Connectionism is a technical term for a group of related techniques. These techniques include areas such as Artificial Neural Networks, Semantic Networks and a few other similar ideas. My present focus is on neural networks (though I am looking for resources on the other techniques). Neural networks are programs designed to simulate the workings of the brain. They consist of a network of small mathematical-based nodes, which work together to form patterns of information. They have tremendous potential and currently seem to be having a great deal of success with image processing and robot control.
These are libraries of code or classes for use in programming within the Connectionist field. They are not meant as stand alone applications, but rather as tools for building your own applications.
This software implements flexible Bayesian models for regression and classification applications that are based on multilayer perceptron neural networks or on Gaussian processes. The implementation uses Markov chain Monte Carlo methods. Software modules that support Markov chain sampling are included in the distribution, and may be useful in other applications.
BELIEF is a Common Lisp implementation of the Dempster and Kong fusion and propagation algorithm for Graphical Belief Function Models and the Lauritzen and Spiegelhalter algorithm for Graphical Probabilistic Models. It includes code for manipulating graphical belief models such as Bayes Nets and Relevance Diagrams (a subset of Influence Diagrams) using both belief functions and probabilities as basic representations of uncertainty. It uses the Shenoy and Shafer version of the algorithm, so one of its unique features is that it supports both probability distributions and belief functions. It also has limited support for second order models (probability distributions on parameters).
A simple back-propogation ANN in Python.
Cellular Neural Networks (CNN) is a massive parallel computing paradigm defined in discrete N-dimensional spaces. A visualizing CNN Simulator which allows to track the way in which the state trajectories evolve, thus gaining an insight into the behavior of CNN dynamics. This may be useful for forming an idea how a CNN 'works', especially for those people who are not experienced in CNN theory.
CONICAL is a C++ class library for building simulations common in computational neuroscience. Currently its focus is on compartmental modeling, with capabilities similar to GENESIS and NEURON. A model neuron is built out of compartments, usually with a cylindrical shape. When small enough, these open-ended cylinders can approximate nearly any geometry. Future classes may support reaction-diffusion kinetics and more. A key feature of CONICAL is its cross-platform compatibility; it has been fully co-developed and tested under Unix, DOS, and Mac OS.
ffnet is a fast and easy-to-use feed-forward neural network training solution for python. Many nice features are implemented: arbitrary network connectivity, automatic data normalization, very efficient training tools, network export to fortran code.
Joone is a neural net framework to create, train and test neural nets. The aim is to create a distributed environment based on JavaSpaces both for enthusiastic and professional users, based on the newest Java technologies. Joone is composed of a central engine that is the fulcrum of all applications that already exist or will be developed. The neural engine is modular, scalable, multitasking and tensile. Everyone can write new modules to implement new algorithms or new architectures starting from the simple components distributed with the core engine. The main idea is to create the basis to promote a zillion of AI applications that revolve around the core framework.
A simple, fast, efficient C++ Matrix class designed for scientists and engineers. The Matrix class is well suited for applications with complex math algorithms. As an demonstration of the Matrix class, it was used to implement the backward error propagation algorithm for a multi-layer feed-forward artificial neural network.
A set of ANSI C packages that illustrate Adaline networks, back-propagation, the Hopfield model, BAM, Boltzman, CPN, SOM, and ART1. Coded in portable, self-contained ANSI C. With complete example applications from a variety of well-known application domains.
Many neuroevolution methods evolve fixed-topology networks. Some methods evolve topologies in addition to weights, but these usually have a bound on the complexity of networks that can be evolved and begin evolution with random topologies. This project is based on a neuroevolution method called NeuroEvolution of Augmenting Topologies (NEAT) that can evolve networks of unbounded complexity from a minimal starting point.
The research as a broader goal of showing that evolving topologies is necessary to achieve 3 major goals of neuroevolution: (1) Continual coevolution: Successful competitive coevolution can use the evolution of topologies to continuously elaborate strategies. (2) Evolution of Adaptive Networks: The evolution of topologies allows neuroevolution to evolve adaptive networks with plastic synapses by designating which connections should be adaptive and in what ways. (3) Combining Expert Networks: Separate expert neural networks can be fused through the evolution of connecting neurons between them.
