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3. “NeuroSolutions is one of the slickest and most complete packages for neural network simulation that anyone could wish for—and one of the most flexible as well…” -Dan Ellis IEEE Spectrum Click here to return to the ND main menu.

4. NeuroSolutions The Neural Network Simulation Environment NeuroSolutions provides an object-oriented simulation environment for neural network design and application. It has quickly evolved into the software tool of choice for both the neural network beginner and expert alike. This leading edge software combines a modular, icon-based network design interface with an implementation of advanced learning procedures, such as recurrent backpropagation and backpropagation through time.The result is a virtually unconstrained environment for designing neural networks to solve real-world problems such as financial forecasting, pattern recognition, process control, targeting marketing, and many more. Select any of the following topics for more information or use the arrows to step through them all. Interactive Probing Graphical User Interface Neural Network Creation Advanced Features

5. Graphical User Interface (GUI) NeuroSolutions is based on the concept that neural networks can be broken down into a fundamental set of neural components. By allowing the user to arbitrarily interconnect these components, a virtually infinite number of neural models can be constructed. Neural components, such as axons, synapses, and gradient search engines, are laid out on a graphical breadboard and connected together to form a neural network. Input components are used to inject signals, and probe components are used to visualize the network’s response.

6. Neural Network Creation Creating neural networks is fast and easy with NeuroSolutions. Let the NeuralExpert create and customize a neural network for your type of application. Or, let the NeuralBuilder build a neural network topology to your specifications. Plus, enjoy the flexibility of being able to create neural networks directly from palettes of customizable components or modifying existing designs. Select either of the following topics for more information. NeuralExpert Application-based neural network designer. NeuralBuilder Topology-based neural network creator. Data Manager Data management and analysis tool.

7. Interactive Probing Probing is an important step in the neural network design process and is therefore, an integral part of NeuroSolutions. As with the neural components, the probe components are inherently modular; the way you view the data is independent of what the data represents. NeuroSolutions probes provide you with real-time access to all variables during the simulation, along with a variety of ways to visualize them. This represents an unparalleled ability to see what is going on inside your networks.

8. Advanced Features Advanced users will want to utilize the software to the next level. Researchers will invariably want to integrate their own algorithms into NeuroSolutions; application developers will want to integrate NeuroSolutions algorithms into their own; and those prototyping large networks within NeuroSolutions will often want to run them on faster hardware platforms. NeuroSolutions was designed to accommodate all of these requirements. Select any of the following topics for more information. Genetic Optimization Macros Sensitivity Analysis OLE Automation Code Generation Dynamic Link Libraries (DLLs)

9. Probing Matrix Viewer Neural networks are often criticized as being a “black box” technology. With NeuroSolutions’ extensive and versatile set of probing tools, this is no longer the case. Probes provide you with real-time access to all internal network variables, such as: •Inputs/Outputs •Weights •Errors •Hidden States •Gradients •Sensitivities Probing is an important step in the neural network design process, therefore we have made it an integral part of NeuroSolutions. All network data are reported through a common protocol, and all NeuroSolutions probes understand this protocol. This provides you with access to all internal variables, along with a variety of ways to visualize them. Bar Chart Matrix Editor State Space Data Writer Image Viewer Scatter Plot Data Graph Hinton Diagram Spectrum Analyzer

10. Probing Matrix Viewer Matrix Editor This probe displays the current data values in matrix format, providing quantitative information about the data being probed. It can be used to obtain the value of any internal network variable. Similar in function to the Matrix Viewer, this probe has a very important additional function; it allows you to modify the data being probed. For example, you may want to modify the weights or inject a specific pattern to determine how the network responds.

11. Probing Data Writer Scatter Plot The Data Writer collects and displays data in matrix format during a simulation. The collected data may then be written to an ASCII or binary file, which can be used for further processing or reporting results. This probe plots the temporal data of one channel (PE) against the temporal data of one of the other channels. Multiple pairs of channels can be specified. The data from each pair is used as the X and Y coordinate of a two-dimensional graph. The points are collected over a number of samples to produce a scatter plot.

12. Probing Data Graph Spectrum Analyzer This probe can be used as a multi-channel graph, displaying amplitude versus time. Typical uses include displaying time-varying network activity and network learning curves. This is a necessity when working with temporal problems such as time series prediction. The Spectrum Analyzer is used to compute periodograms from temporal data. Periodograms are generated by averaging windowed Fast Fourier Transforms (FFT's) over time.

