Notes

Unsupervised Learning in the Brain Papers: "Unsupervised Yearning", "Complementary Roles of Basal Ganglia and Cerebellum in Learning and Motor Control", "Development of the Brain Depends on the Visual Environment".

We are aware of the fact that the brain as well uses unsupervised learning. Several examples were collected for the lecture. In 1970 an experiment was conducted on cats. The kittens were housed from birth in a completely dark room, but from the age of 2 weeks they were put in a special apparatus for an average of about 5 hours a day. The kitten stood on a clear glass platform inside a tall cylinder of which the entire surface was covered in black and white stripes (in different experiments they used horizontal and vertical stripes). Those poor cats then were virtually blind for contours perpendicular to the orientation they had experienced. They recorded single neurons from primary visual cortex and found that almost all cats had their neurons trained to be most selective in direction of the stripes presented in the experiment. Interpretation: the neurons are the cluster centers and they move around during learning (growing up). When presented one stimulus only, then all cluster centers group in the same optimum.

In the picture we have a spike rate curve of a single neuron with respect to the neurons orientation. Experiments show that this distribution changes in adolescent subjects and becomes more rigid with increasing age.

Another group analyzed visual cortical activity of awake ferrets during development (2007). They provide a one-sentence summary: The relation between spontaneous activity and activity evoked by natural stimuli in the primary visual cortex reveals that the cortical circuit progressively adapts its internal model to the statistical structure of the environment. Paper: "Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment".

In the figures: Notation: Evoked and spontaneous (dark) neural activity (EA and SA). Multi-neural EA (aEA). In the top-left figure, the posterior distribution represented by EA is increasingly dominated by the prior distribution as brightness or contrast is decreased. In the right figure, ferrets either receiving no stimulus (middle) or viewing natural (top) or artificial stimuli (bottom) is used to construct neural activity distributions in young and adult animals. It reveals the level of statistical adaptation of the internal model to the stimulus ensemble. The internal model of young animals (left) is expected to show little adaptation to the natural environment and thus aEA for natural (and also for artificial) scenes should be different from SA. Adult animals (right) are expected to have adapted to natural scenes and thus to exhibit a high degree of similarity between SA and natural stimuli aEA, but not between SA and artificial stimuli aEA.

We now know that these distributions adapt, but from the presented experiments it is unclear what the conditions are to trigger an adaptation. Another experiment ("Stimuling Timing-Dependent Plasticity in Cortical processing of Orientation") shows that the relative timing of presynaptic and postsynaptic spikes plays a critical role in activity-induced synaptic orientation 9single unit recording in cat V1). Induction of a significant shift required that the interval between the pair fall within +- 40ms otherwise nothing changed. Another path to understand the learning in neural circuits leads to the recent advances in Deep Neural Networks (DNN). Several groups tried to map layers (as in DNN layers) to cortical regions. Several mapping strategies were found. We can show that dissimilarity matrices of regions in both systems look similar, especially in higher cortical regions vs deeper layers of neural networks. Interestingly, the animals we recorded from never knew any labels that were used to train the DNNs.

1st Experiment: The statistics of the neuronal activity has adapted to represent the input data statistics (spatial). 2nd Experiment: The statistics of the neuronal activity has adapted to represent the input temporal data statistics.

In the picture we have the confusion matrix of V4 neural units and units in artificial networks from a comparable depth.