Sense Element Engagement Theory Explains How Neural Networks Produce Cortical Prosthetic Vision

Abstract

<p>Demonstrating that an understanding of how neural networks produce a specific quality of experience has been achieved would provide a foundation for new research programs and neurotechnologies. The phenomena that comprise cortical prosthetic vision have two desirable properties for the pursuit of this goal: 1) Models of the subjective qualities of cortical prosthetic vision can be constructed; and 2) These models can be related in a natural way to models of the objective aspects of cortical prosthetic vision. Sense element engagement theory portrays the qualities of cortical prosthetic vision together with coordinated objective neural phenomena as constituting sensible spatiotemporal patterns that are produced by neural interactions. Small-scale neural network simulations are used to illustrate how these patterns are thought to arise. It is proposed that simulations and an electronic neural network (ENN) should be employed in devising tests of the theory. Large-scale simulations can provide estimates of parameter values that are required to construct an ENN. The ENN will be used to develop a prosthetic device that is predicted by the theory to produce visual forms in a novel fashion. According to the theory, confirmation of this prediction would also provide evidence that this ENN is a sentient device.</p>

Progress in Developing a Neuromorphic Device that is Predicted to Enhance Cortical Prosthetic Vision by Enabling the Formation of Multiple Visual Geometries

“…When this graphic is appropriately sized and the fixation point (+) is viewed from a distance of 54 , the visual experience should approximate the perception of four stimulation-induced phosphenes centered (−7.5°, 8.5°) from the direction of gaze. The visual space that phosphenes inhabit can be modeled as a visual geometry [1,2] using relations between idealized, flattened V1 coordinates ( , ) and retinal eccentricity and azimuth coordinates (e.g., [18] and [19], Ch. 2), = ln(1 + ⁄ ), = − ( + )180 °⁄ (1) and then approximating visual geometry coordinates ̂ and by finding the inverse of equations (1),…”

“…The visual space that phosphenes inhabit can be modeled as a visual geometry [1,2] using relations between idealized, flattened V1 coordinates ( , ) and retinal eccentricity and azimuth coordinates (e.g., [18] and [19], Ch. 2), = ln(1 + ⁄ ), = − ( + )180 °⁄ (1) and then approximating visual geometry coordinates ̂ and by finding the inverse of equations (1),…”

“…The V1 geometry is composed of 939 idealized columns and the visual geometry of 939 corresponding visual regions. As in [1] and [2], the visual geometry was constructed by using equations (2) to transform coordinates of the center of V1 column to values ( ̂ , ) of idealized visual geometry coordinates, assigning the value (0.15 + 0.09 ̂ ) . to the radius of visual geometric region , and then converting ( ̂ , ) values to cartesian coordinates °, ° that specify the center of each visual region in degrees of visual angle.…”

“…It is therefore assumed that each phosphene inhabits at least one visual geometric region . Because it seems unlikely that the lightness of a phosphene can be modeled using a real number, an interval of values is used [1,2]. It is assumed that Blue-Yellow and Red-Green opponents are activated approximately equally by electrical stimulation, thereby resulting in typically achromatic phosphenes [7,20].…”

“…The sense element engagement (SEE) theory of cortical prosthetic vision (CPV) was devised in order to provide a testable explanation of how neural interactions give rise to specific qualities of experience [1,2]. The phenomena of CPV were specifically chosen for explanation because the qualities of cortical prosthetic visual experience are readily amenable to mathematical modeling, elements of the resulting models can be related to models of neuron and neural network activity in a natural way, and knowledge of such relationships could lead to improvements in CPV.…”