13. Probing Bar Chart State Space This probe provides a qualitative feel for probed data in the form of horizontal bars. The length of each bar represents the magnitude of the signal at one channel. This is very useful for static classification problems when making a comparison between an output and its desired response, or as a thermometer of the network’s performance when probing the mean squared error. The State Space probe displays a 3-D representation of the system’s state as it evolves over time. It does this by plotting the signal against approximations of its first and second derivatives. This tool is very useful for dynamic system analysis.

14. Probing Image Viewer Hinton Diagram The Image Viewer interprets and displays network data as a bitmap image. The data is normalized and converted to a matrix of values corresponding to pixel intensity levels (white corresponds to one and black corresponds to zero). The Hinton diagram provides a qualitative display of the values in a data matrix (normally a weight matrix). Each value is represented by a square whose size is associated with the magnitude, and whose color indicates the sign.

15. NeuralExpert NeuralExpert Application-based neural network designer. Do you have a specific problem that you would like to solve with neural networks? Let the NeuralExpert design and customize a neural network topology around your data and the solution you would like to find. The NeuralExpert eliminates the need to know which type of topology is best for your problem type by selecting the right design for your application and customizing it to your needs. Simply select the type of problem you would like to solve, and then answer a few questions about your data and how you would like to process it. The NeuralExpert will select the appropriate neural network topology based on the problem type and customize it for your application-specific data. The created network will typically be capable of solving your problem. However, since it is created in NeuroSolutions, any part of the neural network topology can be updated or modified.

16. NeuralExpert Step 1: Using the NeuralExpert Problem Type Selection The NeuralExpert makes it easy to create the appropriate type of neural network for your application. Simply select type of problem you want to solve and provide it with a sample of your data. The NeuralExpert automatically tailors its questions to the problem type. There is even a “beginner level” that allows you to skip over advanced options that typically don’t need to be selected. Here is an example of the steps taken to create a neural network to classify the sex of crabs from their characteristics… Select the type of problem you want to solve from the following types of problems: • Classification • Function Approximation • Prediction • Clustering

17. NeuralExpert Steps 3-4: Step 2: Tag Input Columns Input File Selection Indicate which columns in the input file to use as inputs for your problem. You can even use symbolic or categorical data such as “Male” and “Female” in the input columns! Indicate where your input file is located. The NeuralExpert will customize the solution for your specific inputs.

18. NeuralExpert Steps 6-7: Step 5: Tag Desired Columns Desired File Selection Indicate which columns in the desired file to model. As with the inputs, you use symbolic or categorical data in any of the columns. Indicate which data to use as an example of the data you are trying to model. This can be a separate file or the same file as the input file.

19. NeuralExpert Use Your Neural Network Step 8: Set Network Complexity Based on your data and selections, the NeuralExpert customizes a neural network specifically for your problem. The NeuralExpert places buttons directly on the breadboard to explain how this specific neural network works and allow you to modify or test it. Finally, choose a level of complexity for your neural network. Simple networks will typically train faster and produce better results. More complex networks can be useful for learning more complex relationships within the data.

20. NeuralBuilder NeuralBuilder Topology-based neural network creator. The icon-based user interface of NeuroSolutions provides unprecedented design flexibility for constructing a neural network. This level of access normally requires that you have a substantial amount of neural network knowledge. The NeuralBuilder eliminates this requirement by hiding the complexities of the network and simplifying the design process down to an easy, step-by-step procedure. Simply select a neural model, and then answer a few questions about its configuration parameters. The NeuralBuilder will compute default values for each parameter based on your input data. These default values will typically produce a network that is capable of solving your problem. However, the real power of NeuroSolutions is the level of access provided to you for parameter optimization. All of the parameters of the constructed network can be completely customized.

21. NeuralBuilder Step 1: Models supported by NeuralBuilder Model Selection • • Multilayer Perceptron • • Generalized Feedforward Network • • Modular Neural Network • • Jordan/Elman Network • • Principal Component Analysis Network • • Radial Basis Function Network • • Self-Organizing Feature Map Network • • Time-Lag Recurrent Network • • Recurrent Network • Generalized Regression Network • Probabilistic Network • CANFIS Network (Fuzzy Logic) • Support Vector Machine Select desired neural model from list provided. Respective descriptions appear under list box.

