- Windows 2000 / XP / Vista / 7 / 8 / 10
- 1,0 GHz CPU
- 256 MB RAM
- 20 MB HD
Mathematical Model used by MemBrain
The neuron and link model of MemBrain is very flexible: Everything from simple time invariant Feed-Forward Networks to Nets with spiking neurons, arbitrary loopback connections and signal runtime delays on the links can be
simulated. It is even possible to directly connect the output of a neuron to its own input.
In principle every net in MemBrain is a valid net. Through the consequent object oriented approach every neuron and also every link in MemBrain can have different properties. Links can have user-defined logical lengths, so that real runtime behaviour can be simulated and visualized.
Net with loopback links
Snapshot of a chaotic net with
many loop backs. In the center
of the net
there is a neuron that
Click pictures to enlarge.
The exact mathematical model for neurons and links used by MemBrain is
detailed in MemBrain's HTML help.
Available learning algorithms
Currently the following learning algorithms are
implemented in MemBrain.
- Standard Backpropagation (only forward links are trained)
- Standard Backpropagation with momentum (only forward links are
- Backpropagation with support for loopback links
- Backpropagation with support for loopback links and with momentum
- RPROP (Resilient Backpropagation) with support for loopback links
- Cascade Correlation with support for loopback links (using
Backpropagation with momentum)
- Cascade Correlation with support for loopback links (using RPROP)
- Trial and Error with support for loopback links
- 'Winner Takes it All' for SOMs ('Self Organizing Maps').
The MemBrain DLL
Many functionalities of MemBrain are also available as a DLL
(Dynamic Link Library). Two DLL versions are provided: 32 Bit and 64 Bit.
With the MemBrain DLL you can incorporate neural nets that have been designed
into your own applications no matter which programming language you use.
The DLL features:
- Loading an arbitrary number of neural nets from MemBrain files
- Assigning values to the input neurons of the nets
- Performing simulation steps of the nets
- Reading back the results from the output neurons of the nets
- Saving the nets back to file
- All functionalities to train the
nets, to create, load, import and export data sets and much more
- All functionalities to create and edit neural nets
By this means it is easily possible to incorporate neural nets from
MemBrain into a production system without the need of having MemBrain running in parallel and without
having to deal with the TCP/IP interface or the scripting language of MemBrain.
Additionally, applications can be designed that create and/or edit neural nets
during run time and thus can dynamically adapt to new problems.
The MemBrain DLL is included in the installation packet of MemBrain, the
corresponding files will be
copied to a separate sub folder automatically during the installation. In the download
area you will find
an example on how to use the DLL from within Microsoft Excel VBA (Visual Basic for Applications).
Java users are provided with a JNI wrapper DLL and a corresponding Java class
to directly access the
DLL from Java programs. The Java class file contains comments on how to use
the files. Both, the
wrapper DLL and the Java class file are copied to the DLL folder during
An object oriented set of C# wrapper classes is available for free, too (see download
Certainly the MemBrain DLL also is accessible from the C/C++ programming
Detailed information on the DLL is included in the corresponding header file "MemBrain_inc.h".
Automatic Code Generation
MemBrain features a code generator which can translate
a currently loaded neural net into C code that can be compiled using any standard C compiler so that the net can be run within user
software (e.g. on an embedded platform).
The generated code can model every aspect of MemBrain neural nets starting
from the simple Feed-Forward net up to nets that feature time variant components, delays on links, loopback
links, random firing ect.
The code consists of a fix library portion which is generated during the
installation of MemBrain and two files that are dynamically created by MemBrain for every single net: A header and an
implementation file. The MemBrain-generated implementation file provides net specific definitions of
The header file publishes some important definitions for the net and
incorporates the preprocessor directives which optimize the library code according to the generated net.
For this purpose the net is subjected to a detailed analysis procedure in
order to only invoke the library portions that are absolutely necessary to implement the net. This approach ensures that
MemBrain always generates code that is highly optimized according to the user's needs and provides maximum
prformance with minimum memory requirements.
Code generation follows a 'relaxed ANSI-C' standard. I.e. all relevant ANSI-C
rules are obeyed, however, the code makes use of some common extensions like single line comments in C++
With respect to the possible use on embedded targets no dynamic memory
allocation is used.
The code provides the following means to operate the generated net.
