Corticonics, echoing electronics, is the art of identifying anatomical an physiological attributes of cortical organization to be abstracted and used in the modeling and simulation of the cortex. Generally, the cortex, in conjunction with the subcortical centers, is responsible for all higher-level brain functions such as cognition, thought, language, memory and learning, control of the complex motor function, and possibly the more esoteric attributes of intention, awareness and consciousness. In fact, about 75% of all human brain tissue, defining the association cortices is devoted to these functions. Thereby, understanding the workings of the cortex can have profound scientific, technological, and clinical implications. Unfortunately, the intrinsic interest of these higher-level functions is equaled by the difficulties involved—both technical and conceptual—in understanding their neurobiological basis. Nonetheless, progress to further this incomplete understanding is being made through studies of brain tissue that is damaged or has lesions, from in vivo imaging of the brain, and from electrode and patch-clamp studies in non-human primates. These studies aim at developing a complementary computational approach to modeling and studying the cortex employing the concepts and tools of nonlinear dynamics.
The non-linearity and organization of cortical tissue make the cortex a high-dimensional non-linear dynamical system. As such, it exhibits in its state-space not only static (fixed point) attractors but also dynamic (periodic, quasi-periodic and chaotic) attractors depending on its location in parameter space. However, important questions remain unanswered about these brain functions and, specifically, about the role of attractors in cortical cognitive processes. An assumption is that the most obvious role for attractors is to make it possible to operate on or utilize the activity trace caused by a stimulus after the stimulus has disappeared. Several important inquiries result from this assumption. Namely, 1) Is a particular attractor associated with the recognition of a particular object or stimulus?; 2) Is the setting of cortical activity onto an attractor state synonymous with the recognition process?; and 3) Is such persistent activity needed for the formation of memory?
Current modeling practices fall short of providing answers to these resulting inquiries. Neural networks are the predominate model used to explain brain functions and how these brain functions could be mimicked in computing environments. Specifically, a neural network is an information processing paradigm that is inspired by the way biological nervous systems process information. The key element of this paradigm is the novel structure of the information processing system. It is generally composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. Neural networks have an ability to derive meaning from complicated or imprecise data. This ability can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques.
However, current brain computational models do not effectively predict the behavior observed in the cortex. Stated differently, current models do not effectively choose those features of cortical organization to make salient in the model and eliminate and ignore those features of cortical organization that do not provide any added benefit. The test of the model lies in how well it can produce, predict, and synthesize cortical functions. Current models, although effective in providing a general model for brain and/or nervous system functions do not effectively and reliably model detailed cortical functions—functions that if properly modeled could provide substantial insight to how to process large volumes of data. Such insight may be applied to numerous data intensive processing applications to improve processing efficiencies. With increased processing efficiencies computing technologies could be used to automate numerous manual tasks—manual tasks that we take for granted, such as voice recognition and synthesis, data searching, basic learning, etc.
From the foregoing it is appreciated that there exists a need for comprehensive systems and methods offering a dynamical brain model, and specifically the cortex, that may be applied to various data processing applications. The present invention meets this pressing need in the art.