22. NeuralBuilder Step 2: Step 3: Training Data Cross Validation Data Select the training file(s), tagging the input and desired data. Included are facilities for data prediction and symbol translation. Select the testing file(s) used for cross validation. This data can either be extracted from the training set or read from separate files.

23. NeuralBuilder Step 4: Step 5: Topology Configuration Layer Configuration Specify global parameters relating to the network’s topology for the selected neural model. This can be as simple as specifying the number of layers. Specify the number of processing elements, activation function, gradient search method, and learning rate for each network layer.

24. NeuralBuilder Step 6: Step 7: Simulation Control Probe Configuration Specify a stop criterion for the training. This can be based on the number of iterations and/or the error in either the training set or test set. Specify probes to visualize the data.At each of the five most common network points, you can choose the probe which is most appropriate for the data at that point.

25. Data Manager Data Manager Data management and analysis tool. Do you want to manage multiple datasets and analyze them in a simple user interface? The Data Manager allows you to easily analyze, preprocess and partition data. Also included is the ability to plot the data and view the results in the same screen. The datasets are saved within one data project, enabling simple file organization and providing a user-friendly interface to manipulate your datasets. Simply open the data set and perform several different type of analyses or create your neural network right in the Data Manager. The Data Manager is directly tied into the NeuralBuilder which will compute default values for each parameter based on your input data. These default values will typically produce a network that is capable of solving your problem. However, the real power of NeuroSolutions is the level of access provided to you for parameter optimization. All of the parameters of the constructed network can be completely customized.

26. Data Manager Step 1: Step 2: Opening Data Analyze Data Select Open Data File on the interface. Select Analyze Data to choose from several different analyses functions.

27. Data Manager Step 3: Step 4: Preprocess Data Partition Data Select Preprocess Data on the interface to choose from many preprocessing features. Select Partition Data on the interface to select different options of segmenting the dataset.

28. Data Manager Step 5: Step 6: Plots Manage Datasets Select Plot on the interface to plot the data in a Time Series Plot or X-Y Scatter Plot. Select Manage Datasets on the interface to select many options for data management .

29. Data Manager Step 7: Build Neural Model Select Build Neural Models on the interface to begin creating your neural model in NeuroSolutions. The NeuralBuilder will compute default values for each parameter based on your input data. These default values will typically produce a network that is capable of solving your problem. All of the parameters of the constructed network can be completely customized.

30. Advanced Features Genetic Optimization Sensitivity Analysis After training a neural network, you may want to know the effect that each of the network inputs is having on the network output. Sensitivity analysis is a method for extracting the cause and effect relationship between the inputs and outputs of the network. The input channels that produce low sensitivity values can be considered insignificant and can most often be removed from the network. This will reduce the size of the network, which in turn reduces the complexity and the training time. Furthermore, this may also improve the network performance. All levels of NeuroSolutions Users level and above include Genetic Optimization. Genetic Optimization allows you to optimize virtually any parameter in a neural network to produce the lowest error. For example, the number of hidden units, the learning rates, and the input selection can all be optimized to improve the network performance. Individual weights used in the neural network can even be updated through Genetic Optimization as an alternative to traditional training methods.

31. Advanced Features Code Generation Dynamic Link Libraries (DLLs) The Professional level generates ANSI-compatible C++ source code for any network, including learning. This allows a simulation prototyped within the GUI to be run on other hardware platforms. In addition, NeuroSolutions’ networks can be integrated into your own applications. The Developers level allows you to integrate your own algorithms into NeuroSolutions through dynamic link libraries (DLL). Every GUI component implements a function belonging to NeuroSolutions’ Simulation Protocol. Developers can add components by simply writing ANSI-compatible C functions that conform to this protocol.

32. Advanced Features Macros OLE Automation NeuroSolutions is a fully compliant OLE Automation Server. This means that NeuroSolutions can receive control messages from OLE Automation Controllers, such as Visual Basic, Microsoft Excel, Microsoft Access, and Delphi. Writing a fully-functioning VB program is as simple as recording a NeuroSolutions macro, clicking on the convert to VB button, and pasting the converted VB code into the desired VB application. A VB application could be written to set a network’s parameters, run the network, then retrieve the network’s output. Embedded in NeuroSolutions is a comprehensive macro language, which allows the user to record a sequence of operations and store them as a program. Any action that can be performed using the mouse and keyboard can be duplicated with a macro statement. This powerful feature gives the user unprecedented flexibility in constructing, editing, and running neural networks. When running the NeuroSolutions demos, keep in mind that they were constructed entirely with macros.

