Computational Neuroscience and Cognitive Robotics ...

Processing visual stimuli using biological inspired neural networks implemented on neuromorphic hardware Pedro Machado, Kofi Appiah and T.M. McGinnity Computational Neuroscience and Cognitive Robotics Laboratory Nottingham Trent University, School of Science and Technology ABSTRACT

METHODOLOGY

The retina is a thin peace of tissue situated at the back of the eye and it is responsible for performing the first stages of image processing [1]. It is composed of seven layers; each of these layers accommodates different cell types that are responsible for processing the visual stimuli and forwarding the processed information to the visual cortex via the optic nerve [1] (see Figure 1).

The current implementation is an improvement of the Lobula Giant Movement Detector (LGMD) described in [6], where the summing cells have been replaced by a biologically plausible leaky-integrate-and-fire neuron models (LIFs, figure 4). Each LIF is composed of eight inhibitory cells and one excitatory cell also designated as Receptive Fields (RF). 𝜏𝑚 𝑑𝑢 𝑑𝑡

= −𝑢 𝑡 + 𝑅𝑇 𝑡 (1)

Figure 3: Schematic diagram of the LIF [5]

Figure 1: Representation of the retina [1]

The difference between the excitatory and inhibitory light intensities is converted into a current that is feed into each LIF model and the resulting spikes are fed into each group of cells (see figure 4). Each group of cells will respond to specific movements (looming, horizontal and vertical).

In recent years there has been renewed interest a wide in the retinal research as a result of innovations in technology such as multi-electrode arrays [2] which allow for acquisition of data from retinal slices. The evolution of these devices in terms of size, speed, parallel channels and resolution now enables researchers to perform a deeper study of the retina[3]. In turn, these results enable computer scientists to design more accurate computational models. Figure 4: Hierarchical neural network

In parallel, the evolution brought by the latest generation of field programmable gate arrays (FPGAs) has impacted on the ability to emulate retinal visual processing in machines. FPGAs are powerful hardware computing platforms that can be freely reconfigured after manufacturing offering significant amounts of logic elements, built-in memory and hardware intellectual property (IP) blocks that can be used to describe custom circuits. Because of their unique characteristics, parallelism, low power consumption and the exponential increase of transistors per square, FPGAs (see Figure 2) are an attractive target for implementation of neuromorphic hardware [4].

RESULTS The looming and direction detectors have been tested with different types of synthetic images and the show that each type of cells are responding as expected (see figure 5).

Figure 2: FPGA device

OBJECTIVES 1) Investigate more sophisticated biologically plausible computational models of the retina; as compared to the current state of the art and thus develop an improved understanding of how visual information is encoded in spikes from retinal ganglion cells (Figure 3); 2) Analyse and model using a high performance cluster computing system based on FPGAs; how the retinal response to synthetic images differs from natural scenes and how such differences are encoded; 3) Develop specific models for selected retinal cells, for emulation in neuromorphic hardware to solve challenging practical problems of dynamic vision in a much more biological and computationally efficient manner; 4) Integrate such hardware emulations of specific cells to emulate overall retinal function using neuromorphic hardware (very large scale integration (VLSI) circuits that mimic neuro-biological architectures) for application in cognitive robotics.

Figure 3: Neuromorphic retina [5]

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Figure 5: 1st row - frame sequences, 2nd row - cells responses, 3rd row - spike train. 1st column - looming movements, 2nd – column - horizontal movements and 3rd column - vertical movements.

FUTURE WORK • Train hierarchical neural networks • Model other types of cells (e.g. prediction cells) • Evaluate and test the neuromorphic chip in different scenarios and with different cameras (e.g. CCD, spiking and Infra-Red) • Publish results in the IEEE transactions on Neural Networks and Learning Systems

REFERENCES [1] H. Kolb, “How the Retina Works » American Scientist,” Am. Sci., vol. 91, pp. 28–34, 2003. [2] D. R. Cantrell, J. Cang, J. B. Troy, and X. Liu, “Non-centered spike-triggered covariance analysis reveals neurotrophin-3 as a developmental regulator of receptive field properties of ON-OFF retinal ganglion cells,” PLoS Comput.Biol., vol. 6, no. 10, 2010. [3] T. Gollisch and M. Meister, “Eye Smarter than Scientists Believed: Neural Computations in Circuits of the Retina,” Neuron, vol. 65, no. 2, pp. 150– 164, 2010. [4] H. Paugam-Moisy, “Spiking Neuron Networks A survey,” Idiapch, vol. 592, pp. 1–44, 2006. [5] Eidgenössische Technische Hochschule Zürich, retrieved online https://www.bsse.ethz.ch/bel/research/electrophysiology-and-neuroscience/retinalinvestigations.html on the 08/05/2017. [6] Meng, H.; Appiah, K.; Yue, S.; Hunter, A.; Hobden, M.; Priestley, N.; Hobden, P.; Pettit, C.; “A Modified Model for the Lobula Giant Movement Detector and Its FPGA Implementation”; Journal of Computer Vision and Image Understanding; Volume 114; Issue 11; Pages. 12381247; 2010