Any sensory experience of the world, from the touch of a caress to the smile on our friend’s face, is embedded in time and it is often associated with the perception of the flow of it. The perception of time is therefore a peculiar sensory experience built without dedicated sensors. How the perception of time and the content of a sensory experience interact to give rise to this unique percept is unclear. A few empirical evidences show the existence of this interaction, for example the speed of a moving object or the number of items displayed on a computer screen can bias the perceived duration of those objects. However, to what extent the coding of time is embedded within the coding of the stimulus itself, is sustained by the activity of the same or distinct neural populations and subserved by similar or distinct neural mechanisms is far from clear. Addressing these puzzles represents a way to gain insight on the mechanism(s) through which the brain represents the passage of time. In my talk I will present behavioral and neuroimaging studies to show how concurrent changes of visual stimulus duration, speed, visual contrast and numerosity, shape and modulate brain’s and pupil’s responses and, in case of numerosity and time, influence the topographic organization of these features along the cortical visual hierarchy.

Precisely estimating event timing is essential for survival, yet temporal distortions are ubiquitous in our daily sensory experience. Here, we tested whether the relative position, duration, and distance in time of two sequentially-organized events—standard S, with constant duration, and comparison C, with duration varying trial-by-trial—are causal factors in generating temporal distortions. We found that temporal distortions emerge when the first event is shorter than the second event. Importantly, a significant interaction suggests that a longer inter-stimulus interval (ISI) helps to counteract such serial distortion effect only when the constant S is in the first position, but not if the unpredictable C is in the first position. These results imply the existence of a perceptual bias in perceiving ordered event durations, mechanistically contributing to distortion in time perception. Our results clarify the mechanisms generating time distortions by identifying a hitherto unknown duration-dependent encoding inefficiency in human serial temporal perception, something akin to a strong prior that can be overridden for highly predictable sensory events but unfolds for unpredictable ones.

To generate brain circuits that are both flexible and stable requires the coordination of powerful developmental mechanisms acting at different scales, including activity-dependent synaptic plasticity and changes in single neuron properties. The brain prepares to efficiently compute information and reliably generate behavior during early development without any prior sensory experience but through patterned spontaneous activity. After the onset of sensory experience, ongoing activity continues to modify sensory circuits, and plays an important functional role in the mature brain. Using quantitative data analysis, experiment-driven theory and computational modeling, I will present a framework for how neural circuits are built and organized during early postnatal development into functional units, and how they are modified by intact and perturbed sensory-evoked activity. Inspired by experimental data from sensory cortex, I will then show how neural circuits use the resulting non-random connectivity to flexibly gate a network’s response, providing a mechanism for routing information.

Three intimately related dimensions of the mammalian genome—linear DNA sequence, gene transcription, and 3D genome architecture—are crucial for the development of nervous systems. Changes in the linear genome (e.g., de novo mutations), transcriptome, and 3D genome structure lead to debilitating neurodevelopmental disorders, such as autism and schizophrenia. However, current technologies and data are severely limited: (1) 3D genome structures of single brain cells have not been solved; (2) little is known about the dynamics of single-cell transcriptome and 3D genome after birth; (3) true de novo mutations are extremely difficult to distinguish from false positives (DNA damage and/or amplification errors). Here, I filled in this longstanding technological and knowledge gap. I recently developed a high-resolution method—diploid chromatin conformation capture (Dip-C)—which resolved the first 3D structure of the human genome, tackling a longstanding problem dating back to the 1880s. Using Dip-C, I obtained the first 3D genome structure of a single brain cell, and created the first transcriptome and 3D genome atlas of the mouse brain during postnatal development. I found that in adults, 3D genome “structure types” delineate all major cell types, with high correlation between chromatin A/B compartments and gene expression. During development, both transcriptome and 3D genome are extensively transformed in the first month of life. In neurons, 3D genome is rewired across scales, correlated with gene expression modules, and independent of sensory experience. Finally, I examined allele-specific structure of imprinted genes, revealing local and chromosome-wide differences. More recently, I expanded my 3D genome atlas to the human and mouse cerebellum—the most consistently affected brain region in autism. I uncovered unique 3D genome rewiring throughout life, providing a structural basis for the cerebellum’s unique mode of development and aging. In addition, to accurately measure de novo mutations in a single cell, I developed a new method—multiplex end-tagging amplification of complementary strands (META-CS), which eliminates nearly all false positives by virtue of DNA complementarity. Using META-CS, I determined the true mutation spectrum of single human brain cells, free from chemical artifacts. Together, my findings uncovered an unknown dimension of neurodevelopment, and open up opportunities for new treatments for autism and other developmental disorders.

