Abstract
Many researchers have explored the relationship between recurrent neural networks and finite state machines. Finite state machines constitute the best-characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. The neurally-inspired lateral inhibition method, and its application to motion detection tasks, have been successfully implemented in recent years. In this paper, control knowledge of the algorithmic lateral inhibition (ALI) method is described and applied by means of finite state machines, in which the state space is constituted from the set of distinguishable cases of accumulated charge in a local memory. The article describes an ALI implementation for a motion detection task. For the implementation, we have chosen to use one of the members of the 16-nm Kintex UltraScale+ family of Xilinx FPGAs. FPGAs provide the necessary accuracy, resolution, and precision to run neural algorithms alongside current sensor technologies. The results offered in this paper demonstrate that this implementation provides accurate object tracking performance on several datasets, obtaining a high F-score value (0.86) for the most complex sequence used. Moreover, it outperforms implementations of a complete ALI algorithm and a simplified version of the ALI algorithm—named “accumulative computation”—which was run about ten years ago, now reaching real-time processing times that were simply not achievable at that time for ALI.
Highlights
Over recent decades, many researchers have explored the relationship between discrete-time recurrent neural networks and finite state machines, either by showing their computational equivalence or by training the former to perform as finite state recognizers [1]
This section includes all the relevant details on the evaluation process carried out to check the performance of the implemented algorithm, which was undertaken using field programmable gate arrays (FPGAs) to reduce the execution time of the sequential moving object detection algorithm
FPGA data were introduced in the previous section, together with the results of the corresponding analysis
Summary
Many researchers have explored the relationship between discrete-time recurrent neural networks and finite state machines, either by showing their computational equivalence or by training the former to perform as finite state recognizers [1]. The relationship between discrete-time recurrent neural networks and finite state machines has very deep roots [2,3]. Kleene formalized the sets of input sequences that led a McCulloch-Pitts network to a given state, and later, Minsky showed that any finite state machine can be simulated by a discrete-time recurrent neural net using McCulloch-Pitts units [2]. M.T.; Fernández-Caballero, A.; Fernández, M.A.; Mira, J.; Delgado, A.E. Algorithmic lateral inhibition formal model for real-time motion detection.
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