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Cortical processing

8 results

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Passive odor exposure can change olfactory perception beyond simple pattern separation. Recent studies reveal that principal neurons in the mouse olfactory bulb exhibit bi-directional changes in population- level representations that depend on input similarity. Our analysis reveals the presence of a systematic drift, manifested as rotation of population vectors away from their initial representations. We explain these phenomena using a recurrent inhibition feedback model with unsupervised Hebbian learning. Our model predicts a power- law decay in mean population activity over days of odor expo

ePoster · Computational Neuroscience

Dual role, single pathway: A pyramidal cell model of feedback integration in function and learning

Daniel Schmid, Christian Jarvers, Timo Oess, Heiko Neumann · Bernstein Conference 2024

Successful integration of feedforward and feedback signals play a major role in cortical processing [1]. Pyramidal cells are thought to be the main sites to perform this integration locally. Signals from the two different pathways are integrated at distinct sites of the pyramidal cell [2]. Information originating up-stream, closer to primary sensory input, is integrated at basal peri-somatic dendrites and drives the cell's firing. Contextual information from down-stream sources is integrated at distal apical dendrites and amplifies the cell's firing only if coinciding with basal input. Importa

ePoster · Computational Neuroscience

Cholinergic neuromodulation of processing sensory and reinforcement signals

Natalia Babushkina, Peter Severin Graff, Fatemeh Yousefi, Simon Musall · FENS Forum 2024

…to regulate arousal, sensory processing and learning. Although the cholinergic system is one of the extensively studied neuromodulatory system, its impact on cortical processing remains unclear. Earlier studies have shown that BFCNs in rostral and caudal basal forebrain nuclei respond largely similar to reward and punishment despite their non-overlapping cortical projections, suggesting that BFCNs act as a unified broadcasting system. In contrast, more recent findings revealed dist

ePoster · Neuroscience

Interoceptive processing in the standing position is attenuated compared to the sitting position

Mayu Dohata, Naotsugu Kaneko, Ryogo Takahashi et al. · FENS Forum 2024

…influences cognitive and emotional processing, understanding how postural control influences interoception could contribute to reveal the neural basis of its effects on cognition and emotion. Here, we aimed to investigate the effects of posture on interoception by using heartbeat evoked potentials (HEPs), which are event-related potentials that reflect the cortical processing of heartbeats.T

ePoster · Neuroscience

Neural encoding of sensory “surprise” in the mouse cortex

Diego Benusiglio, Sofija Perovic, Richard Somervail et al. · FENS Forum 2024

“Surprising” sensory stimuli trigger a large and widespread electrocortical activity in the mammal brain – the vertex potential – that is coupled with marked changes in behavioral output. It is, however, still unclear how “surprise” is represented at different hierarchical levels of cortical processing, as well as at different spatiotemporal scales, ranging from individual neurons to large cortical areas. Also, if and how surprise at individual neuron level relates to the widespread surprise-related vertex potential widely studied in humans remains elusive.To answer those questions, we recorde

ePoster · Neuroscience

Emergence of convolutional structure in neural circuits

Alessandro Ingrosso, Sebastian Goldt · COSYNE 2022

Exploiting invariances in the inputs is crucial for constructing efficient representations and accurate predictions in neural circuits. In neuroscience, translation invariance is at the heart of models of the visual system, while convolutional neural networks designed to exploit translation invariance triggered the first wave of deep learning successes. While the hallmarks of convolutions, namely localised receptive fields that tile the input space, can be implemented with fully-connected neural networks, learning convolutions directly from inputs in a fully-connected network has so far proven

ePoster · Computational Neuroscience

Fast cortical processing is required in many scenarios where both sensory input and the corresponding cognitive responses change rapidly. Therefore, efficient learning in such networks needs to tackle the issue of credit assignment continuously, in real time. Recent years have witnessed a surge of cortical learning models which address this question by approximating the error backpropagation algorithm. However, all of these either require long relaxation phases following a change in sensory stimuli, which renders them unable to cope with the fast time scales imposed by, e.g., saccades, or impo

ePoster · Computational Neuroscience

Higher-order thalamic nuclei have extensive connections with various cortical areas. Yet their functionals roles remain not well understood. In our recent studies, using optogenetic and chemogenetic tools we manipulated the activity of a higher-order thalamic nucleus, the lateral posterior nucleus (LP, analogous to the primate pulvinar nucleus) and its projections and examined the effects on sensory discrimination and information processing functions in the cortex. We found an overall suppressive effect on layer 2/3 pyramidal neurons in the cortex, resulting in enhancements of sensory featu

Fri, Nov 13, 2020 · 06:00 UTC

Seminar · Neuroscience