- Reset and initialize the net
- Assign input activations to the input neurons
- Execute simulation steps of the net
- Read back results from the output neurons
The mentioned code library is included in the installation packet of
MemBrain. The corresponding files are automatically copied into a corresponding sub folder during the installation. The MemBrain
help (press F1 in MemBrain) contains details about using the code in your own applications.
object oriented scripting language
MemBrain features a powerful, fully featured object oriented scripting language. Almost all
operations in MemBrain are available through scripting, too.
Scripts also provide a means for remote controlling
MemBrain from other software or for preparing
small presentations that show live
operation of neural nets.
MemBrain scripts can interact with the user in many ways including message
boxes, file open or
save dialogs, numerical and string value input fields, trace window text output
etc. Due to the
powerful maths and file operations available in MemBrain scripts can also be
used to optimize
neural net training and validation strategies according to user's needs. E.g.
scripting can be
used to select optimum learning parameters and neural net candidates
automatically based on
user defined algorithms.
Furthermore, MemBrain's scripting language contains powerful functions to
create and modify
neural nets. Thus, even your own evolutionary algorithms for net design can be
realized through scripting.
The MemBrain scripting language is based on the scripting engine AngelScript
which provides a strongly typed C++/Java like scripting language. The scripts are compiled into
byte code by MemBrain before execution which guarantees optimum runtime performance.
MemBrain Scripts can be executed in several ways:
- Manually, via the menu <Scripting> in MemBrain
- From the command line, as parameter to MemBrain.exe
- Through a special file interface while MemBrain is running
- From within the external graphical script debugger
Details on executing scripts via one of the named ways are contained in
MemBrain's help file.
Examples of MemBrain scripts are available in the download
Source Level Debugger for scripts
MemBrain features a powerful graphical debugger for scripts including the
- Syntax highlighting
- Fully featured text editor, including block indentation, copy/paste,
search function, bookmarks...
- Compile, build run and pause script remotely from within debugger
- Build output window: See compiler messages directly in the debugger
window, jump to code location via mouse click
- Debug output window
- Variable watch window
- Graphical breakpoints
- Step Into, Step Over, Step Out
- Attach debugger to running script
- Run debugger on the same machine as MemBrain or on an other machine
within the LAN
- Start debugger from within MemBrain
- Start MemBrain from within the debugger
The following screenshot shows the debugger in action:
Click picture to enlarge
automatic Network analysis
You don't have to tell MemBrain what net architecture you're currently building up.
Just place as many neurons as you like and where you like and connect them in the way you like.
Besides the possibility of interconnecting neurons by drawing single links there
are also mighty editing features available in MemBrain that allow insertion of thousands
of links with a few mouse clicks.
The integrated neural network analysis feature detects the logical structure of the
net, like layers and loopback links, whenever needed automatically in background. This information can be visualized, too.
Example result of the network analysis
Click picture to enlarge.
MemBrain provides an optional, integrated password controlled 256 bit AES
encryption for neural nets and data set files (lessons). Files which use this
procetion can only be opened by MemBrain or the MemBrain DLL with the correct
Die used AES encryption represents one of the strongest
encryption techgnologies available today.
Multi Core support
MemBrain automatically determines the optimum usage of the available
computing cores of the operating system. Through this MemBrain always only takes
as much of the available computing power as reasonable with respect to the
currently operated neural net. The rules for determining the number of used
cores can even be adjusted by the user if desired.
Moreover, the process priority of MemBrain is freely adjustable via the
MemBrain menu so that the actual burden of MemBrain on the computer becomes
adjustable in a wide range.
Open TCP/IP interface
MemBrain incorporates the possibility to connect to other MemBrain instances or MemBrain compatible device drivers over a TCP/IP link. It doesn't make any difference if the other
applications are running on different computers or on the same machine.
MemBrain's TCP/IP Weblink protocol is provided as part of the context sensitive HTML help that comes with the application.
Click picture to enlarge.
The screen shot shows a picture matrix of 160 x 120 input neurons, invoked into a neural net over TCP/IP from the 'MemBrain Picture Driver' which is also available for download
in the download area.
Neurons can be displayed as grey scale pixels in MemBrain depending on their
current activation value.
This gives the impression of a monochrome bitmap.
Click pictures to enlarge.