Feedback pathways

…[1] - bypassing certain layers, and feedback pathways which allow information to flow from later (multisensory) to earlier (‘unisensory’) parts of the network [2]. However, pure-feedforward models have been shown to be optimal for many multisensory tasks [3,4], commonly used to test experimental subjects or computational models; raising the question, what role do non-

Goal-directed behavior in dynamic environments requires animals to predict relevant changes, such as the occurrence of reinforcement, from the presence of sensory cues. This has been formalized in the prediction error theory, where the difference between the predicted and the received reinforcement serves as a driving force in the learning process to optimize future reinforcement prediction and to enable anticipatory approach or avoidance behavior. In the fruit fly brain, dopaminergic neurons in the mushroom body, a central structure for learning and memory, have been suggested to compute such

Mechanisms of top-down modulation in sensory perception and their relation to the underlying connectivity are not completely understood. Here we present a computational model of two interconnected cortical areas and use it to study the impact of inter-areal connectivity on sensory processing. The model integrates a multitude of data from rodent primary somatosensory cortex and reproduces biological features across multiple scales: from tens of ion channel models and electrical types, to thousands of morphologically detailed neurons, to millions of stochastic synapses mediating local and long-r

Spatial vision is the ability to perceive visual objects within 3-dimensional space, and its dysfunction is detrimental to our ability to interact with the visual world. Our visual experience is a result of the concerted activity of neuronal ensembles in the sensory hierarchy. How the organization of objects in space influences interactions in this hierarchy is poorly understood. We investigated this question in the inter-laminar interactions in the visual cortex, a canonical motif of hierarchical information processing. We analyzed laminar electrophysiological recordings in cortical area V1 w

We live in highly structured environments where patterns often repeat. Our brain forms internal models of the world by extracting the regularities. Such models are utilised to form predictions about future events and incoming information. Predictions lead to efficient neural processing of incoming stimuli and hence facilitate perception. Although the existence of prediction signals is known, much less is known about the neural mechanisms underlying the propagation of prediction signals, especially in higher-vision. Predictive processing theories propose that in cortical hierarchies, high-leve

Dynamic pathways of information flow between distributed brain regions underlie the diversity of behavior. However, it remains unclear how neuronal activity in one area causally influences ongoing population activity in another, and how such interactions change over time. Here we introduce a causal approach to quantify cortical interactions by pairing simultaneous electrophysiological recordings with neural perturbations. We found that the influence primary visual cortex (V1) and higher visual area LM had on each other was surprisingly variable over time. Both feedforward and feedback pathways

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well-established that it depends on pre and postsynaptic activity. However, models that rely solely on pre and postsynaptic activity for synaptic changes have, to date, not been able to account for learning complex tasks that demand hierarchical networks. Here, we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then neurons higher in the hierarchy can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when

Synaptic plasticity is believed to be a key physiological mechanism for learning. It is well-established that it depends on pre and postsynaptic activity. However, models that rely solely on pre and postsynaptic activity for synaptic changes have, to date, not been able to account for learning complex tasks that demand hierarchical networks. Here, we show that if synaptic plasticity is regulated by high-frequency bursts of spikes, then neurons higher in the hierarchy can coordinate the plasticity of lower-level connections. Using simulations and mathematical analyses, we demonstrate that, when