Biomedical subjects

Kwabena Boahen

Publications and source records attributed to Kwabena Boahen.

7 recordsLinked to original sources

PubMed2007

Thermodynamically equivalent silicon models of voltage-dependent ion channels.

We model ion channels in silicon by exploiting similarities between the thermodynamic principles that govern ion channels and those that govern transistors. Using just eight transistors, we replicate--for the first time in silicon--the sigmoidal voltage dependence of activation (or inactivation) and the bell-shaped voltage-dependence of its time constant. We derive equations describing the dynamics of our silicon analog and explore its flexibility by varying various parameters. In addition, we validate the design by implementing a channel with a single activation variable. The design's compactness allows tens of thousands of copies to be built on a single chip, facilitating the study of biologically realistic models of neural computation at the network level in silicon.

Animals↗

PubMed2006

A silicon retina that reproduces signals in the optic nerve.

Prosthetic devices may someday be used to treat lesions of the central nervous system. Similar to neural circuits, these prosthetic devices should adapt their properties over time, independent of external control. Here we describe an artificial retina, constructed in silicon using single-transistor synaptic primitives, with two forms of locally controlled adaptation: luminance adaptation and contrast gain control. Both forms of adaptation rely on local modulation of synaptic strength, thus meeting the criteria of internal control. Our device is the first to reproduce the responses of the four major ganglion cell types that drive visual cortex, producing 3600 spiking outputs in total. We demonstrate how the responses of our device's ganglion cells compare to those measured from the mammalian retina. Replicating the retina's synaptic organization in our chip made it possible to perform these computations using a hundred times less energy than a microprocessor-and to match the mammalian retina in size and weight. With this level of efficiency and autonomy, it is now possible to develop fully implantable intraocular prostheses.

Algorithms↗

PubMed2005

Contrast adaptation in subthreshold and spiking responses of mammalian Y-type retinal ganglion cells.

Retinal ganglion cells adapt their responses to the amplitude of fluctuations around the mean light level, or the "contrast." But, in mammalian retina, it is not known whether adaptation arises exclusively at the level of synaptic inputs or whether there is also adaptation in the process of ganglion cell spike generation. Here, we made intracellular recordings from guinea pig Y-type ganglion cells and quantified changes in contrast sensitivity (gain) using a linear-nonlinear analysis. This analysis allowed us to measure adaptation in the presence of nonlinearities, such as the spike threshold, and to compare adaptation in subthreshold and spiking responses. At high contrast (0.30), relative to low contrast (0.10), gain reduced to 0.82 +/- 0.016 (mean +/- SEM) for the subthreshold response and to 0.61 +/- 0.011 for the spiking response. Thus, there was an apparent reduction in gain between the subthreshold and spiking response of 0.74 +/- 0.013. Control experiments suggested that the above effects could not be explained by an artifact of the intracellular recording conditions: extracellular recordings showed a gain change of 0.58 +/- 0.022. For intracellular recordings, negative current reduced the spike output but did not affect the gain change in the subthreshold response: 0.80 +/- 0.051. Thus, adaptation in the subthreshold response did not require spike-dependent conductances. We conclude that the contrast-dependent gain change in the spiking response can be explained by both a synaptic mechanism, as reflected by responses in the subthreshold potential, and an intrinsic mechanism in the ganglion cell related to spike generation.

Action Potentials↗

PubMed2005

Neuromorphic Microchips.

Compact, efficient electronics based on the brain's neural system could yield implantable silicon retinas to restore vision, as well as robotic eyes and other smart sensors.

Electronics↗

PubMed2004

Optic nerve signals in a neuromorphic chip I: Outer and inner retina models.

We present a novel model for the mammalian retina and analyze its behavior. Our outer retina model performs bandpass spatiotemporal filtering. It is comprised of two reciprocally connected resistive grids that model the cone and horizontal cell syncytia. We show analytically that its sensitivity is proportional to the space-constant-ratio of the two grids while its half-max response is set by the local average intensity. Thus, this outer retina model realizes luminance adaptation. Our inner retina model performs high-pass temporal filtering. It features slow negative feedback whose strength is modulated by a locally computed measure of temporal contrast, modeling two kinds of amacrine cells, one narrow-field, the other wide-field. We show analytically that, when the input is spectrally pure, the corner-frequency tracks the input frequency. But when the input is broadband, the corner frequency is proportional to contrast. Thus, this inner retina model realizes temporal frequency adaptation as well as contrast gain control. We present CMOS circuit designs for our retina model in this paper as well. Experimental measurements from the fabricated chip, and validation of our analytical results, are presented in the companion paper [Zaghloul and Boahen (2004)].

Action Potentials↗

PubMed2004

Optic nerve signals in a neuromorphic chip II: Testing and results.

Seeking to match the brain's computational efficiency, we draw inspiration from its neural circuits. To model the four main output (ganglion) cell types found in the retina, we morphed outer and inner retina circuits into a 96 x 60-photoreceptor, 3.5 x 3.3 mm2, 0.35 microm-CMOS chip. Our retinomorphic chip produces spike trains for 3600 ganglion cells (GCs), and consumes 62.7 mW at 45 spikes/s/GC. This chip, which is the first silicon retina to successfully model inner retina circuitry, approaches the spatial density of the retina. We present experimental measurements showing that the chip's subthreshold current-mode circuits realize luminance adaptation, bandpass spatiotemporal filtering, temporal adaptation and contrast gain control. The four different GC outputs produced by our chip encode light onset or offset in a sustained or transient fashion, producing a quadrature-like representation. The retinomorphic chip's circuit design is described in a companion paper [Zaghloul and Boahen (2004)].

Action Potentials↗

PubMed2003

Different circuits for ON and OFF retinal ganglion cells cause different contrast sensitivities.

The theory of "parallel pathways" predicts that, except for a sign reversal, ON and OFF ganglion cells are driven by a similar presynaptic circuit. To test this hypothesis, we measured synaptic inputs to ON and OFF cells as reflected in the subthreshold membrane potential. We made intracellular recordings from brisk-transient (Y) cells in the in vitro guinea pig retina and show that ON and OFF cells in fact express significant asymmetries in their synaptic inputs. An ON cell receives relatively linear input that modulates a single excitatory conductance; whereas an OFF cell receives rectified input that modulates both inhibitory and excitatory conductances. The ON pathway, blocked by L-AP-4, tonically inhibits an OFF cell at mean luminance and phasically inhibits an OFF cell during a light increment. Our results suggest that basal glutamate release is high at ON but not OFF bipolar terminals, and inhibition between pathways is unidirectional: ON --> OFF. These circuit asymmetries explain asymmetric contrast sensitivity observed in spiking behavior.

Action Potentials↗