Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , the ability of a binary neural-network comprising only neurons with zero thresholds and binary ights to map given samples of a Boolean function is studied. A mathematical model describing a network with such restrictions is developed. It is shown that this model is quite amenable to algebraic manipulation. A key feature of the model is that it replaces the two input and output variables with a single "normalized" variable. The model is then used to provide a priori criteria, stated in terms of the new variable, that a given Boolean function must satisfy in order to be mapped by a network having one or two layers. These criteria provide necessary, and in the case of a one-layer network, sufficient conditions for samples of a Boolean function to be mapped by a binary neural network with zero thresholds. It is shown that the necessary conditions imposed by the two-layer network are, in some sense, minimal.
['Vinay Deolalikar']
I am writing a research paper on a idea Mapping Boolean functions with neural networks having binary weights and zero thresholds
[{"from": "human", "value": "I am writing a research paper on a idea Mapping Boolean functions with neural networks having binary weights and zero thresholds"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: , the ability of a binary neural-network comprising only neurons with zero thresholds and binary ights to map given samples of a Boolean function is studied. A mathematical model describing a network with such restrictions is developed. It is shown that this model is quite amenable to algebraic manipulation. A key feature of the model is that it replaces the two input and output variables with a single \"normalized\" variable. The model is then used to provide a priori criteria, stated in terms of the new variable, that a given Boolean function must satisfy in order to be mapped by a network having one or two layers. These criteria provide necessary, and in the case of a one-layer network, sufficient conditions for samples of a Boolean function to be mapped by a binary neural network with zero thresholds. It is shown that the necessary conditions imposed by the two-layer network are, in some sense, minimal."}]