Despite significant progress, our knowledge of the functioning of the central nervous system still remains scarce to date. A better understanding of its behavior, in either normal or diseased conditions, goes through an increased knowledge of basic mechanisms involved in neuronal function, including at the single-cell level. This has motivated significant efforts for the development of miniaturized sensing devices to monitor neuronal activity with high spatial and signal resolution. One of the main challenges remaining to be addressed in this domain is, however, the ability to create in vitro spatially ordered neuronal networks at low density with a precise control of the cell location to ensure proper monitoring of the activity of a defined set of neurons. Here, we present a novel self-aligned chemical functionalization method, based on a repellant surface with patterned attractive areas, which permits the elaboration of low-density neuronal network down to individual cells with a high control of the soma location and axonal growth. This approach is compatible with complementary metal-oxide-semiconductor line technology at a wafer scale and allows performing the cell culture on packaged chip outside microelectronics facilities. Rat cortical neurons were cultured on such patterned surfaces for over one month and displayed a very high degree of organization in large networks. Indeed, more than 90% of the network nodes were settled by a soma and 100% of the connecting lines were occupied by a neurite, with a very good selectivity (low parasitic cell connections). After optimization, networks composed of 75% of unicellular nodes were obtained, together with a control at the micron scale of the location of the somas. Finally, we demonstrated that the dendritic neuronal growth was guided by the surface functionalization, even when micrometer scale topologies were encountered and we succeeded to control the extension growth along one-dimensional-aligned nanostructures with sub-micrometrical scale precision. This novel approach now opens the way for precise monitoring of neuronal network activity at the single-cell level.