What is the best programming language to implement neural networks?

I'm not looking for a Neural Networks library, since I'm creating new kinds of networks. For that I need a good "dataflow" language.

Of course you can do this in C, C++, Java and co. but dealing from scratch with the mul开发者_运维技巧tithreading etc. would be a nightmare.

At the other extremity, languages like Oz or Erlang seem more adapted, but they don't have many libraries, and they are harder to master (it's easy to play with them, but is it OK to create complete software ?).

What would you suggest ?

I watched an interesting conference presentation about using Erlang for Neural Networks. You might want to check it out:

From Telecom Networks to Neural Networks; Erlang, as the unintentional Neural Network Programming Language

I also know that the presented system is going to be open-sourced any day now according the authors tweet.

Erlang is very well suited for NN.

1. Neurons can be modeled by processes (no problem with having millions of them)
2. Connections/synapses can be represented by PIDs of target neuron. It is very easy to initialize such a network as part of standard init procedure in OTP. Communication would be realized by message passing.
3. Maybe it would be good to have global address space in ETS/mnesia (build in datastores) in order to do dynamic reconfiguration of network structure.
4. Pattern matching in receive block can determine what kind of signal neuron receives and modify it on the fly.
5. It would be very easy to monitor such a network.

Also consider that Erlang NN would be 'live' all the time. You would be able to query neurons, layers, routers etc any time. In C/C++ you just read current state of arrays/data structure.

Regarding performance, we all know that C/C++ is orders of magnitude faster than Erlang, however NN topic is tricky.

If the network would hold very few neurons, in very wide address space, in regular array, iterating over it again and again could be costly (in C). Equivalent situation in Erlang would be solved by single query to root/roots (input layer) neurons, which would propagate query directly to well addressed neighborhs.

DXNN1, and DXNN2 which was built and introduced in the textbook: Handbook of Neuroevolution Through Erlang: http://www.amazon.com/Handbook-Neuroevolution-Through-Erlang-Gene/dp/1461444624/ref=zg_bs_760204_22

Are open source and available at: https://github.com/CorticalComputer

If you are interested in data flow programming and multi-threading then I would suggest National Instruments LabVIEW. In this case you don't need to bother about multi-threading since its already there and you can also use OOP since now OOP is also native with LabVIEW. LabVIEW OOP is also purely based on data flow programming paradigm.

If you have any Java experience, then use Scala which is a JVM language that is based on the same concept of "actors" as Erlang. But it is less strict than Erlang and can easily use any existing Java libraries.

Then, when you find a computationally expensive task that would work better in Erlang, you can use Erlang's jinterface library to communicate between your Scala code and your distributed Erlang nodes.

Using Java does not mean dealing from scratch with multithreading - just use one of numerous Java Actor Libraries.

It's not a language in and of itself, but Emergent is very powerful and can be highly customized (it has a full scripting language).

It's open source, too, which could be helpful as a guide if you need to make your own version for your novel architectures.

Why reinvent the wheel? Try PyBrain. It's free and very comprehensive:

• Quickstart

Another big plus for Erlang is full integration with Drakon

http://drakon-editor.sourceforge.net/drakon-erlang/intro.html

It all depends on your application. C++, Python are some good programming languages for machine learning