OCAS Cortiphica

OCAS Cortiphica
OCAS Cortiphica is a software complex of neural networks composed of integrated elements of the Neural Integrated Development Environment (NIDE), artificial cognitive systems and artificial intelligence systems. This tool allows everyone from researchers and engineers to students and schoolchildren, to create neural network architectures with hundreds of millions of elements and network-based complex-structured cores which carry out different cognitive functions.

OCAS Cortiphica allows you to develop both "classic" neural networks, such as perceptrons, and biomorphic neural networks, similar to the neural networks of living organisms. This can be achieved by reducing the base model of the neuron with the multiphase formation of the memory trace embedded in the OCAS Cortiphica system to the more basic neural-like elements of the "classic" neural networks.

OCAS Cortiphica is not based on neurons but instead cybergens. Various elements combine to provide the program code which give OCAS Cortiphica its structure and provide a dynamic reformation of the bodies of neurons (catfish), the processes of neurons (dendrites and axons) and the synapses (interneurons), all collectively working to create and develop the neural networks.

OCAS Cortiphica is designed to develop fundamentally new neural network architectures and can be used in a number of industries.

Scientific Research

In the academic field, OCAS Cortiphica can be successfully used to solve two main classes of task: (1) the development of neural network artificial intelligence systems (2) the construction of dynamic nerve node models of living beings which are reconstructed in the course of neurobiological research.


The system inputs various modes of signal to a neural network (text, sound, images, data from other sensory recorders) and the output of control signals to drives and other executable devices. OCAS Cortiphica can be used to create control systems for technical equipment, such as autonomous robots, automated complexes and virtual 3D models.


OCAS Cortiphica can be used in schools during to educate students in the various technical specialties, such as information technology and neuroscience.

Practical work on the development and implementation of neural networks in the OCAS Cortiphica environment will provide students with a clear understanding of the memory formation processes in a living neuron, the organization of synaptic signalling, neuropathy, the organization and synchronization of neural networks and their rearrangement during signal processing.

OCAS Cortiphica has already been successfully applied in specialist areas like neuro modelling. Using ready-made elements of neurons under the guidance of teachers, schoolchildren can first copy, and then independently develop, basic neural network architectures. This is facilitated by the unique ability of OCAS Cortiphica to reproduce physical representations of virtual creations, within which students can apply the developed neural networks to the management of 3D models.

Graphical Interface

The development of neural networks in OCAS Cortiphica is carried out in a window of the module "Master of Networks" with a graphical interface. The system has created numerous convenient tools to quickly facilitate numerous connections between randomly located soma neurons. The system also uses nested sheets to model the three-dimensional volume of neural networks created. Since networks can consist of hundreds of millions of elements that cannot be accommodated in the computer's RAM, a mechanism is created for combining network parts into groups — loci. Within each locus, work is carried out independently of the rest of the network. The network hierarchy becomes recursive — loci may contain other loci. For quick access to network fragments, a mechanism for generating a visual representation of the network at different scales is implemented.

Neuron Models

A memory trace forms in a nerve cell through a series of stages, from the appearance of electrical potentials to the formation of new protein receptors and structural reorganization as a result of the inclusion of genes in the cell nucleus. The base models of artificial neurons used in OCAS Cortiphica simulate the process of consolidation and reconsolidation of the memory trace, which includes the short, medium and long-term phases of memory formation.

Shorter memory phases serve as the basis for the formation of more long-term ones. The models of compartments (dendrites, catfish, axons) of OCAS neurons use parameters such as the frequency of action potentials, axonal facilitation, the release of a mediator, the events of a mediator-receptor connection, postsynaptic potentials and the synthesis of new receptors of a mediator during training. Important cognitive abilities of neural networks are associated with each of the phases of memory formation in artificial neurons. Editing models of neuron compartments is carried out in the module "Master of Neuron Models". The created neuron compartment models are stored in the model library as a template for use in creating various neurons.


To control the growth and development of large neural networks in OCAS Cortiphica, "cybergenomics" is used - software algorithms that ensure the formation and modification of the network structure by simulating the effects of genetic expression in living organisms. Elements of artificial neurons (dendrites, soma, axons, and also synapses) are created as sets of cybergens.

Cybergens provide for the deployment and structural changes of neural networks. Cybergens are understood to be structurally functional units of software code that control the development of a particular trait, properties, procedures, or expression of other cybergens within a cybergenetic system, such as a neurogenetic network. Cybergens determine the development, growth and functioning of a cyberorganism. There are different types of cybergens such as structural, coordinate, functiona, etc. Cybergens can be edited in the software module "Cybergen Master".

Signal events in a neural network can cause its structural reorganization, realizing the effects of epigenetic regulation.

Signal Input and Output Interfaces

The system provides input to the neural network and output from the signals of various modalities: text, sound, images and other sensory signals, including signals from external devices and drives. The input video signal can be stratified by wavelength to activate various types of artificial photoreceptors. In addition, the system has virtual cameras used within the virtual world of the system.

Virtual World

For the development of neural control networks for simulated cyber-physical systems in the framework of NIDE OCAS Cortiphica, a virtual world of polygons and controlled 3D-models is created.

Performance and Hardware

NIDE OCAS Cortiphica is designed as a parallel high-performance software package for supercomputers. The system is implemented in a low-level C ++ language with a set of cross-platform Qt class libraries.

As a hardware platform, nodes based on high-performance servers with Intel Xeon Processor E5-2650V3 processors and Xeon PHI co-processors (or similar) are recommended. The reason for choosing the Intel PHI platform is a combination of high-powered processors and a large amount of RAM (256 gigabytes) in the servers.

The OpenMP library is used for the efficient parallel execution of computation on systems with shared memory. The system allows you to create neural networks with 30 million artificial synapses per computer node.
NIDE OCAS Cortiphica is supplied as a computer program in accordance with the license agreement under basic (non-exclusive) licensing terms.

The system can be installed on the customer's computers or operated remotely from OCAS computers with Internet access.

If necessary, robotic chassis are supplied with the development system, or if 3D-models of the virtual world are required by the customer. In the case of using the system for educational purposes, OCAS can supply the appropriate methodological support for any required programs.
A demonstration of training and additional training (with recombination of a memory trace) of an element of a corticomorphic network to manage a virtual pipeline
An example of teaching a hippomorphic neural network to turn a virtual car – oin two directions in NIDE OCAS Cortiphica
The formation of graphical-object neurons (grandmother cells) in the OCAS corticomorphous neural network
Multimodal association (text, image and motility of the robot) and cybergenomics (for example, the germination of the neural network locus) in NIDE OCAS Cortiphica