Topic: decoding

Natural soundscapes such as from a forest, a busy restaurant, or a busy intersection are generally composed of a cacophony of sounds that the brain needs to interpret either independently or collectively. In certain instances sounds - such as from moving cars, sirens, and people talking - are perceived in unison and are recognized collectively as single sound (e.g., city noise). In other instances, such as for the cocktail party problem, multiple sounds compete for attention so that the surrounding background noise (e.g., speech babble) interferes with the perception of a single sound source (

New memories are initially labile and have to be consolidated into stable long-term representations. Current theories assume that this is supported by a shift in the neural substrate that supports the memory, away from rapidly plastic hippocampal networks towards more stable representations in the neocortex. Rehearsal, i.e. repeated activation of the neural circuits that store a memory, is thought to crucially contribute to the formation of neocortical long-term memory representations. This may either be achieved by repeated study during wakefulness or by a covert reactivation of memory traces

Individual neurons in visual cortex provide the brain with unreliable estimates of visual features. It is not known if the single-neuron variability is correlated across large neural populations, thus impairing the global encoding of stimuli. We recorded simultaneously from up to 50,000 neurons in mouse primary visual cortex (V1) and in higher-order visual areas and measured stimulus discrimination thresholds of 0.35 degrees and 0.37 degrees respectively in an orientation decoding task. These neural thresholds were almost 100 times smaller than the behavioral discrimination thresholds reported

Visual attention selects only a tiny fraction of visual input information for further processing. Selection starts in the primary visual cortex (V1), which creates a bottom-up saliency map to guide the fovea to selected visual locations via gaze shifts. This motivates a new framework that views vision as consisting of encoding, selection, and decoding stages, placing selection on center stage. It suggests a massive loss of non-selected information from V1 downstream along the visual pathway. Hence, feedback from downstream visual cortical areas to V1 for better decoding (recognition), through

Information is represented in the brain by the coordinated activity of populations of neurons. Recent large-scale neural recording methods in combination with machine learning algorithms are helping understand how sensory processing and cognition emerge from neural population activity. This talk will explore the most popular machine learning methods used to gather meaningful low-dimensional representations from higher-dimensional neural recordings. To illustrate the potential of these approaches, Pedro will present his research in which chemical information is decoded from the olfactory system