A Guide for the Multiplexed: The Development of Visual Feature Maps in the Brain

Representational maps in the brain: concepts, approaches, and applications

Neural systems have evolved to process sensory stimuli in a way that allows for efficient and adaptive behavior in a complex environment. Recent technological advances enable us to investigate sensory processing in animal models by simultaneously recording the activity of large populations of neurons with single-cell resolution, yielding high-dimensional datasets. In this review, we discuss concepts and approaches for assessing the population-level representation of sensory stimuli in the form of a representational map. In such a map, not only are the identities of stimuli distinctly represented, but their relational similarity is also mapped onto the space of neuronal activity. We highlight example studies in which the structure of representational maps in the brain are estimated from recordings in humans as well as animals and compare their methodological approaches. Finally, we integrate these aspects and provide an outlook for how the concept of representational maps could be applied to various fields in basic and clinical neuroscience.

The development of bi-directionally coupled self-organizing neurovascular networks captures orientation-selective neural and hemodynamic cortical responses

Networks of neurons are the primary substrate of information processing. Conversely, blood vessels in the brain are generally viewed to have physiological functions unrelated to information processing, such as the timely supply of oxygen, and other nutrients to the neural tissue. However, recent studies have shown that cerebral microvessels, like neurons, exhibit tuned responses to sensory stimuli. Tuned neural responses to sensory stimuli may be enhanced with experience-dependent Hebbian plasticity and other forms of learning. Hence, it is possible that the microvascular network might also be subject to some form of competitive learning rules during early postnatal development such that its fine-scale structure becomes optimized for metabolic delivery to a given neural micro-architecture. To explore the possibility of adaptive lateral interactions and tuned responses in cerebral microvessels, we modelled the cortical neurovascular network by interconnecting two laterally connected self-organizing networks. The afferent and lateral connections of the neural and vascular networks were defined by trainable weights. By varying the topology of lateral connectivity in the vascular network layer, we observed that the partial correspondence of feature selectivity between neural and hemodynamic responses could be explained by lateral coupling across local blood vessels such that the central domain receives an excitatory drive of more blood flow and a distal surrounding region where blood flow is reduced. Critically, our simulations suggest a new role for feedback from the vascular to the neural network because the radius of vascular perfusion determines whether the cortical neural map develops into a clustered vs. salt-and-pepper organization.