Neuromorphic hardware databases for exploring structure-function relationships in the brain

Neuromorphic hardware is the term used to describe full custom-designed int egrated circuits, or silicon,chips' that are the product of neuromorphic en gineering-a methodology for the synthesis of biologically inspired elements and systems, such as individual neurons, retinae, cochleas, oculomotor sys tems and central pattern generators. We focus on the implementation of neur ons and networks of neurons, designed to illuminate structure-function rela tionships. Neuromorphic hardware can be constructed with either digital or analogue ci rcuitry or with mixed-signal circuitry-a hybrid of the two. Currently, most examples of this type of hardware are constructed using analogue circuits, in complementary metal-oxide-semiconductor technology. The correspondence between these circuits and neurons, or networks of neurons, can exist at a number of levels. At the lowest level, this correspondence is between membr ane ion channels and field-effect transistors. At higher levels, the corres pondence is between whole conductances and firing behaviour, and filters an d amplifiers, devices found in conventional integrated circuit design. Simi larly, neuromorphic engineers can choose to design Hodgkin-Huxley model neu rons, or reduced models, such as integrate-and-fire neurons. In addition to the choice of level, there is also choice within the design technique itse lf; for example, resistive and capacitive properties of the neuronal membra ne can be constructed with extrinsic devices, or using the intrinsic proper ties of the materials from which the transistors themselves are composed. S o, silicon neurons can be built, with dendritic, somatic and axonal structu res, and endowed with ionic, synaptic and morphological properties. Example s of the structure-function relationships already explored using neuromorph ic hardware include correlation detection and direction selectivity. Establishing a database for this hardware is valuable for two reasons: firs t, independently of neuroscientific motivations, the field of neuromorphic engineering would benefit greatly from a resource in which circuit designs could be stored in a form appropriate for reuse and re-fabrication. Analogu e designers would benefit particularly from such a database, as there are n o equivalents to the algorithmic design methods available to designers of d igital circuits. Second, and more importantly for the purpose of this theme issue, is the possibility of a database of silicon neuron designs replicat ing specific neuronal types and morphologies. In the future, it may be poss ible to use an automated process to translate morphometric data directly in to circuit design compatible formats. The question that needs to be addressed is: what could a neuromorphic hardw are database contribute to the wider neuroscientific community that a conve ntional database could not? One answer is that neuromorphic hardware is exp ected to provide analogue sensory-motor systems for interfacing the computa tional power of symbolic, digital systems with the external, analogue envir onment. It is also expected to contribute to ongoing work in neural-silicon interfaces and prosthetics. Finally, there is a possibility that the use o f evolving circuits, using reconfigurable hardware and genetic algorithms, will create an explosion in the number of designs available to the neurosci ence community. All this creates the need for a database to be established, and it would be advantageous to set about this while the field is relative ly young. This paper outlines a framework for the construction of a neuromo rphic hardware database, for use in the biological exploration of structure -function relationships.