Modelling and Analysis of Retinal Ganglion Cells Through System Identification

Abstract

Modelling biological systems is difficult due to insufficient knowledge about the internal components and organisation, and the complexity of the interactions within the system. At cellular level existing computational models of visual neurons can be derived by quantitatively fitting particular sets of physiological data using an input-output analysis where a known input is given to the system and its output is recorded. These models need to capture the full spatio-temporal description of neuron behaviour under natural viewing conditions. At a computational level we aspire to take advantage of state-of-the-art techniques to accurately model non-standard types of retinal ganglion cells. Using system identification techniques to express the biological input-output coupling mathematically, and computational modelling techniques to model highly complex neuronal structures, we will "identify" ganglion cell behaviour with visual scenes, and represent the mapping between perception and response automatically.

Computational modelling of salamander retinal ganglion cells using machine learning approaches

“…An early approach to developing computational models of the retina was based on non-linear system identification techniques which are often used as way of reverse engineering complex systems [8]. Previously they has been used to understand the responses of auditory neurons in [8], retinal ganglion cells in [9] and in [10], the Wiener theory of non-linear system identification [11] was applied to study the underlying functional relationship between a cell membrane potential and the resulting spiking response from RGCs of a catfish. Following this study, the Wiener-Volterra method [12] has been extensively used to model non-linear biological systems [13,14,15,16,17,18,19]; however a major drawback of the Wiener-Volterra approach is the geometrically increasing computational complexity with the kernel order [20].…”

Bioinspired Approach to Modeling Retinal Ganglion Cells Using System Identification Techniques

“…The NARMAX technique lends itself to a broad range of applications in several areas which include modelling robot behaviour [22], time series analysis [23], iceberg calving and detecting and tracking time-varying causality for EEG data [24]. In previous work, [18], [25] the NARMAX methodology has been utilised to help formulate a retina modelling development process and in particular, to express the biological input-output relationship using polynomial models.…”

“…In this work we expand on [25] by introducing, in addition to the NARMAX model, the self-organising fuzzy neural network (SOFNN) and NARX methodologies. The predictive performance of the investigated methodologies to adequately model a retinal ganglion cell's output is evaluated.…”