33. There are six different levels of NeuroSolutions. Select any of the levels below for a description, or use the arrows to advance through them one at a time. Educator Users Consultants Professional Information is also available for the following options and add-on products. Developers Lite Developers Source Code License NeuroSolutions For Excel Custom Solution Wizard

34. Educator Level Unrestricted Topologies • Multilayer perceptions (MLPs) • Generalized feedforward networks • Up to 50 inputs/neurons per layer Learning Paradigms • Backpropagation • Competitive Advantage • • Double-precision calculations • 32-bit code • • Faster simulations • • Icon-based graphical user interface • • Extensive probing capabilities • • Easy neural network creation with the • NeuralExpert and the NeuralBuilder

35. User Level Unrestricted Topologies • All topologies of the Educator • Modular networks • Jordan-Elman networks • Self Organizing Feature Map networks • Radial Basis Function networks • Fuzzy Logic networks • Support Vector Machine networks • Up to 500 inputs/neurons per layer Additional Features • Genetic optimization of neural network parameters and weights. • Learning Paradigms • • Backpropagation • All search methods of Educator level • Conjugate Gradient • Levenberg-Marquardt • • Unsupervised Learning • Hebbian • Oja’s • Sanger’s • Competitive • Kohonen Competitive Advantage • More neurons per layer • More neural models to choose from • More unsupervised learning rules

36. Consultants Level Unrestricted Topologies • All topologies of the Users • Hopfield networks • Time Delay Neural networks • Time-Lag Recurrent networks • User-defined network topologies • Over 90 components to build from • A virtually infinite number of possible networks Learning Paradigms • All paradigms of Users • Recurrent backpropagation • Backpropagation through time Competitive Advantage • Unlimited inputs/outputs/neurons per layer • Modular design allowing user-defined network topologies • Dynamic systems modeling • Time-Lag Recurrent networks

37. Professional Level Unrestricted Topologies • All topologies of the Consultants Learning Paradigms • All paradigms of Consultants Additional Features • ANSI C++ Source Code generation for Visual C++ & Borland compilers • Embed networks into your own applications • Train networks on faster computers (Code generation for Unix requires Source Code License.)

38. Developers Lite Level Unrestricted Topologies • All topologies of the Consultants Learning Paradigms • All paradigms of Consultants Additional Features • User-defined dynamic link libraries • Customized neural components • Nonlinearities • Interconnection matrices • Gradient search procedures • Error criteria • Unsupervised learning rules • Memory structures • Customized input • Customized output • Customized parameter scheduling

39. Developers Level Unrestricted Topologies • All topologies of the Consultants Learning Paradigms • All paradigms of Consultants Additional Features • All additional features of Developers Lite • All additional features of Professional

40. Source Code License The Professional and Developers levels of NeuroSolutions allow you to generate ANSI-compatible C++ source code for the networks you create with the graphical user interface. The generated code links against an object library which contains the implementations for the neural components. Pre-compiled libraries are included for Visual C++ (6.0 – 7.0) and Borland C++ Builder (3.0 or higher). In order to compile the generated code on another platform such as UNIX, or on another Windows compiler, you would need to purchase the Source Code License. Included with the license is the source code for the entire object library, enabling you to compile this library for your particular platform/compiler and link it with the generated code.

41. NeuroSolutions for Excel • Unrestricted Topologies • • All topologies of the licensed level of NeuroSolutions • Learning Paradigms • • All learning paradigms of the licensed version of NeuroSolutions • Additional Features • • Data Preprocessing and Analysis • • Visual Data Selection • • Training and testing from within Microsoft Excel • Leave-N-Out Training • • Parameter Optimization • • Sensitivity Analysis • • Automated Report Generation • • Custom Batch Creation / Execution

42. Custom Solution Wizard • Unrestricted Topologies • • All topologies of the licensed level of NeuroSolutions and the Custom Solution Wizard • Learning Paradigms • • All learning paradigms of the licensed version of NeuroSolutions and the Custom Solution Wizard • Additional Features • • Generates and compiles a Dynamic Link Library (DLL) for any NeuroSolutions neural network • • Supports both recall and learning networks (Developers level) • • Allows you to easily embed a neural network into your own application developed with: • Visual Basic • Microsoft Excel • Microsoft Access • Visual C++ • Active Server Pages (ASP web pages) • TradingSolutions • NeuroSolutions for Matlab

