Sensory representations

Observation of neural firing patterns can constrain theories for the types of activity patterns that the brain uses to guide behavior. However, directly perturbing neural activity, ideally with great specificity, is required to causally test any particular theory. We combined two-photon (2p) imaging and cellular resolution optogenetic photostimulation1 to causally test how neural activity in the mouse primary visual cortex (V1) is read out to detect visual stimuli. Both an unweighted mean readout or a weighted read out of visually sensitive neurons could support detection, but the weighted rea

…predictive cue representation in the sensory cortex. However, evidence is growing that animals learn more rapidly than classical performance measures suggest, challenging the prevailing model of sensory cortical plasticity. Here, we investigated the relationship between learning and sensory cortical representations. We traine

…electric signals from external sensory cues, showing how predictive plasticity and two-stage signal cancellation mediate a sense of self. Sompolinsky explores attractor networks, revealing how discrete and continuous attractors can stabilize activity patterns, enable working memory, and incorporate chaot

…modulatory effects of movement on sensory coding. Surprisingly, these effects are not consistent across species. For example, in rodents and insects locomotion increases the magnitude of visual responses [1-3], while in primates, locomotion has a weak suppressive influence [4]. These differences raise intriguing questions about the computational purpose of such modulations and the generality of the underlying principles of sensory processing. Here, we address these questions from a theoretical perspective. Our approach is groun

Cognitive maps are abstract internal representations of the external world that facilitate planning and efficient navigation. How the brain learns such maps from variable sensory experience remains elusive. Previous models either relied on discrete environments [1], linear embeddings of continuous states [2], or generative models trained by predicting sensory input [3]. However, the world is neither discrete nor linear, and the brain presumably does not learn a generative model of its sensory inputs. So, how can the brain learn a map and use it to plan strategically? In this work, we develop

Spontaneous activity in young ferrets (p22) is organised into modular, low-dimensional patterns across multiple cortical areas [1]. In the following two weeks (p22-43), these activity patterns undergo a transition similar in all areas, and show a gradual increase in dimensionality in most areas, suggesting an expansion of representational capacity [2]. However, it is unclear what the functional role of having low-dimensional activity patterns at the initial stage of development is, since one would expect the limited dimensionality to restrict representational learning. In this work, we investi

Increasing evidence indicates that anesthetics strongly affect sensory representations and network dynamics in the thalamo-cortical circuits. By contrast, processing in the early sensory systems is thought to be mostly preserved under anesthesia, based on stimulus tuning properties like those observed in wakefulness. However, in the absence of recordings tracking the activity of the same neurons across states, there is no evidence that anesthesia preserves peripheral one-to-one sensory representations. Here, tracking for the first time the same cochlear nucleus neurons across wakefulness and i

…by the animal’s prior experience to sensory cues. Such adaptive behaviors are shown to be mediated by distinct zebrafish forebrain nuclei that are homologues to mammalian cortico-limbic circuits. Our lab has previously shown structured resting state activity in the zebrafish forebrain, where ensembles of spatially organized neurons exhibit correlated activity. In the mammalian cortex, distinct classes of GABAergic neurons were show to play diverse roles in organizing su

…Sensory reactivation involves the recurrence of a pattern of neural activity during sleep or quiet waking that previously occurred during a sensory experience. Prevailing theories of offline memory consolidation posit that the pattern of neurons activated during a sensory experience will be faithfully reactivated, thereby stabilizing the entire pattern. However, sensory-evoked patterns are not stable, but instead drift across repeated experiences. Our stud

Sensory cortices maintain robust sensory representations despite an inevitable loss of neurons during aging and even during the prodromal stages of neurodegenerative diseases. However, the mechanisms that safeguard sensory representations and provide the cortical network with robustness against the loss of neurons are largely unknown. Here, we tested in how far the targeted elimination of functionally identified neurons affects the dynamics of sound-evoked population responses and could trigger homeostatic processes at the network level. We combined longitudinal two-photon calcium imaging of p

…that neural response properties of sensory representations are strongly shaped by the need

…to attentive processing of sensory input. We argue that this intuitively appealing account is not only inaccurate, but has led to substantial conceptual confusion (to the point where some attention researchers offer to abandon the term ‘attention’ altogether). As an alternative, we offer a “diachronic” framework that describes attentional selectivity as a process that unfolds over time. Key to this view is the concept of attentional epi

Sensory representations are typically thought as neuronal activity patterns that encode physical attributes of the outside world. However, increasing evidence is showing that as animals learned the association between a sensory stimulus and its behavioral relevance, stimulus representation in sensory cortical areas can change. In this seminar I will present recent experiments from our lab showing that the activity in the olfactory piriform cortex (PC) of mice encodes not only odor information, but also non-olfactory variables associated with the behavioral task. By developing an associative ol