Bifurcation analysis in a silicon neuron

International audienceIn this paper, we describe an analysis of the nonlinear dynamical phenomenon associated with a silicon neuron. Our silicon neuron integrates Hodgkin-Huxley (HH) model formalism, including the membrane voltage dependency of temporal dynamics. Analysis of the bifurcation conditions allow us to identify different regimes in the parameter space that are desirable for biasing our silicon neuron. This approach of studying bifurcations is useful because it is believed that computational properties of neurons are based on the bifurcations exhibited by these dynamical systems in response to some changing stimulus. We describe numerical simulations and measurements of the Hopf bifurcation which is characteristic of class 2 excitability in the HH model. We also show a phenomenon observed in biological neurons and termed excitation block. Hence, by showing that this silicon neuron has similar bifurcations to a certain class of biological neurons, we can claim that the silicon neuron can also perform similar computation

Formation of antiwaves in gap-junction-coupled chains of neurons

Using network models consisting of gap junction coupled Wang-Buszaki neurons, we demonstrate that it is possible to obtain not only synchronous activity between neurons but also a variety of constant phase shifts between 0 and \pi. We call these phase shifts intermediate stable phaselocked states. These phase shifts can produce a large variety of wave-like activity patterns in one-dimensional chains and two-dimensional arrays of neurons, which can be studied by reducing the system of equations to a phase model. The 2\pi periodic coupling functions of these models are characterized by prominent higher order terms in their Fourier expansion, which can be varied by changing model parameters. We study how the relative contribution of the odd and even terms affect what solutions are possible, the basin of attraction of those solutions and their stability. These models may be applicable to the spinal central pattern generators of the dogfish and also to the developing neocortex of the neonatal rat

Neuroelectronic interfacing with cultured multielectrode arrays toward a cultured probe

Efficient and selective electrical stimulation and recording of neural activity in peripheral, spinal, or central pathways requires multielectrode arrays at micrometer scale. ¿Cultured probe¿ devices are being developed, i.e., cell-cultured planar multielectrode arrays (MEAs). They may enhance efficiency and selectivity because neural cells have been grown over and around each electrode site as electrode-specific local networks. If, after implantation, collateral sprouts branch from a motor fiber (ventral horn area) and if they can be guided and contacted to each ¿host¿ network, a very selective and efficient interface will result. Four basic aspects of the design and development of a cultured probe, coated with rat cortical or dorsal root ganglion neurons, are described. First, the importance of optimization of the cell-electrode contact is presented. It turns out that impedance spectroscopy, and detailed modeling of the electrode-cell interface, is a very helpful technique, which shows whether a cell is covering an electrode and how strong the sealing is. Second, the dielectrophoretic trapping method directs cells efficiently to desired spots on the substrate, and cells remain viable after the treatment. The number of cells trapped is dependent on the electric field parameters and the occurrence of a secondary force, a fluid flow (as a result of field-induced heating). It was found that the viability of trapped cortical cells was not influenced by the electric field. Third, cells must adhere to the surface of the substrate and form networks, which are locally confined, to one electrode site. For that, chemical modification of the substrate and electrode areas with various coatings, such as polyethyleneimine (PEI) and fluorocarbon monolayers promotes or inhibits adhesion of cells. Finally, it is shown how PEI patterning, by a stamping technique, successfully guides outgrowth of collaterals from a neonatal rat lumbar spinal cord explant, after six days in cultur

Dynamical laser spike processing

Novel materials and devices in photonics have the potential to revolutionize optical information processing, beyond conventional binary-logic approaches. Laser systems offer a rich repertoire of useful dynamical behaviors, including the excitable dynamics also found in the time-resolved "spiking" of neurons. Spiking reconciles the expressiveness and efficiency of analog processing with the robustness and scalability of digital processing. We demonstrate that graphene-coupled laser systems offer a unified low-level spike optical processing paradigm that goes well beyond previously studied laser dynamics. We show that this platform can simultaneously exhibit logic-level restoration, cascadability and input-output isolation---fundamental challenges in optical information processing. We also implement low-level spike-processing tasks that are critical for higher level processing: temporal pattern detection and stable recurrent memory. We study these properties in the context of a fiber laser system, but the addition of graphene leads to a number of advantages which stem from its unique properties, including high absorption and fast carrier relaxation. These could lead to significant speed and efficiency improvements in unconventional laser processing devices, and ongoing research on graphene microfabrication promises compatibility with integrated laser platforms.Comment: 13 pages, 7 figure