NEURObjects is a set of C++ library classes for neural networks development. The main goal of the library consists in supporting researchers and practitioners in developing new neural network methods and applications, exploiting the potentialities of object-oriented design and programming. NEURObjects provides also general purpose applications for classification problems and can be used for fast prototyping of inductive machine learning applications.
The Numenta Platform for Intelligent Computing (NuPIC) is built around HTM networds (Hierarchical Temporal Memory). Based on Jeff Hawkins idea as laid out in his On Intelligence book. NuPIC consists of the Numenta Tools Framework and the Numenta Runtime Engine.
Free for non-commercial use.
Pulcinella is written in CommonLisp, and appears as a library of Lisp functions for creating, modifying and evaluating valuation systems. Alternatively, the user can choose to interact with Pulcinella via a graphical interface (only available in Allegro CL). Pulcinella provides primitives to build and evaluate uncertainty models according to several uncertainty calculi, including probability theory, possibility theory, and Dempster-Shafer's theory of belief functions; and the possibility theory by Zadeh, Dubois and Prade's. A User's Manual is available on request.
SCN Artificial Neural Network Library provides a programmer with a simple object-oriented API for constructing ANNs. Currently, the library supports non-recursive networks with an arbitrary number of layers, each with an arbitrary number of nodes. Facilities exist for training with momentum, and there are plans to gracefully extend the functionality of the library in later releases.
A bit different from the other entries, this is a reference to a collection of software rather than one application. It was all developed by the UTCS Neural Net Research Group. Here's a summary of some of the packages available:
Example neural net codes from the book, The Pattern Recognition Basics of AI. These are simple example codes of these various neural nets. They work well as a good starting point for simple experimentation and for learning what the code is like behind the simulators. The types of networks available on this site are: (implemented in C++)
These are various applications, software kits, etc. meant for research in the field of Connectionism. Their ease of use will vary, as they were designed to meet some particular research interest more than as an easy to use commercial package.
(am6.tar.Z on ftp site)
The software that we are releasing now is for creating, and evaluating, feed-forward networks such as those used with the backpropagation learning algorithm. The software is aimed both at the expert programmer/neural network researcher who may wish to tailor significant portions of the system to his/her precise needs, as well as at casual users who will wish to use the system with an absolute minimum of effort.
DDLab is an interactive graphics program for research into the dynamics of finite binary networks, relevant to the study of complexity, emergent phenomena, neural networks, and aspects of theoretical biology such as gene regulatory networks. A network can be set up with any architecture between regular CA (1d or 2d) and "random Boolean networks" (networks with arbitrary connections and heterogeneous rules). The network may also have heterogeneous neighborhood sizes.
GENESIS (short for GEneral NEural SImulation System) is a general purpose simulation platform which was developed to support the simulation of neural systems ranging from complex models of single neurons to simulations of large networks made up of more abstract neuronal components. GENESIS has provided the basis for laboratory courses in neural simulation at both Caltech and the Marine Biological Laboratory in Woods Hole, MA, as well as several other institutions. Most current GENESIS applications involve realistic simulations of biological neural systems. Although the software can also model more abstract networks, other simulators are more suitable for backpropagation and similar connectionist modeling.
The JavaBayes system is a set of tools, containing a graphical editor, a core inference engine and a parser. JavaBayes can produce:
Jbpe is a back-propagation neural network editor/simulator.
The Neural Network Generator is a genetic algorithm for the topological optimization of feedforward neural networks. It implements the Semantic Changing Genetic Algorithm and the Unit-Cluster Model. The Semantic Changing Genetic Algorithm is an extended genetic algorithm that allows fast dynamic adaptation of the genetic coding through population analysis. The Unit-Cluster Model is an approach to the construction of modular feedforward networks with a ''backbone'' structure.
NOTE: To compile this on Linux requires one change in the Makefiles. You will need to change '-ltermlib' to '-ltermcap'.
nn is a high-level neural network specification language. The current version is best suited for feed-forward nets, but recurrent models can and have been implemented, e.g. Hopfield nets, Jordan/Elman nets, etc. In nn, it is easy to change network dynamics. The nn compiler can generate C code or executable programs (so there must be a C compiler available), with a powerful command line interface (but everything may also be controlled via the graphical interface, xnn). It is possible for the user to write C routines that can be called from inside the nn specification, and to use the nn specification as a function that is called from a C program. Please note that no programming is necessary in order to use the network models that come with the system (`netpack').
xnn is a graphical front end to networks generated by the nn compiler, and to the compiler itself. The xnn graphical interface is intuitive and easy to use for beginners, yet powerful, with many possibilities for visualizing network data.
