Estimating natural targets in the visual space is an important problem in neuroscience primarily because animals have to negotiate targets in order to accomplish a task. In freshwater turtles, it is believed that the visual cortex plays a role in accomplishing the task of predicting target location. In this paper, we consider a set of 'fish-images' and represent these images with a sparse and an over-complete set of spatial basis functions. The associated coefficient signals are further compressed, along every column, using principal components. This provides an appropriate input to the model of the visual cortex, and the associated cortical response of a large number of pyramidal cells are generated. We estimate the cortical input from the associated neural response by constructing an Autoregressive and Moving Average (ARMA) Model. The input to the model is the neuronal response suitably smoothed by a low pass filter. The output of the ARMA model is precisely the prediction of the cortical inputs. This paper illustrates the role of natural scene reconstruction from the activity waves of a set of pyramidal neurons.
|Number of pages||5|
|Journal||Proceedings of the IEEE Conference on Decision and Control|
|State||Published - 2004|
|Event||2004 43rd IEEE Conference on Decision and Control (CDC) - Nassau, Bahamas|
Duration: Dec 14 2004 → Dec 17 2004