Orientation columns

A characteristic feature of developing sensory circuits is spontaneous co-activations that initiate organized networks and maps in preparation for sensory experience. In species such as primates and carnivores, the mature visual cortex is characterized by iterated clusters of similarly responding neurons, or modules, and provides an excellent model system for exploring the mechanisms underlying the development of functional network organization. The sequence of early events that leads to the initial emergence of modular network structure remains unknown. Interestingly during this period of ear

Cortical visual prostheses are designed to restore vision by stimulating neurons, inducing visual perceptions in people with impaired vision. To obtain useful visual perception, it is essential to induce, through stimulation, activity that closely mimics that of natural vision. Existing V1 prostheses stimulation strategies primarily focus on retinotopic stimulation, overlooking other important encoding properties and thereby failing to replicate the complex neural activity patterns of natural vision. Our work introduces a novel stimulation protocol that targets cortical tissue according to bot

A fundamental property of neurons in the primary visual cortex is their preference for orientation of edges. Recent studies further consolidated the finding that the spatial arrangement of orientation selective neurons in orientation domains and pinwheels is invariant across primates, carnivores and potentially even in some marsupials. No functional advantage favoring this universal common design is currently know. Here we address the question of whether the universal common design can provide a functional advantage over alternative designs. We formulate a mathematical theory formalizing the h

Complex spatio-temporal neural population dynamics such as traveling waves are known to exist across multiple brain regions (Lubenov & Siapas, 2009; Muller et al., 2014), and have been hypothesized to play diverse roles from information transfer (Sato et al., 2012) to long-term memory consolidation (Muller et al., 2016). To-date, however, the empirical validation of these computational hypotheses has been hindered by the lack of a flexible and efficiently trainable model of such behavior. In this work, we introduce the Locally Coupled Oscillatory Recurrent Neural Network (LocoRNN), and sho