'''A little over a decade ago, a biologist asked the question “Can a biologist fix a radio?” (Lazebnik). That question framed an amusing yet profound discussion of which methods are most appropriate to understand the inner workings of a system, such as a radio. For the engineer, the answer is straightforward: you trace out the transistors, resistors, capacitors etc., and then draw an electrical circuit diagram. At that point you have understood how the radio works and have sufficient information to reproduce its function. For the biologist, as Lazebnik suggests, the answer is more complicated. You first get a hundred radios, snip out one transistor in each, and observe what happens. Perhaps the radio will make a peculiar buzzing noise that is statistically significant across the population of radios, which indicates that the transistor is necessary to make the sound normal. Or perhaps we should snip out a resistor, and then homogenize it to find out the relative composition of silicon, carbon, etc. We might find that certain compositions correlate with louder volumes, for example, or that if we modify the composition, the radio volume decreases. In the end, we might draw a kind of neat box-and-arrow diagram, in which the antenna feeds to the circuit board, and the circuit board feeds to the speaker, and the microphone feeds to the recording circuit, and so on, based on these empirical studies. The only problem is that this does not actually show how the radio works, at least not in any way that would allow us to reproduce the function of the radio given the diagram. As Lazebnik argues, even though we could multiply experiments to add pieces of the diagram, we still won't really understand how the radio works. To paraphrase Feynman, if we cannot recreate it, then perhaps we have not understood it. ’’’

        – Joshua Brown, Front. Neuro

Researchers record activity of individual neurons, local field potentials, relative spike timings, hemodynamic responses etc. in order to understand the neural substrates of behaviour in model organisms. This style of research has yielded significant results and yet a growing body of research is beginning to question the extent to which these approaches can inform us about the hierarchical organization of the brain. The critics point to the inability of big-data approaches in shedding light on the organization of the brain into well defined modules with specific functions, much like the Arithmetic and Logic Unit(ALU), RAM, adders, registers etc. in the modern computer.
The situation is further complicated by our limited understanding of the entire neural anatomy of mammals. We presently lack an exact map or connectome of the mammalian brain. How do we delineate the various functional hierarchies in this mess of wiring ? And if collecting gargantuan amounts of data just doesn’t cut it, then what ?
One approach comes from computational modelling. The idea is to simulate spiking neural networks that perform deep classification tasks and then performing detailed analysis of the individual nodes of the network to discover common patterns of activity. This approach fillips the need for having the exact connectivity profile of the entire neural network. Again, such an analysis cannot be limited to statistical correlations between specific neurons alone and must include notions of modularity - the idea that on sufficient training, neuronal sectors emerge that specialize at doing certain tasks and should be thought of as differentiated.