At the end of the 19th century, while Ramon y Cajal was working out his ‘neuron doctrine’ [4], Charles Sherrington was beginning to identify physiological discontinuities in the flow of information that mediated reflexive behaviors which he ascribed to ‘synapsis’ between axons and their targets [5]. Sherrington’s ideas found a strong anatomical correlate in Cajal’s work on the law of dynamic polarization (Fig. 2). It must have been a great aha moment when the worlds of physiology and neuroanatomy seized upon the idea that they were in fact studying the same thing, synapses, from different perspectives. The idea that physical connectivity of neurons could underlie neural function was the grand synthesis of 20th century neurobiology.
Despite the promising beginnings in anatomy and physiology, the conceptual links between cellular connections and behavior have perhaps not evolved as much as one might have hoped. Many modern findings, in fact, seem to emphasize almost the opposite idea: that anatomical connectivity per se is an inadequate platform to understand an organism’s behavior [6]. For example, the fact that synapses can strengthen or weaken or even be silent; the fact that hormonal and paracrine effects can change a neural circuit’s behavior; and the fact that the behavioral state of an organism can change rapidly all suggest that the wiring diagram is insufficient to get at the physical underpinnings of a functioning brain [7].
But these caveats are not the main reason that synaptic networks have not been intensively studied. Rather, for the most part, such data have just not been available. The principal reason is technical: connectional maps of networks require high resolution imaging over large volumes, a challenging mix [8]. However, it appears that neuroscience is on the cusp of entering a time when direct detailed information about network connectivity will be readily available thanks to recent developments in imaging technologies that reveal neural network organization.
Although connectomics is a nascent field, research is already moving in several different directions. It may be useful to formally divide connectional data mapping into four connectomic categories—projectional, interclass, intraclass, and saturated (Fig. 3)—because these bodies of work are asking quite different kinds of questions, and to some degree require different techniques.
Projectional connectomics
The brain is unlike other organ systems because the principal cells (neurons) specifically interact with a large number of other cells that may be located considerable distances apart (even meters apart in large animals). Thus, it is essential to map the pathways by which neurons in different parts of the brain are connected. Such long-range connections (Fig. 3a) are most easily mapped by methods that can cover large expanses such as magnetic resonance techniques [9] or labeling by axonal transport [10]. In 2005, the term connectome [11] was coined to refer to a proposed complete mapping of the connectivity matrix of the human brain. Progress on ‘The Human Connectome Project’ has provided a framework for integrating many different kinds of human brain imaging data from many different subjects and has resulted in increasingly more detailed parcellation of human brain regions and their connectivity [12]. Importantly, however, the current limiting resolution of techniques that map full human brains is in the range of a cubic millimeter—a trillion-fold larger than the resolution required of the techniques used to generate maps of synaptic connectivity.
Interclass connectomics
The brain is also unlike other organ systems because of the sheer diversity of its cellular components. In many animals the matter of neuronal cell diversity is simplified somewhat because it appears that the same neuron class is used multiple times in a single animal’s nervous system. Not only single cell classes, but also multicellular motifs (Fig. 3b) seem to be used repeatedly. The use of stereotyped cellular ensembles is commonplace in all organs (for example, the renal nephron) where the inherent redundancy of multiple copies of the same ensemble improves functional capacity. In the brain, multiple copies seem to play a different role. In the visual system, for example, the same cellular motifs are duplicated many times over in order to analyze each position in visual space. There is nothing redundant about this duplication (damage to a small part of retina leads to a blind spot). However, learning how visual signals are passed from photoreceptors to their downstream targets in one patch of the retina is often sufficient to explain how such signals are processed throughout most of the retina. There is widespread belief—as yet unproven—that a similarly stereotyped circuit might be in use throughout the cerebral cortex.
One challenge in generating and interpreting cell-type connectome data relates to the cell-type classification process per se. Cells belong to multiple overlapping classes depending, in part, on whether the criterion is functional, structural, or biochemical. While we attempt to create logical frameworks by placing things in separate cubby holes, the actual ‘logic’ of animal evolution requires no such tidy classification structure for a nervous system to do its work. The lines between fixed neuronal categories can be especially blurry when the function of a particular neuron is an emergent property that only manifests itself after a protracted period of development and learning [13].
Intraclass connectomics
Beyond the identification of cell classes and their canonical connectivity is a more subtle problem that is easily seen by considering the connectivity of cerebellar cortex. The cerebellum appears relatively simple: there is only one type of axonal output (from Purkinje cells) and two types of axonal inputs (mossy and climbing fibers). Within the cerebellum there are only a handful of cell types (granule cells, Purkinje cells, and several types of interneurons). The connectivity (in a canonical sense) has been worked out, but both what the cerebellum does exactly and how it does it remain elusive. Why is this? The way the cerebellum works probably depends on how the climbing fibers, parallel fibers, and inhibitory neurons that innervate Purkinje cells are organized. It is not sufficient to know that both classes of axons innervate Purkinje cells. What presumably matters is which particular neurons among each of these classes co-innervate the same Purkinje cell. Understanding this kind of network connectivity is difficult because there may be no intrinsic molecular markers to help discriminate one parallel fiber from any of perhaps hundreds of millions of other parallel fibers in the same cerebellum. Probably some of this connectivity variation is established by the effects of neural activity. Hence, we suspect that it is primarily in the intraclass connectome (Fig. 3c) where one will find the connectional patterns underlying long-term memories.
Saturated connectomics: a digital brain
A single dataset could contain projectional, canonical, and intraclass connectional information (Fig. 3d) if one were willing (and able) to generate a true digital rendering of a brain containing everything down to every last synapse (or even further to every synaptic vesicle). The important point is that a digital brain (Fig. 3e) with a fully saturated wiring diagram is more useful than an actual brain in a critical way: it can be mined forever by virtue of the conversion of tissue into a permanent digital equivalent.