43. NeuroSolutions for Excel NeuroSolutions for Excel is a revolutionary product which benefits both the beginner and advanced neural network developer. For the beginner, NeuroSolutions for Excel offers visual data selection, one step training and testing, and automated report generation. For the advanced user, NeuroSolutions for Excel offers the ability to perform parameter optimization, run batch experiments, and create custom batch experiments programmatically. The best part is that all of these tasks can be performed without ever leaving Microsoft Excel. NeuroSolutions for Excel is organized into the seven modules listed below. Select any of the following modules for more information or use the arrows to step through them all. Preprocess Data Create Data Files Analyze Data Train Network Tag Data Test Network Create/Open Network

44. Preprocess Data Module The Preprocess Data module allows you to easily apply various preprocessing techniques to your raw data to prepare it for input into a neural network. You can also create your own custom Preprocess Data batches by calling built-in NeuroSolutions functions and/or writing Visual Basic code. These custom batches can then be run from the NeuroSolutions for Excel menu from within Microsoft Excel. The following Preprocess Data operations are built into NeuroSolutions for Excel: • Difference Computes the difference or percent difference along a column of data. • Randomize Rows Randomly arranges the rows of data within the active worksheet and writes the result to • a new worksheet. • Sample Creates a new worksheet made up of every Nth row of data within the active worksheet. • Moving Average Computes the moving average of a column using the chosen window length. • Translate Symbolic Columns Translates columns that have been tagged as symbol. • Insert Column LabelsInserts a row of column labels into the first row of the active worksheet. • Clean Data Cleans the data by replacing blank cells, error codes, and/or user-defined text with an interpolated value, the column average, a random value, or the closest value in a column. • Shift The input data is adjusted to either move the inputs back by a specified shift value to do predictions or move the inputs forward to lead your desired output. • Encode Two Class Column The selected column of data is checked to verify that there are two classes contained within the column and is then encoded into another column. The data to be encoded can be textual or numeric, but must be translated to only numeric, integer codes. The encoded column will be written in the first empty column in the dataset.

45. Analyze Data Module The Analyze Data module provides you with useful information about your data. The operations available in this module can be used during the preprocessing stage of neural network design or to analyze the network output. You can also create your own custom Analyze Data batches by calling built-in NeuroSolutions functions and/or writing Visual Basic code. These custom batches can then be run from the NeuroSolutions for Excel menu from within Microsoft Excel. The following Analyze Data operations are built into NeuroSolutions for Excel: • Correlation Computes the correlation between each of the columns of data on the active worksheet. • Time Series Plot Creates a Time Series Plot of the selected columns. • XY Scatter Plot Creates an XY Scatter Plot of the selected columns. • Histogram Computes the histogram of a selected column of data. • Summary Statistics Computes various statistics for a selected column of data. • Trend Accuracy Computes the trend accuracy of the selected columns.

46. Tag Data Module The Tag Data module provides a simple graphical method for tagging portions of your data as Training Input, Training Desired, Cross Validation Input, Cross Validation Desired, Testing Input, Testing Desired, and Production Input. This module also provides powerful autotag methods. You can also create your own custom Tag Data batches by calling built-in NeuroSolutions functions and/or writing Visual Basic code. These custom batches can then be run from the NeuroSolutions for Excel menu from within Microsoft Excel. The following Tag Data operations are built into NeuroSolutions for Excel: • Column(s) As Input Tags the selected column(s) of data as Input. • Column(s) As Desired Tags the selected column(s) of data as Desired. • Column(s) As Symbol Tags the selected column(s) of data as Symbol. • Row(s) As Training Tags the selected row(s) of data as Training. • Row(s) As Cross Validation Tags the selected row(s) of data as Cross Validation. • Row(s) As Testing Tags the selected row(s) of data as Testing. • Row(s) As Production Tags the selected row(s) of data as Production. • All Columns As Input Tags all columns as Input. • All Non-Numeric Columns As Symbol Tags all non-numeric columns as symbol. • All Rows As Training Tags all rows as Training. • Rows By Percentages Tags the rows of data within the active worksheet as Training, Cross Validation, and Testing according to user-defined percentages. • Clear Tags Allows you to clear any existing tag. • Clear Column Tag Clears the tag(s) of the selected column(s). • Clear Symbol Tag Clears the symbol tag for the selected column(s). • Clear Row Tag Clear the tag(s) of the selected row(s). • Clear All Tags Clears all of the tags on the active worksheet. • Select Cross-SectionAllows you to automatically select any existing cross-section. • Refresh Tag Formatting Refreshes the tag formatting.

