The Orbitofrontal Cortex, Food Reward, Body Weight, and Obesity

In primates including humans, the orbitofrontal cortex is the key brain region representing the reward value and subjective pleasantness of the sight, smell, taste and texture of food. At stages of processing before this, in the insular taste cortex and inferior temporal visual cortex, the identity of the food is represented, but not its affective value. In rodents, the whole organisation of reward systems appears to be different, with reward value reflected earlier in processing systems. In primates and humans, the amygdala is overshadowed by the great development of the orbitofrontal cortex. Social and cognitive factors exert a top-down influence on the orbitofrontal cortex, to modulate the reward value of food that is represented in the orbitofrontal cortex. Recent evidence shows that even in the resting state, with no food present as a stimulus, the liking for food, and probably as a consequence of that body mass index, is correlated with the functional connectivity of the orbitofrontal cortex and ventromedial prefrontal cortex. This suggests that individual differences in these orbitofrontal cortex reward systems contribute to individual differences in food pleasantness, and obesity. Implications of how these reward systems in the brain operate for understanding, preventing, and treating obesity are described.

The ventral visual system

The brain processes involved in visual object recognition are described. Evidence is presented that what is computed are sparse distributed representations of objects that are invariant with respect to transforms including position, size, and even view in the ventral stream towards the inferior temporal visual cortex. Then biologically plausible unsupervised learning mechanisms that can perform this computation are described that use a synaptic modification rule what utilises a memory trace. These are compared with deep learning and other machine learning approaches that require supervision.

An Information Theoretic Approach to the Contributions of the Firing Rates and the Correlations Between the Firing of Neurons

To analyze the extent to which populations of neurons encode information in the numbers of spikes each neuron emits or in the relative time of firing of the different neurons that might reflect synchronization, we developed and analyzed the performance of an information theoretic approach. The formula quantifies the corrections to the instantaneous information rate that result from correlations in spike emission between pairs of neurons. We showed how these cross-cell terms can be separated from the correlations that occur between the spikes emitted by each neuron, the auto-cell terms in the information rate expansion. We also described a method to test whether the estimate of the amount of information contributed by stimulus-dependent synchronization is significant. With simulated data, we show that the approach can separate information arising from the number of spikes emitted by each neuron from the redundancy that can arise if neurons have common inputs and from the synergy that can arise if cells have stimulus-dependent synchronization. The usefulness of the approach is also demonstrated by showing how it helps to interpret the encoding shown by neurons in the primate inferior temporal visual cortex. When applied to a sample dataset of simultaneously recorded inferior temporal cortex neurons, the algorithm showed that most of the information is available in the number of spikes emitted by each cell; that there is typically just a small degree (approximately 12%) of redundancy between simultaneously recorded inferior temporal cortex (IT) neurons; and that there is very little gain of information that arises from stimulus-dependent synchronization effects in these neurons.