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tions of neural networks, the theme of the book is principles rather than applica- From the perspective of pattern recognition, neural networks can be regarded. Collection of Papers and Books concerning Deep Neural Networks - CDitzel/ Deep-Learning-Literature. Neural Networks for Pattern Recognition – Statistical foundation, perspective and alternatives, Graduate course, Semester Bet /
Extended Data Fig. The winner-take-all computation is broken into five subfunctions: weight multiplication, summation, pairwise annihilation, signal restoration and reporting. In the chemical reactions listed next to the five subfunctions, the species in black are needed as part of the function, the species in grey are needed to facilitate the reactions and the waste species are not shown. In the DNA-strand-displacement implementation, weight multiplication and signal restoration are both catalytic reactions. The grey circle with an arrow indicates the direction of the catalytic cycle. Representative, but not all possible, states are shown for the pairwise-annihilation reaction. Each domain is labelled with a name, and asterisks in the names indicate sequence complementarity.
In the DNA-strand-displacement implementation, weight multiplication and signal restoration are both catalytic reactions. The grey circle with an arrow indicates the direction of the catalytic cycle.
Representative, but not all possible, states are shown for the pairwise-annihilation reaction. Each domain is labelled with a name, and asterisks in the names indicate sequence complementarity.
Black-filled and white-filled arrowheads indicate the forwards and backwards directions of a reaction step, respectively. Each black number indicates the identity of a seesaw node.
The location and absolute value of each red number indicates the identity and relative initial concentration of a DNA species, respectively. A red number on a wire connected to a node or between two nodes indicates a free signal molecule, which can be an input or fuel strand. A red number inside a node indicates a gate molecule, which can be a weight, summation gate or restoration gate.
A red number on a wire that stops perpendicularly at two wires indicates an annihilator molecule.
A negative red number inside a half node with a zigzag arrow indicates a reporter molecule. The experimental data left, same as Fig. All fluorescence kinetics data and simulation are shown over the course of 2. In each output trajectory plot, dotted lines indicate fluorescence kinetics data and solid lines indicate simulation.
The patterns to the left and right of the arrow indicate input signal and output classification, respectively. The thresholding mechanism has been reported previously in work on seesaw DNA circuits The extended toehold in threshold molecule has 7 nucleotides.
In b and c, to compare the range of inputs, the concentration of each input strand is shown relative to 50 nM. The initial concentration of each weight molecule is either 0 or 50 nM; weight fuels are twice the concentration of weight molecules.
Source Data Extended Data Fig. The initial concentration of each weight molecule is either 0 or 10 nM 0.
Today, algorithms for image recognition are well advanced and can be found in many applications such as search engines, security systems, industrial robots, medical devices, and virtual reality. Besides the many areas of application, another reason for the fast progress in image recognition might be the vast knowledge about the human visual system.
The eye is arguably the best studied human sensory organ and the visual cortex has been the main object of interest in a large number of neuroscientific studies. Findings from vision science have inspired the development of new hardware as well as novel algorithms and computational tools.
High-definition and high-speed cameras have long surpassed the capacities of the human eye in terms of spatial and temporal resolution. On the software side though, it still proves to be a difficult task to extend the scope of present achievements in static image recognition to dynamic visual recognition of moving objects or a moving scene. The benefit of accurate and fast dynamic visual recognition is apparent: each of the above-mentioned applications of image recognition constitutes a potential application area for dynamic visual recognition systems.
Any kind of robot that must navigate within a three-dimensional environment or perform tasks on moving objects would benefit from an accurate and fast dynamic visual system. The popular topic of self-driving cars is only one example.
Other potential implementations include security systems, automated traffic prediction and tolls, monitoring of manufacturing processes, navigational tools in air and ship traffic, or diagnostic assistants for inspections or surgery. Since the human visual system's adaptability and efficiency are still highly superior to computer systems when it comes to tasks of dynamic vision, it is natural to let biology serve as an inspiration for the development of new computational models.
Previous works have used a combination of bio-inspired visual sensors and spiking neural networks for the recognition of human postures Perez-Carrasco et al. We consider these very promising approaches, though the mentioned works lack benchmarking results that make them comparable.
This paper introduces a new system for dynamic visual recognition that combines a silicon retina device with a brain-like spiking neural network SNN.
As we introduce the different parts of our proposed system, we include findings from vision science that inspired us or that might provide promising approaches for future improvements.
We present the setup and the results of a benchmarking experiment carried out on the MNIST-DVS dataset and show that our system achieves a classification accuracy of The SNN architecture NeuCube is very flexible in terms of its connectivity and learning algorithms and allows for the visualization of the learning processes inside the SNN. After discussing the advantages and limitations of the system, we conclude by suggesting further exploration of the system's performance with modified algorithms and different datasets.
Unlike conventional frame-based video cameras that capture multiple frames per second and store a large number of pixels for each of these frames, the DVS only captures changes in the brightness of single pixels caused by movement of the scene or an object Lichtsteiner et al.
This is called an Address Event Representation AER since the output of the sensor consists of a time series of events together with their location address , representing the temporal contrast of a specific pixel at a specific time.
By responding to temporal contrast on the pixel-level rather than taking a continuous series of snapshots of the whole scene, the DVS mimics the functioning of the human retina much better than conventional video cameras Purves, Together with its focus on movements within a scene there is another reason to choose the DVS over a conventional video camera for a dynamic vision system based on a spiking neural network: the address event output of the DVS comes in the form of a series of spike trains, each spike train corresponding to one pixel of the sensor.
Every single spike in the train of one specific pixel represents a change in brightness in that pixel at a specific time.