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

Abstract

Neuromorphic hardware is the term used to describe full custom–designed integrated circuits, or silicon ‘chips’, that are the product of neuromorphic engineering—a methodology for the synthesis of biologically inspired elements and systems, such as individual neurons, retinae, cochleas, oculomotor systems and central pattern generators. We focus on the implementation of neurons and networks of neurons, designed to illuminate structure–function relationships. Neuromorphic hardware can be constructed with either digital or analogue circuitry 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 membrane ion channels and field–effect transistors. At higher levels, the correspondence is between whole conductances and firing behaviour, and filters and amplifiers, devices found in conventional integrated circuit design. Similarly, neuromorphic engineers can choose to design Hodgkin–Huxley model neurons, or reduced models, such as integrate–and–fire neurons. In addition to the choice of level, there is also choice within the design technique itself; for example, resistive and capacitive properties of the neuronal membrane can be constructed with extrinsic devices, or using the intrinsic properties of the materials from which the transistors themselves are composed. So, silicon neurons can be built, with dendritic, somatic and axonal structures, and endowed with ionic, synaptic and morphological properties. Examples of the structure–function relationships already explored using neuromorphic hardware include correlation detection and direction selectivity. Establishing a database for this hardware is valuable for two reasons: first, 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. Analogue designers would benefit particularly from such a database, as there are no equivalents to the algorithmic design methods available to designers of digital circuits. Second, and more importantly for the purpose of this theme issue, is the possibility of a database of silicon neuron designs replicating specific neuronal types and morphologies. In the future, it may be possible to use an automated process to translate morphometric data directly into circuit design compatible formats. The question that needs to be addressed is: what could a neuromorphic hardware database contribute to the wider neuroscientific community that a conventional database could not? One answer is that neuromorphic hardware is expected to provide analogue sensory–motor systems for interfacing the computational power of symbolic, digital systems with the external, analogue environment. It is also expected to contribute to ongoing work in neural–silicon interfaces and prosthetics. Finally, there is a possibility that the use of evolving circuits, using reconfigurable hardware and genetic algorithms, will create an explosion in the number of designs available to the neuroscience 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 relatively young. This paper outlines a framework for the construction of a neuromorphic hardware database, for use in the biological exploration of structure–function relationships.