Silicon Brains, Photonics, etc.

The semester is officially over, which is exciting: I finally get to get back into the swing of research (with the occasional GRE study break, of course). As such, blogging will tend to be lighter; but before I lock myself in the lab, here are a few things that came to pass while I was busy finishing up the semester…

Building Brains in Silicon
Among other things, I wrote a paper for my computational neuroscience class on – you guessed it – some really cool work coming out of Kwabena Boahen’s group (formerly here at Penn, now at Stanford) on silicon-based artifical neural systems. This is sometimes classed as ‘neuromorphic engineering’, a term (coined by Carver Mead in the 1980’s) which has come to refer to a relatively recent interdisciplinary paradigm dealing with the development and study of artificial neural systems, drawing on principles from such fields as physics, biology, and computer/electrical engineering to design electronic-based analogues of biological systems. A number of people are using this to try to design new VLSI-based systems based on biological systems.

Some others are trying to reverse this scenario: while ‘real’ neural systems are experimentally studied by neurobiologists while grossly simplified ones are modeled by computational neuroscientists, groups like Boahen’s are trying to bridge these modes of inquiry by exploiting similarities between electronic and neural circuits. Mahowald and Douglas wrote a seminal paper in 1991 describing the first ‘silicon neuron’, and a good deal of work has gone on since then. For example, a number of ‘thermodynamic’ models of ion channels have been developed, building on concepts like Hodgkin/Huxley-type models. Anyway, by exploiting the beautiful similarity between ion channels and metal-oxide-semiconductor (MOS) transistors as two-state systems (simplistically, ion channels are either open or closed, with the energy barrier – and hence the transition rate – between the two states being modulated via, for example, a voltage; on the other hand, a voltage applied across the source and the drain of a MOSFET causes charges to diffuse through the ‘conduction channel’, with the effective barrier to this diffusion being modulated by a gate voltage), Boahen and his graduate student Kai Hynna have recently taken an important step toward ‘building a brain in silicon’. Using an approach that combines the advantages of experiment and artificial modeling, they have developed a simple electronic circuit that replicates the nonlinear dynamics of the gating particles of voltage-dependent ion channels.

References:
- Hynna and Boahen’s recent paper: K. M. Hynna and K. Boahen, Neural Computation 19, 327 (2007).
- 1991 silicon neuron paper: M. Mahowald and R. Douglas, Nature 354, 515 (1991).
- Thermodynamic models of ion channels: A. Destexhe and J. R. Huguenard, J. Comput. Neurosci. 9, 259 (2000).

Update: I guess Tech Review thought this stuff is cool, too: the latest issue has an article on Boahen’s work. It takes a broader view of his work than I have above - I just focused on one particular aspect.

Quasicrystals and Complex Materials as 3D Photonic Structures
I wrote another paper for my modern optics class, based on this recent experimental paper by Man, Megens, Steinhardt and Chaikin on three-dimensional quasicrystals as complete photonic bandgap materials. Here’s the deal: since Schrödinger’s wave equation and the electromagnetic wave equation are formally similar (neglecting spin statistics), it isn’t all that surprising that a number of analogies exist between electronic waves and light. In particular, electromagnetic waves can propagate in structures of periodic dielectric constant, and interference due to multiple Bragg reflections from these interfaces leads to directional-dependent energy band gaps. A major goal is to try to develop artificial structures to act as complete, omnidirectional photonic bandgap (PBG) crystals with bandgaps in the visible regime (wavelength ~ 400-700nm), and a lot of effort has gone into this. Interestingly, recent innovations in materials science and the study of complex materials – such as quasicrystals (QC), liquid crystals (LCs), and colloidal self-assembly – have breathed new life into this quest.

References:
- Experimental confirmation of the almost-spherical effective Brillouin zone (and hence the potential of developing a 3D PBG structure) of a macroscopic 3D icosahedral photonic QC: W. Man, M. Megens, P. J. Steinhardt and P. M. Chaikin, Nature 436, 993 (2005).
- Experimental approach towards assembling 3D analogues of the QC structures studied by Man et al. on a smaller scale using holographic optical trapping: Y. Roichman and D. G. Grier, Opt. Exp. 13, 5434 (2005).
- Another experimental approach, using a novel 7-beam optical interference holography technique: W. Y. Tam, Appl. Phys. Lett. 89, 251111 (2006).
- Using nematic liquid crystals in ‘inverse opal’ structures as PBG materials (tuned by parameters such as an external electric field) – for example, since liquid crystals are birefringent, modulating their orientational order using a field can influence their optical properties (a principle on which liquid crystal displays are based): K. Busch and S. John, Phys. Rev. Lett. 83, 967 (1998).
- Recent computational work has indicated a feasible method of fabricating 3D visible PBG crystals with two different types of lattice structure using self-assembly of a mixture of colloidal spheres of two different sizes: A. P. Hynninen, J. H. Thijssen, E. C. Vermolen, M. Dijkstra, and A. van Blaaderen, Nature Mater. 6, 202 (2007).