47. Create/Open Network Module The Create/Open Network module allows you to create a NeuroSolutions breadboard from scratch through the use of the NeuralBuilder utility or open an existing NeuroSolutions breadboard. You can also create your own custom Create Network batches by calling built-in NeuroSolutions functions and/or writing Visual Basic code. These custom batches can then be run from the NeuroSolutions for Excel menu from within Microsoft Excel. The following Create/Open Network operations are built into NeuroSolutions for Excel: • New Classification Network Creates a new NeuroSolutions breadboard with typical elements used for a classification problem. • New Function Approximation Network Creates a new NeuroSolutions breadboard with typical elements used for a classification problem. • New Custom Network Starts the NeuralBuilder which guides you step-by-step through the creation of a new NeuroSolutions breadboard. • Open Opens an existing NeuroSolutions breadboard. • Close Closes the active NeuroSolutions breadboard. • Save Saves the active NeuroSolutions breadboard. • Save As Allows you to save the active NeuroSolutions breadboard to a user- specified location. • Load Best Weights Loads the best weights for the active network. • Tile Excel/NS Horizontally tiles NeuroSolutions and Microsoft Excel.

48. Create Data Files Module The Create Data Files module creates tab delimited ASCII files for each tagged cross-section. You can also create your own custom Create Data Files batches by calling built-in NeuroSolutions functions and/or writing Visual Basic code. These custom batches can then be run from the NeuroSolutions for Excel menu from within Microsoft Excel. The following Create Data Files operations are built into NeuroSolutions for Excel: • All Files Creates data files for all tagged cross-sections within the active worksheet. • Training Files Creates Training Input and Training Desired files from the correspondingly tagged cross-sections within the active worksheet. • Cross Validation FilesCreates Cross Validation Input and Cross Validation Desired files from the correspondingly tagged cross-sections within the active worksheet. • Testing Files Creates Testing Input and Testing Desired files from the correspondingly tagged data cross-sections within the active worksheet. • Production Input File Creates Production Input file from the correspondingly tagged data cross-section within the active worksheet. • View Data File Allows you to view (in Notepad) a data file that was created for the active worksheet. • Delete Data Files Deletes all of the files previously created for the active worksheet.

49. Train Network Module The Train Network module gives you the ability to train a network once, multiple times with different random initial conditions, and multiple times while varying a network parameter. This powerful module permits you to easily find the optimum network for a particular problem. You can also create your own custom Train Network batches by calling built-in NeuroSolutions functions and/or writing Visual Basic code. These custom batches can then be run from the NeuroSolutions for Excel menu from within Microsoft Excel. The following Train Network operations are built into NeuroSolutions for Excel: • Train Trains the active NeuroSolutions breadboard one time and creates a report of the results. • Train N Times Trains the active NeuroSolutions breadboard N times with different random initial conditions and creates report of the results. • Vary a Parameter Trains the active NeuroSolutions breadboard N times for each value of a network parameter as the parameter is varied from a user defined starting value by a user-defined increment for a user defined number of variations. • Leave-N-Out Training Trains the network multiple times, each time omitting a different subset of the data and using that subset for testing. The outputs from each tested subset are combined into one testing report and the model is trained one additional time using all of the data. • Train Genetic Trains the active NeuroSolutions breadboard while genetically optimizing the choice of inputs and parameter values to achieve the best model.

50. Test Network Module The Test Network module can be used to test a network after training has been completed. In testing the network, various performance measures are computed. This module also allows you to perform sensitivity analysis on the network. You can also create your own custom Test Network batches by calling built in NeuroSolutions functions and/or writing Visual Basic code. These custom batches can then be run from the NeuroSolutions for Excel menu from within Microsoft Excel. The following Test Network operations are built into NeuroSolutions for Excel: • TestTests the active NeuroSolutions breadboard on the chosen data set and creates a report of the results. • Sensitivity About the MeanPerforms sensitivity analysis on the chosen data set.