NOTE: You have to run the install program that comes with this to get the license key installed. It gets put (by default) in /usr/lib. If you (like myself) want to install the package somewhere other than in the /usr directory structure (the install program gives you this option) you will have to set up some environmental variables (NNLIBDIR & NNINCLUDEDIR are required). You can read about these (and a few other optional variables) in appendix A of the documentation (pg 113).
NEURON is an extensible nerve modeling and simulation program. It allows you to create complex nerve models by connecting multiple one-dimensional sections together to form arbitrary cell morphologies, and allows you to insert multiple membrane properties into these sections (including channels, synapses, ionic concentrations, and counters). The interface was designed to present the neural modeler with a intuitive environment and hide the details of the numerical methods used in the simulation.
As the field of Connectionist modeling has grown, so has the need for a comprehensive simulation environment for the development and testing of Connectionist models. Our goal in developing PDP++ has been to integrate several powerful software development and user interface tools into a general purpose simulation environment that is both user friendly and user extensible. The simulator is built in the C++ programming language, and incorporates a state of the art script interpreter with the full expressive power of C++. The graphical user interface is built with the Interviews toolkit, and allows full access to the data structures and processing modules out of which the simulator is built. We have constructed several useful graphical modules for easy interaction with the structure and the contents of neural networks, and we've made it possible to change and adapt many things. At the programming level, we have set things up in such a way as to make user extensions as painless as possible. The programmer creates new C++ objects, which might be new kinds of units or new kinds of processes; once compiled and linked into the simulator, these new objects can then be accessed and used like any other.
RNS (Recurrent Network Simulator) is a simulator for recurrent neural networks. Regular neural networks are also supported. The program uses a derivative of the back-propagation algorithm, but also includes other (not that well tested) algorithms.
The semnet.py module defines several simple classes for building and using semantic networks. A semantic network is a way of representing knowledge, and it enables the program to do simple reasoning with very little effort on the part of the programmer.
The following classes are defined:
With these three object types, you can very quickly define knowledge about a set of objects, and query them for logical conclusions.
Stuttgart Neural Net Simulator (version 4.1). An awesome neural net simulator. Better than any commercial simulator I've seen. The simulator kernel is written in C (it's fast!). It supports over 20 different network architectures, has 2D and 3D X-based graphical representations, the 2D GUI has an integrated network editor, and can generate a separate NN program in C. SNNS is very powerful, though a bit difficult to learn at first. To help with this it comes with example networks and tutorials for many of the architectures. ENZO, a supplementary system allows you to evolve your networks with genetic algorithms.
SPRLIB (Statistical Pattern Recognition Library) was developed to support the easy construction and simulation of pattern classifiers. It consist of a library of functions (written in C) that can be called from your own program. Most of the well-known classifiers are present (k-nn, Fisher, Parzen, ....), as well as error estimation and dataset generation routines.
ANNLIB (Artificial Neural Networks Library) is a neural network simulation library based on the data architecture laid down by SPRLIB. The library contains numerous functions for creating, training and testing feed-forward networks. Training algorithms include back-propagation, pseudo-Newton, Levenberg-Marquardt, conjugate gradient descent, BFGS.... Furthermore, it is possible - due to the datastructures' general applicability - to build Kohonen maps and other more exotic network architectures using the same data types.
TOOLDIAG is a collection of methods for statistical pattern recognition. The main area of application is classification. The application area is limited to multidimensional continuous features, without any missing values. No symbolic features (attributes) are allowed. The program in implemented in the 'C' programming language and was tested in several computing environments.
XNBC v8 is a simulation tool for the neuroscientists interested in simulating biological neural networks using a user friendly tool.
XNBC is a software package for simulating biological neural networks.
Four neuron models are available, three phenomenologic models (xnbc, leaky integrator and conditional burster) and an ion-conductance based model. Inputs to the simulated neurons can be provided by experimental data stored in files, allowing the creation of `hybrid'' networks.