approaches, though relatively new and less frequently employed, allow several clear advantages. First, they can be utilized far more selectively. For example, specific ligands can selectively activate or inactivate certain cellular mechanisms, including those that only exist in certain types of neurons (
(3) The “data out” step. Getting data out of neurons is traditionally done using either existing signals (as in intrinsic imaging), secondary signals like blood flow (as in fMRI), dyes (as in calcium imaging), or electrical or magnetic recording (as in microelectrodes and MEG signals). Using molecular techniques, it is possible to have neurons express the dye used to monitor them. Molecular approaches promise some advantages due to the possibility of “clean” selection (see above (1)). For example, it is possible to express a dye only in those neurons of interest (
All these developments in molecular biology already make it a major driving force in neuroscience. However, in the same way that exponential growth in computer science has brought us better toasters, I expect that molecular biology will provide refinements of the three steps that are currently hard to imagine. Some past predictions of computer abilities (e.g., robot control) have been rather unimpressive, whereas others have been rather precise (e.g., Moore's law). While I am a computational neuroscientist with limited background in molecular biology, I still want to go out on a limb and make some predictions of developments we may see.
The readout of data is currently done using physics, thin wires, and optics, and it may be expected that molecular approaches, aided by the decay in cost of DNA sequencing, may offer new approaches. I can see two major classes of experimental questions. Connectivity: I want to know how neurons and brain areas are wired up
To solve the connectivity problem, Tony Zador
Lastly, it seems that the step of recording neural activity can also be reduced to DNA sequencing. When a cell divides, it naturally copies its entire DNA using DNA polymerase. The movement of the polymerase along the DNA template could be engineered to be essentially a molecular ticker tape, such that the environment at that point in time is recorded in the DNA sequence (for details on potential molecular implementations, see
Neural activity affects an intracellular concentration. DNA polymerase copies a template with a fidelity that is regulated by an indicator for neural activity. Sequencing thus yields the indicator concentration as a function of time, and therefore the activity.
Molecular biology is making rapid progress at becoming useful for systems neuroscience. So far, there have been outstanding approaches at improving the selection step—many types of neurons can be selected individually. The stimulation step has been affected by techniques that allow impressive precision. Data out is a promising field, and it seems that molecular biology will have its strongest impact if it combines strong solutions to all three steps. From a systems neuroscience perspective, molecular developments are going to produce large amounts of highly relevant information. In the same way that microprocessors made their way into our toasters and made them better and cheaper, we now can see how custom-designed molecular machines may make experiments in system neuroscience cheaper and more powerful. However, it seems important to realize that the development of all these tools has high promise—but ultimately, data does not suffice to understand how we perceive, think, and act. If molecular techniques allow massive amounts of data about the brain to be obtained, the central problem will be how to interpret and make sense of this data, a problem similar to other Omics approaches. Cheaper experiments will lead to massive amounts of data furthering an ongoing shift from obtaining data to interpreting it.