Fig. 5. The universal network
Thinking
I guess that the phenomenon of synesthesia is the result of multi-input
and multi-output, because I don’t have other sensory data. Because the
parameters of network shared by the multi-input and multi-output, so one
input may affect other output when the two outputs are the attributes of
some similar object, such as fire and sun which have red color and hot
attributes. For example, When we see red(vision input), we may feel
warm(warm sensation output), however When we see blue, we may feel cool.
Because we can also use the convolution
neural network to process sequence
data and nature language processing is also represented by functions, so
nature language processing can use the same network as image recognition
and speech recognition so long as the output node and the input node and
more parameters add into the network. The network is universal so long
as the network can process the different inputs. We can first send image
or speech to the network and the output is language or other
information, then send the language or information to the network’s
input, process it or predict it. I guess that connection in mind can
realize through the universal network and sending the output into input.
For example, it’s cloudy now. This information is sent to the network’s
input, the predict output is there will be a rain. This output is sent
to network’s input, the predict output is that I need an umbrella.
Because the information about it’s cloudy and there will be a rain is
represented by image or audio or language, so only universal network can
do it. The most likelyhood event can be seen as the effect of the cause.
It’s causal reasoning. Connection in mind is the key of creativity,
synesthesia is the assistant.
Conclusions
1.The abstract network(prediction network) achieves the intended
functionality of multi-attribute recognition through multi-dimension
regression. The Concrete network can generate the output which the input
rarely contained from the attributes the network learned. Its
generalization ability is good because the network is a continuous
function.
2.We can use one neural network to do image recognition, speech
recognition, nature language processing and other things simultaneously
so long as the output node and the input node and more parameters add
into the network. The network is universal so long as it can process
different inputs. I guess that the phenomenon of synesthesia is the
result of multi-input and multi-output. I guess that connection in mind
can realize through the universal network and sending the output into
input.
References
- Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, MIT
Press, (2016)
- Andrew L. Maas, Awni Y. Hannum and Andrew Y. Ng, Rectified
Nonlinearities Improve Neural Network Acoustic Models, ICML, (2013)
- Tensorflow Tutorials and Apis, https://tensorflow.google.cn/learn last
accessed 2020/3/20
- Jin-xin Wei, Qun-ying Ren, A Functionally Separate
Autoencoder, unpublished