Behavioral experience shapes neural circuits, adding and subtracting connections between neurons that will ultimately control sensation and perception. We are using natural sensory experience to uncover basic principles of information processing in the cerebral cortex, with a focus on how sensory learning can selectively alter synaptic strength. I will discuss recent findings that differentiate reinforcement learning from sensory experience, showing rapid and selective plasticity of thalamic and inhibitory synapses within primary sensory cortex.

Several lines of research have demonstrated that internally generated sensory experience - such as during memory, dreaming and mental imagery - activates similar neural representations as externally triggered perception. This overlap raises a fundamental challenge: how is the brain able to keep apart signals reflecting imagination and reality? In a series of online psychophysics experiments combined with computational modelling, we investigated to what extent imagination and perception are confused when the same content is simultaneously imagined and perceived. We found that simultaneous congruent mental imagery consistently led to an increase in perceptual presence responses, and that congruent perceptual presence responses were in turn associated with a more vivid imagery experience. Our findings can be best explained by a simple signal detection model in which imagined and perceived signals are added together. Perceptual reality monitoring can then easily be implemented by evaluating whether this intermixed signal is strong or vivid enough to pass a ‘reality threshold’. Our model suggests that, in contrast to self-generated sensory changes during movement, our brain does not discount self-generated sensory signals during mental imagery. This has profound implications for our understanding of reality monitoring and perception in general.

Sensory experience often depends on one’s own actions, including self-motion. Theories of predictive coding postulate that actions are regulated by calculating prediction error, which is the difference between sensory experience and expectation based on self-generated actions. Signals consistent with prediction error have been reported in mouse visual cortex (V1) when visual flow coupled to running was unexpectedly stopped. Here, we show such signals can be elicited by visual stimuli uncoupled to animal’s running. We recorded V1 neurons while presenting drifting gratings that unexpectedly stopped. We found strong responses to visual perturbations, which were enhanced during running. Perturbation responses were strongest in the preferred orientation of individual neurons and perturbation responsive neurons were more likely to prefer slow visual speeds. Our results indicate that prediction error signals can be explained by the convergence of known motor and sensory signals, providing a purely sensory and motor explanation for purported mismatch signals.

Sensory stimuli are often predictable consequences of one’s actions, and behavior exerts a correspondingly strong influence over sensory responses in the brain. Closed-loop experiments with the ability to control the sensory outcomes of specific animal behaviors have revealed that neural responses to self-generated sounds are suppressed in the auditory cortex, suggesting a role for prediction in local sensory processing. However, it is unclear whether this phenomenon derives from a precise movement-based prediction or how it affects the neural representation of incoming stimuli. We address these questions by designing a behavioral paradigm where mice learn to expect the predictable acoustic consequences of a simple forelimb movement. Neuronal recordings from auditory cortex revealed suppression of neural responses that was strongest for the expected tone and specific to the time of the sound-associated movement. Predictive suppression in the auditory cortex was layer-specific, preceded by the arrival of movement information, and unaffected by behavioral relevance or reward association. These findings illustrate that expectation, learned through motor-sensory experience, drives layer-specific predictive processing in the mouse auditory cortex.

Using a combination of in vivo imaging of neuronal circuit functional and structural dynamics, we have investigated the mechanisms by which patterned neural activity and sensory experience alter connectivity in the developing brain. We have identified, in addition to the long-hypothesized Hebbian structural plasticity mechanisms, a kind of plasticity induced by the absence of correlated firing that we dubbed “Stentian plasticity”. In the talk I will discuss the phenomenology and some mechanistic insights regarding Stentian mechanisms in brain development. Further, I will show how glia may have a key role in circuit remodeling during development. These studies have led us to an appreciation of the importance of neuron-glia interactions in early development and the ability of patterned activity to guide circuit wiring.

The developmental journey of cortical interneurons encounters several activity-dependent milestones. During the early postnatal period in developing mice, GABAergic neurons are transient preferential recipients of thalamic inputs and undergo activity-dependent migration arrest, wiring and programmed cell-death. But cortical GABAergic neurons are also specified by very early developmental programs. For example, the earliest born GABAergic neurons develop into hub cells coordinating spontaneous activity in hippocampal slices. Despite their importance for the emergence of sensory experience, their role in coordinating network dynamics, and the role of activity in their integration into cortical networks, the collective in vivo dynamics of GABAergic neurons during the neonatal postnatal period remain unknown. Here, I will present data related to the coordinated activity between GABAergic cells of the mouse barrel cortex and hippocampus in non-anesthetized pups using the recent development of all optical methods to record and manipulate neuronal activity in vivo. I will show that the functional structure of developing GABAergic circuits is remarkably patterned, with segregated assemblies of prospective parvalbumin neurons and highly connected hub cells, both shaped by sensory-dependent processes.

