A Computational Model of Adaptive Sensory Processing in the Electroreception of Mormyrid Electric Fish
Electroreception is a sensory modality found in some fish, which enables them to sense the environment through the detection of electric fields. Biological experimentation on this ability has built an intricate framework that has identified many of the components involved in electroreception's production, but lack the framework for bringing the details back together into a system-level model of how they operate together. This thesis builds and tests a computational model of the Electrosensory Lateral Line Lobe (ELL) in mormyrid electric fish in an attempt to bring some of electroreception's structural details together to help explain its function. The ELL is a brain region that functions as a primary processing area of electroreception. It acts as an adaptive filter that learns to predict reoccurring stimuli and removes them from its sensory stream, passing only novel inputs to other brain regions for further processing. By creating a model of the ELL, the relevant components which underlie the ELL's functional, electrophysiological patterns can be identified and scientific hypotheses regarding their behavior can be tested.
Systems science's approach is adopted to identify the ELL's relevant components and bring them together into a unified conceptual framework. The methodological framework of computational neuroscience is used to create a computational model of this structure of relevant components and to simulate their interactions. Individual activation tendencies of the different included cell types are modeled with dynamical systems equations and are interconnected according to the connectivity of the real ELL. Several of the ELL's input patterns are modeled and incorporated in the model. The computational approach claims that if all of the relevant components of a system are captured and interconnected accurately in a computer program, then when provided with accurate representations of the inputs a simulation should produce functional patterns similar to those of the real system. These simulated patterns generated by the ELL model are compared to recordings from real mormyrid ELLs and their correspondences validate or nullify the model's integrity.
By building a computation model that can capture the relevant components of the ELL's structure and through simulation reproduces its function, a systems-level understanding begins to emerge and leads to a description of how the ELL's structure, along with relevant inputs, generate its function. The model can be manipulated more easily than a biological ELL, and allows us to test hypotheses regarding how changes in the structures affect the function, and how different inputs propagate through the structure in a way that produces complex functional patterns.
0790: Systems science