Neural representation

To date, most studies of neural representation have focused on how a single stimulus is encoded in the spiking activity of neurons. Consequently, little is known about how sensory neurons can preserve information from multiple stimuli – a problem given that many sensory neurons have relatively broad receptive fields. Multiplexing is a neural encoding theory that posits that neurons temporally switch between encoding various stimuli when more than one stimulus is present. To date, the strongest statistical evidence for this theory comes from spike counts measured over long time windows, leading

Neural population signals recorded with magneto- (MEG) or electroencephalography (EEG) contain information about viewed natural images and their categories. However, it remains unclear which specific visual features contribute to this information. To address this, we applied spectral manipulations and different texturization algorithms to natural images of various categories and recorded MEG in human subjects presented with these stimuli. The employed stimulus space allowed us to attribute components of visual information to specific low-, mid- and high-level visual features. Furthermore, this

Neuronal ensembles encoding a specific memory have been shown to be distributed across multiple brain regions. However, the computational principles behind the distributed nature of such memory engrams are unknown. Here we propose that distributed engrams support functional flexibility and versatility. We investigated whether distributed engrams can differentially regulate memory discrimination and generalization, opposing and complementary computations that must be balanced for adaptive memory-guided behavior. For instance, while animals need to discriminate between threat-predictive and neut

Working memory (WM) enables retaining and manipulating information for brief periods. Attractor models have been developed for explaining diverse phenomena linked to WM, including error-correcting dynamics [1], cardinal repulsion [2], serial biases [3], and the like [4-5]. Yet, in the real-world, a relevant stimulus must be remembered despite interference from distractors that can occur unpredictably in time and anywhere in space. We provide novel experimental evidence, and suggest a computational mechanism, for biases induced by spatially and temporally unpredictable, task-irrelevant distract

…In contrast, the neural mechanisms controlling the selection of representations held ‘in mind’, in working memory, are unknown. In this talk, I will present evidence that the prefrontal cortex controls working memory by selectively enhancing and transforming the contents of working memory. In particular, I will show how the neural rep

Understanding the nature of neural representation is a central challenge of neuroscience. One common approach to this challenge is to compute receptive fields by correlating neural activity with external variables drawn from sensory signals. But these receptive fields are only meaningful to the experimenter, not the organism, because only the experimenter has access to both the neural activity and knowledge of the external variables. To understand neural representation more directly, recent methodological advances have sought to capture the intrinsic geometry of sensory driven neural responses

Evolution has endowed primates, including humans, with a powerful visual system, seemingly providing us with a detailed perception of our surroundings. But in reality the underlying process is one of active filtering, enhancement and reshaping. For visual motion perception, the dorsal pathway in primate visual cortex and in particular area MT/V5 is considered to be of critical importance. Combining physiological and psychophysical approaches we have used the processing and perception of visual motion and area MT/V5 as a model for the interaction of sensory (bottom-up) signals with cognitive (t

…that phase precession may be a general neural pattern for representing sequential events for learning and memory. By recording human single-neuron activity during spatial navigation, we

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 (

It has long been known that when viewing a set of images, we misjudge individual elements as being closer to the mean than they are (Hollingworth, 1910) and recall seeing the (absent) set mean (Deese, 1959; Roediger & McDermott (1995). Recent studies found that viewing sets of images, simultaneously or sequentially, leads to perception of set statistics (mean, range) with poor memory for individual elements. Ensemble perception was found for sets of simple images (e.g. circles varying in size or brightness; lines of varying orientation), complex objects (e.g. faces of varying emotion), as well