About the presenter: Peter de Florez Professor of Neuroscience Head, Department of Brain and Cognitive Sciences McGovern Institute for Brain Research Massachusetts Institute of Technology
Abstract: Visual object recognition is a fundamental building block of memory and cognition, and a central problem in systems neuroscience, human psychophysics, and computer vision. The computational crux of visual object recognition is that the recognition system must somehow be robust to tremendous possible range of images produced by each object and by the range of objects that belong to the same category. The brain’s solution to this “invariance” problem is thought to be conveyed by the neural outputs at the top of the ventral visual processing stream – the inferior temporal cortex (IT). To move the field from phenomenology to model-based understanding, we are testing falsifiable, mechanistic hypotheses of how IT cortex underlies all of object recognition behavior. The current leading hypothesis framework - inspired by previous work of several labs - is that IT cortex is a neuronal population basis for simple, rapid learning of new objects by downstream neurons. Working within this framework, we have recently found that a simple downstream learning rule working on IT neuronal population rates codes accurately predicts both the pattern and magnitude of behavioral performance over a large range of visual object recognition tasks. The predictions are so accurate as to be statistically perfect (i.e. indistinguishable within the variability of behavior).
I will show methodological proof-of-principle for our next test of this hypotheses – direct causal manipulation (optogenetic and pharmacological) of those IT rate codes and their predicted changes in object discrimination behavior. But what are the neural mechanisms that produce that IT population basis from the visual image? By using machine learning methods to search a set biologically-constrained neural network architectures for those that have high object recognition performance, we have built populations of model “neurons” that are very good predictors of the responses properties of IT neurons, even though these models were not optimized to fit those neural responses directly. Similarly, we found that “neurons” in intermediate layers of these models are also very good predictors of intermediate ventral stream layers (V4). This suggests that these networks contain key neural mechanisms that produce the IT population basis and its support of human object recognition - mechanisms we now aim to decipher.