Artificial cerebellum on FPGA: realistic real-time cerebellar spiking neural network model capable of real-world adaptive motor control.

The cerebellum plays a central role in motor control and learning. Its neuronal network architecture, firing characteristics of component neurons, and learning rules at their synapses have been well understood in terms of anatomy and physiology. A realistic artificial cerebellum with mimetic network architecture and synaptic plasticity mechanisms may allow us to analyze cerebellar information processing in the real world by applying it to adaptive control of actual machines. Several artificial cerebellums have previously been constructed, but they require high-performance hardware to run in real-time for real-world machine control. Presently, we implemented an artificial cerebellum with the size of 104 spiking neuron models on a field-programmable gate array (FPGA) which is compact, lightweight, portable, and low-power-consumption. In the implementation three novel techniques are employed: (1) 16-bit fixed-point operation and randomized rounding, (2) fully connected spike information transmission, and (3) alternative memory that uses pseudo-random number generators. We demonstrate that the FPGA artificial cerebellum runs in real-time, and its component neuron models behave as those in the corresponding artificial cerebellum configured on a personal computer in Python. We applied the FPGA artificial cerebellum to the adaptive control of a machine in the real world and demonstrated that the artificial cerebellum is capable of adaptively reducing control error after sudden load changes. This is the first implementation and demonstration of a spiking artificial cerebellum on an FPGA applicable to real-world adaptive control. The FPGA artificial cerebellum may provide neuroscientific insights into cerebellar information processing in adaptive motor control and may be applied to various neuro-devices to augment and extend human motor control capabilities.

Retinal Circuit Emulator With Spatiotemporal Spike Outputs at Millisecond Resolution in Response to Visual Events.

To gain insights on how visual information of the real world is filtered, compressed, and encoded by the vertebrate retinas, emulating in silico the spatiotemporal patterns of the graded and action potentials of neuronal responses to natural visual scenes on biological time scale is a feasible approach. As a basic platform for such an emulation, we here developed a compact hardware system comprising an analog silicon retina and a field-programmable gate array module. With utilizing the Izhikevich formalism, a retinal circuit model that emulates spiking of ganglion cells was implemented in this system. The emulated spike timing had the resolution of about 2 ms relative to the stimulus onset and was little affected by timings of the synchronous frame sampling in the silicon retina. Thus, the emulator can mimic the event-driven spike outputs of biological retinas. The system was useful for simultaneously visualizing neural images of both the graded potentials and the spikes in response to real live visual scenes. Since our emulator system is reconfigurable, it provides a flexible platform for investigating visual functions of retinal circuits under natural visual environment.

Real-time object tracking based on scale-invariant features employing bio-inspired hardware.

We developed a vision sensor system that performs a scale-invariant feature transform (SIFT) in real time. To apply the SIFT algorithm efficiently, we focus on a two-fold process performed by the visual system: whole-image parallel filtering and frequency-band parallel processing. The vision sensor system comprises an active pixel sensor, a metal-oxide semiconductor (MOS)-based resistive network, a field-programmable gate array (FPGA), and a digital computer. We employed the MOS-based resistive network for instantaneous spatial filtering and a configurable filter size. The FPGA is used to pipeline process the frequency-band signals. The proposed system was evaluated by tracking the feature points detected on an object in a video.

Real-time emulator for reproducing graded potentials in vertebrate retina.

In most parts of the retina, neuronal circuits process visual signals represented by slowly changing membrane potentials, or so-called graded potentials. A feasible approach to speculate about the functional roles of retinal neuronal circuits is to reproduce the graded potentials of retinal neurons in response to natural scenes. In this study, we developed a simulation platform for reproducing graded potentials with the following features: real-time reproduction of retinal neural activities in response to natural scenes, a configurable model structure, and compact hardware. The spatio-temporal properties of neurons were emulated efficiently by a mixed analog-digital architecture that consisted of analog resistive networks and a field-programmable gate array. The neural activities on sustained and transient pathways were emulated from 128 × 128 inputs at 200 frames per second.

Image sensor system with bio-inspired efficient coding and adaptation.

We designed and implemented an image sensor system equipped with three bio-inspired coding and adaptation strategies: logarithmic transform, local average subtraction, and feedback gain control. The system comprises a field-programmable gate array (FPGA), a resistive network, and active pixel sensors (APS), whose light intensity-voltage characteristics are controllable. The system employs multiple time-varying reset voltage signals for APS in order to realize multiple logarithmic intensity-voltage characteristics, which are controlled so that the entropy of the output image is maximized. The system also employs local average subtraction and gain control in order to obtain images with an appropriate contrast. The local average is calculated by the resistive network instantaneously. The designed system was successfully used to obtain appropriate images of objects that were subjected to large changes in illumination.

A visually guided collision warning system with a neuromorphic architecture.

We have designed a visually guided collision warning system with a neuromorphic architecture, employing an algorithm inspired by the visual nervous system of locusts. The system was implemented with mixed analog-digital integrated circuits consisting of an analog resistive network and field-programmable gate array (FPGA) circuits. The resistive network processes the interaction between the laterally spreading excitatory and inhibitory signals instantaneously, which is essential for real-time computation of collision avoidance with a low power consumption and a compact hardware. The system responded selectively to approaching objects of simulated movie images at close range. The system was, however, confronted with serious noise problems due to the vibratory ego-motion, when it was installed in a mobile miniature car. To overcome this problem, we developed the algorithm, which is also installable in FPGA circuits, in order for the system to respond robustly during the ego-motion.