Generalization over contrast and mirror reversal, but not figure-ground reversal, in an "edge-based

Baylis & Driver (Nature Neuroscience, 2001) have recently presented data on the response of neurons in macaque inferotemporal cortex (IT) to various stimulus transformations. They report that neurons can generalize over contrast and mirror reversal, but not over figure-ground reversal. This finding is taken to demonstrate that ``the selectivity of IT neurons is not determined simply by the distinctive contours in a display, contrary to simple edge-based models of shape recognition'', citing our recently presented model of object recognition in cortex (Riesenhuber & Poggio, Nature Neuroscience, 1999). In this memo, I show that the main effects of the experiment can be obtained by performing the appropriate simulations in our simple feedforward model. This suggests for IT cell tuning that the possible contributions of explicit edge assignment processes postulated in (Baylis & Driver, 2001) might be smaller than expected.

Modelling the Somantic Electrical Response of Hippocampal Pyramidal Neurons

A modeling study of hippocampal pyramidal neurons is described. This study is based on simulations using HIPPO, a program which simulates the somatic electrical activity of these cells. HIPPO is based on a) descriptions of eleven non-linear conductances that have been either reported for this class of cell in the literature or postulated in the present study, and b) an approximation of the electrotonic structure of the cell that is derived in this thesis, based on data for the linear properties of these cells. HIPPO is used a) to integrate empirical data from a variety of sources on the electrical characteristics of this type of cell, b) to investigate the functional significance of the various elements that underly the electrical behavior, and c) to provide a tool for the electrophysiologist to supplement direct observation of these cells and provide a method of testing speculations regarding parameters that are not accessible.