What happens when the nervous system fails to adapt? Our perception of the world relies on a nervous system that learns and adapts to sensory information. Based on our experience we can predict what a wooden surface will feel like, that fire is hot, and that a gentle caress from a partner can be soothing. But our sensory experience of the world is not static – warm water can feel like fire on sunburned skin and the gentle brush of our clothes can be excruciating after an injury. In pathological conditions such as chronic pain, changes in nervous system function can cause normally innocuous sensory stimuli to be perceived as aversive or painful long after the initial injury has happened. These changes can sometimes be similar to the formation of a pain ‘memory’ that can modulate and distort our perception of sensory information. Our research program seeks to understand how fundamental processes that govern the formation and maintenance of plastic changes in the nervous system can lead to pathological conditions and how we can reverse engineer these changes to treat chronic conditions.

Neocortical networks must generate and maintain stable activity patterns despite perturbations induced by learning and experience- dependent plasticity. There is abundant theoretical and experimental evidence that network stability is achieved through homeostatic plasticity mechanisms that adjust synaptic and neuronal properties to stabilize some measure of average activity, and this process has been extensively studied in primary visual cortex (V1), where chronic visual deprivation induces an initial drop in activity and ensemble average firing rates (FRs), but over time activity is restored to baseline despite continued deprivation. Here I discuss recent work from the lab in which we followed this FR homeostasis in individual V1 neurons in freely behaving animals during a prolonged visual deprivation/eye-reopening paradigm. We find that - when FRs are perturbed by manipulating sensory experience - over time they return precisely to a cell-autonomous set-point. Finally, we find that homeostatic plasticity is perturbed in a mouse model of Autism spectrum disorder, and this results in a breakdown of FRH within V1. These data suggest that loss of homeostatic plasticity is one primary cause of excitation/inhibition imbalances in ASD models. Together these studies illuminate the role of stabilizing plasticity mechanisms in the ability of neocortical circuits to recover robust function following challenges to their excitability.

Many innate behaviours are the result of multiple sensorimotor programs that are dynamically coordinated to produce higher-order behaviours such as courtship or architecture construction. Extendend phenotypes such as architecture are especially useful for ethological study because the structure itself is a physical record of behavioural intent. A particularly elegant and easily quantifiable structure is the spider orb-web. The geometric symmetry and regularity of these webs have long generated interest in their behavioural origin. However, quantitative analyses of this behaviour have been sparse due to the difficulty of recording web-making in real-time. To address this, we have developed a novel assay enabling real-time, high-resolution tracking of limb movements and web structure produced by the hackled orb-weaver Uloborus diversus. With its small brain size of approximately 100,000 neurons, the spider U. diversus offers a tractable model organism for the study of complex behaviours. Using deep learning frameworks for limb tracking, and unsupervised behavioural clustering methods, we have developed an atlas of stereotyped movement motifs and are investigating the behavioural state transitions of which the geometry of the web is an emergent property. In addition to tracking limb movements, we have developed algorithms to track the web’s dynamic graph structure. We aim to model the relationship between the spider’s sensory experience on the web and its motor decisions, thereby identifying the sensory and internal states contributing to this sensorimotor transformation. Parallel efforts in our group are establishing 2-photon in vivo calcium imaging protocols in this spider, eventually facilitating a search for neural correlates underlying the internal and sensory state variables identified by our behavioural models. In addition, we have assembled a genome, and are developing genetic perturbation methods to investigate the genetic underpinnings of orb-weaving behaviour. Together, we aim to understand how complex innate behaviours are coordinated by underlying neuronal and genetic mechanisms.

The development of neural circuits is profoundly impacted by both spontaneous and sensory experience. This is perhaps most well studied in the visual system, where disruption of early spontaneous activity called retinal waves prior to eye opening and visual deprivation after eye opening leads to alterations in the response properties and connectivity in several visual centers in the brain. We address this question in the retina, which comprises multiple circuits that encode different features of the visual scene, culminating in over 40 different types of retinal ganglion cells. Direction-selective ganglion cells respond strongly to an image moving in the preferred direction and weakly to an image moving in the opposite, or null, direction. Moreover, as recently described (Sabbah et al, 2017) the preferred directions of direction selective ganglion cells cluster along four directions that align along two optic flow axes, causing variation of the relative orientation of preferred directions along the retinal surface. I will provide recent progress in the lab that addresses the role of visual experience and spontaneous retinal waves in the establishment of direction selective tuning and direction selectivity maps in the retina.