Topic: ventral stream

Brains often infer low-dimensional features from high-dimensional input. For example, during visual processing a picture captured by the retina is processed hierarchically through the ventral stream, which gradually infers higher-level features encoded by the lower number of neurons. However, it is still unclear how neural networks learn to perform such tasks. Hebbian Learning (HL), a local form of plasticity that uses pre- and postsynaptic activity to change synaptic efficacy, has been used extensively in receptive field models, sparse coding, independent and principal component analysis, an

A recent approach to understanding the mammalian visual system is to show correspondence between the sequential stages of processing in the ventral stream with layers in a deep convolutional neural network (DCNN), providing evidence that visual information is processed hierarchically, with successive stages containing ever higher-level information. However, correspondence is usually defined as shared variance between brain region and model layer. We propose that task-relevant variance is a stricter test: If a DCNN layer corresponds to a brain region, then substituting the model’s activity with