To understand the basic concepts behind these properties is essential for two reasons: The life-science point of view and the information-technology point of view.
- The first point of view has potential medical applications to cure brain and mind related diseases or even the longer-term goals to work towards neural prosthetic devices and artificial sensory organs.
- The second point of view could lead to new computing devices radically different from contemporary IT technology. Such devices could provide support for complex decision making processes like the one we are currently used to obtain only from human beings.
To improve the performance of such information processing systems, several approaches can be followed depending of the prior knowledge available. Indeed, depending on additional labels (class or continuous value) which can be used (or available) on none of the patterns, on a subset of the patterns or on all of them, unsupervised or supervised learning can be sequentially performed. When there is no prior knowledge on the problem to be solved, knowledge extraction may use an unsupervised neural network as a front-end for forecasting applications or extracting rules. Because of its synthesis capabilities, an unsupervised neural network can be used both for limiting the computation complexity and for extracting the most significant knowledge. Moreover, knowledge extraction is facilitated as soon as multi-viewpoint unsupervised neural network model is used. This kind of methods also allows using in a second step additional information when it is available for optimizing a forecasting problem. However, for a forecasting problem where all patterns are labelled, classical networks using supervised learning can be successfully improved by finding the minimal architecture using pruning algorithms. The pruning methods consist in removing, during learning, the connections or neurons, or both, that have the least influence on the system's performance. Reducing the complexity of the networks prevents overtraining and allows easier implementation and knowledge extraction (variable selection, rule extraction). In any case, combining several models into a committee helps to improve the quality of the knowledge extracted or the forecasting and the proposed methods must be efficient for typical real-world in our domain, dealing with large amount of noisy and temporal data. Both topics are recently developed in the project.
I actually think that, in OpenCog, for example, reasoning or other systems could take advantage of a SNN to provide rule selection or knowledge extraction, etc.
So the point is that even though they might be completely ignoring most higher-level necessary aspects of human cognition, a more comprehensive system can still benefit from incorporating SNNs.
To understand the basic concepts behind these properties is essential for two reasons: The life-science point of view and the information-technology point of view.
Some things that Spiking Neural Networks are useful for (besides attempting to simulate a brain) (from http://ralyx.inria.fr/2007/Raweb/cortex/uid7.html):To improve the performance of such information processing systems, several approaches can be followed depending of the prior knowledge available. Indeed, depending on additional labels (class or continuous value) which can be used (or available) on none of the patterns, on a subset of the patterns or on all of them, unsupervised or supervised learning can be sequentially performed. When there is no prior knowledge on the problem to be solved, knowledge extraction may use an unsupervised neural network as a front-end for forecasting applications or extracting rules. Because of its synthesis capabilities, an unsupervised neural network can be used both for limiting the computation complexity and for extracting the most significant knowledge. Moreover, knowledge extraction is facilitated as soon as multi-viewpoint unsupervised neural network model is used. This kind of methods also allows using in a second step additional information when it is available for optimizing a forecasting problem. However, for a forecasting problem where all patterns are labelled, classical networks using supervised learning can be successfully improved by finding the minimal architecture using pruning algorithms. The pruning methods consist in removing, during learning, the connections or neurons, or both, that have the least influence on the system's performance. Reducing the complexity of the networks prevents overtraining and allows easier implementation and knowledge extraction (variable selection, rule extraction). In any case, combining several models into a committee helps to improve the quality of the knowledge extracted or the forecasting and the proposed methods must be efficient for typical real-world in our domain, dealing with large amount of noisy and temporal data. Both topics are recently developed in the project.
I actually think that, in OpenCog, for example, reasoning or other systems could take advantage of a SNN to provide rule selection or knowledge extraction, etc.
So the point is that even though they might be completely ignoring most higher-level necessary aspects of human cognition, a more comprehensive system can still benefit from incorporating SNNs.