It is difficult to write about grand challenges in our field without pontificating or pretending to show a degree of certainty in assessing the field that I do not possess. I would rather comment on a few of the issues that particularly worry me. Therefore, this article is just a snapshot of our field now, as I see it, and encourage readers to read it as the opinion of just one of their colleagues. My comments are aimed at Circuit Neuroscience. What exactly is Circuit Neuroscienceµ As stated in the mission statement of Frontiers in Neural Circuits, I follow the definition of Circuit Neuroscience as the understanding of the computational function of neural circuits, linking this function with the circuit micro-structure. Within this field, I will address three different types of challenges: scientific, methodological and sociological ones. I think that it is fair to say that we are profoundly ignorant about the structure and function of neural circuits. One could say that the goal of our field is to reverse-engineer biological circuits and that in order to do so we need to know their structure and logic, so that we can understand their computational algorithms. Like engineering students in their final college exam, we are attempting to decipher the “transfer function” of unknown circuits, this time biological ones. To do so effectively, for most neural circuits in most species, we need to solve the following problems: Cell Types Problem: In terms of the structure of neural circuits, one of the fundamental problems we are facing generally is that we still do not know which is the exact complement of neuronal cell types present. In spite of more than a hundred years of neuroanatomy, this issue is still not resolved and it is difficult to imagine how we could reverse engineer a circuit without knowing the list of its parts. Why are we still ignorant of this listµ Besides the fact that the neuronal cell types don"t come pre-labeled and that they are normally mixed together, anatomical efforts in the past have been essentially qualitative, often without clear criteria to differentiate between cell types. The introduction of quantitative anatomical approaches is greatly helping in discerning among cell types. Also, classifications of neurons are increasingly relying on a multifaceted description of their phenotypes, encompassing not just anatomical, but also electrophysiological and molecular features. Particularly powerful is the generation of transgenic animals were neuronal cell types can be actually pre-labeled genetically. A next logical step would involve the systematic use of multivariate statistics with which to explore this multidimensional space and define most, or all, the cell types present. Towards this goal, common efforts by many laboratories, such as the recent Petilla interneuron nomenclature meeting, could standardize the nomenclature and, together with the new more comprehensive type of data on each neuron type (anatomical, physiological and molecular) and their standardization in databases, lead to a universally agreed parts list of most regions of the CNS. Circuit Connectivity Problem: Another fundamental challenge is to decipher the connectivity diagram of neural circuits, one of the holy grails of Neuroscience. Again, in spite of a century of work, we are still at the beginning of this formidable task. With few exceptions, most brain regions are still the “impenetrable jungles where many investigators have lost themselves” that Cajal wrote about. As discussed below, there is hope that in the near future, technical advances will break open this problem, allowing the description, for the first time, of the basic synaptic microcircuits of at least some regions of the brain. Together with the “cell type” problem, this “circuit connectivity” problem is perhaps the biggest Neuroscience breakthrough that could be solved within our lifetimes, revealing the actual structure of neural circuits. What the genome project was for Molecular Biology, the Circuit Connectivity project could be for Neuroscience. This project could help galvanize public opinion and catalyze funding, and it represents a unique opportunity for younger generations of researchers. Circuit Algorithm Problem. As in Engineering, knowledge of the circuit diagram is just one step, albeit a necessary one, to understand the logic and computational algorithms that are implemented in the circuit. I am afraid that we are completely ignorant of such logic for essentially all neuronal circuits. While this is a sad state of affairs, it is at the same time, virgin territory for future efforts, and for this reason, it is difficult to imagine a more exciting time to work in this field. In this respect, a fundamental problem is to discern general strategies when comparing circuits from different parts of the CNS, or from different species. There is practically zero effort nowadays in comparing circuits, yet is seems that this should be essential not only to provide perspective on any one circuit, but also to help the advances by realizing that similar strategies could be used by different circuits. Although perhaps this could be disregarded as 19th century “armchair science", the close comparison of the similarities and differences in structure and algorithms among species and parts of the brain appears necessary and could lead to powerful insights. Circuit Dynamics Problem. A related issue is the better understanding of the temporal dynamics of biological circuits. There have been many meritorious efforts to figure out this logic or “transfer function", by approaching the function of the neural circuits from the systems level, treating them essentially as a black box. These approaches could yield fundamental insights into the algorithms used by neural circuits, particularly if their function can be described by the “Sherringtonian”...