The implication in the paper is that measuring every spike will better enable the discovery of collective phenomena for brains. That is the precise opposite of the discovery and study of collective phenomena in physics. The study of macroscopic behavior, e.g. thermodynamics, came before a detailed understanding of the microscopic dynamics. Statistical mechanics provides bridges to microscopic dynamics in terms of statistical descriptions, not detailed dynamical descriptions. This same is true for other collective phenomena like magnetism, superconductivity and superfluidity, as exemplified by the famous Landau theories. In each case, the phenomena were first discovered at the macroscopic level, studied at the macroscopic level, and even the theoretical framework was established at the macroscopic level; the microscopic measurements and statistical mechanical theories entered at a later stage to refine the understanding already established.
It is unlikely that we will discover analogs of superconductivity or superfluidity in the brain by measuring every spike from every neuron. Analogs already exist and are being already studied at multiple scales of analysis. Animal behavior provides a close analog of the macroscopic behaviors of physical systems, reflecting the collective output of brains that actually matter for the survival of the organism. The study of psychological phenomena in terms of constructs such as memory, attention, language and affect also get at macroscopic properties of nervous system dynamics, and can be studied in their own right somewhat like the Landau theories in physics, although admittedly without the mathematical precision. Collective dynamics of neurons has long been studied in the form of electroencephalography (EEG). Over the last two decades many labs have gathered spiking data simultaneously from dozens to hundreds of neurons. This has not yet led to any tremendous new insight: in fact, much of the dynamics can be captured by the study of correlations between pairs of neurons.
Collective behavior in physics is associated with symmetry principles and conservation laws. For example, sound is a collective motion of fluid molecules. The macroscopic equations of motion of a fluid (the Navier Stokes equations) may be written down as consequences of the conservation of mass and of momentum. Linearization of these equations, gives rise to the wave equation, which describes sound. Note that one does not need to start from the microscopic dynamics of the fluid molecules. What is the nervous system equivalent? Not the symmetry principles important in physics (those still apply, but give you back physical phenomena, for example sound), but so called functional constraints – what the organism must be able to do in order to survive, and what shapes the nervous system through the evolutionary process.
This is related to the “computationalist” perspective, spelled out for the visual system by David Marr among others. This research program starts from the requirements the nervous system faces in order for the organism to survive, and tries to understand the neural circuits and activity from this perspective. “Function shapes form”; the deep principles to understand in physics are the symmetry laws, and in biology they are perhaps engineering principles and evolution. In addition to mapping nervous system architecture, one wants to understand what these principles are as they apply to brains. In order to understand brain dysfunction, one wants to understand the laws of normal function.
Will we get there faster by mapping circuits and physiology or by working on new multi-electrode technology? Ideally, one should not have to choose, as long as effort doesn’t get narrowly focused on conceptually ill-formed goals such as measuring every spike of every neuron (or simulating the human brain without adequate data). Much of this is not news to the practicing neuroscientist, but worth reminding ourselves as we navigate the new landscape of billion-dollar brain projects. Otherwise we risk the fate of naturalist Mr Stapleton as he rushed across the great Grimpen Mire at the conclusion of the Hound of the Baskervilles: even with all his knowledge and expertise, he stepped into a bog, and was not heard of again.