Neuroscience seminars

Despite the consistency of symptoms at the cognitive level, we now know that brain disorders like Autism and Schizophrenia can each arise from mutations in >100 different genes. Presumably there is a convergence of “symptoms” at the level of neural circuits in diagnosed individuals. In this talk I will argue that redundancy in neural circuit parameters implies that we should take a circuit-function rather that circuit-component approach to understanding these disorders. Then I will present our recent empirical work testing a circuit-function theory for Autism: the idea that neural circuits show excess trial-to-trial variability in response to sensory stimuli, and instability in the representations across a timescale of days. For this we analysed in vivo neural population activity data recorded from somatosensory cortex of mouse models of Fragile-X syndrome, a disorder related to autism. Work with Beatriz Mizusaki (Univ of Bristol), Nazim Kourdougli, Anand Suresh, and Carlos Portera-Cailliau (Univ of California, Los Angeles).

Electronic interfaces to the retina represent an exciting development in science, engineering, and medicine – an opportunity to exploit our knowledge of neural circuitry and function to restore or even enhance vision. However, although existing devices demonstrate proof of principle in treating incurable blindness, they produce limited visual function. Some of the reasons for this can be understood based on the precise and specific neural circuitry that mediates visual signaling in the retina. Consideration of this circuitry suggests that future devices may need to operate at single-cell, single-spike resolution in order to mediate naturalistic visual function. I will show large-scale multi-electrode recording and stimulation data from the primate retina indicating that, in some cases, such resolution is possible. I will also discuss cases in which it fails, and propose that we can improve artificial vision in such conditions by incorporating our knowledge of the visual system in bi-directional devices that adapt to the host neural circuitry. Finally, I will introduce the Stanford Artificial Retina Project, aimed at developing a retinal implant that more faithfully reproduces the neural code of the retina, and briefly discuss the implications for scientific investigation and for other neural interfaces of the future.

The dominant view of perception right now is that information travels from the environment to the sensory system, then to the nervous systems which processes it to generate a percept and behaviour. Ongoing behaviour is thought to occur largely through simple iterations of this process. However, this linear view, where information flows only in one direction and the properties of the environment and the sensory system remain static and unaffected by behaviour, is slowly fading. Many of us are beginning to appreciate that perception is largely active, i.e. that information flows back and forth between the three systems modulating their respective properties. In other words, in the real world, the environment and sensorimotor loop is pretty much always closed. I study the loop; in particular I study how the reverse arm of the loop affects sound and vibration perception. I will present two examples of motor modulation of perception at two very different temporal and spatial scales. First, in crickets, I will present data on how high-speed molecular motor activity enhances hearing via the well-studied phenomenon of active amplification. Second, in spiders I will present data on how body posture, a slow macroscopic feature, which can barely be called ‘active’, can nonetheless modulate vibration perception. I hope these results will motivate a conversation about whether ‘active’ perception is an optional feature observed in some sensory systems, or something that is ultimately necessitated by both evolution and physics.

Retinal projections often poorly represent the structure of the physical world: well-defined boundaries within the eye may correspond to irrelevant features of the physical world, while critical features of the physical world may be nearly invisible at the retinal projection. Visual cortex is equipped with specialized mechanisms for sorting these two types of features according to their utility in interpreting the scene, however we know little or nothing about their perceptual computations. I will present novel paradigms for the characterization of these processes in human vision, alongside examples of how the associated empirical results can be combined with targeted models to shape our understanding of the underlying perceptual mechanisms. Although the emerging view is far from complete, it challenges compartmentalized notions of bottom-up/top-down object segmentation, and suggests instead that these two modes are best viewed as an integrated perceptual mechanism.

The curse of dimensionality plagues models of reinforcement learning and decision-making. The process of abstraction solves this by constructing abstract variables describing features shared by different specific instances, reducing dimensionality and enabling generalization in novel situations. We characterized neural representations in monkeys performing a task where a hidden variable described the temporal statistics of stimulus-response-outcome mappings. Abstraction was defined operationally using the generalization performance of neural decoders across task conditions not used for training. This type of generalization requires a particular geometric format of neural representations. Neural ensembles in dorsolateral pre-frontal cortex, anterior cingulate cortex and hippocampus, and in simulated neural networks, simultaneously represented multiple hidden and explicit variables in a format reflecting abstraction. Task events engaging cognitive operations modulated this format. These findings elucidate how the brain and artificial systems represent abstract variables, variables critical for generalization that in turn confers cognitive flexibility.

Sensory cortices display a suite of ubiquitous dynamical features, such as ongoing noise variability, transient overshoots, and oscillations, that have so far escaped a common, principled theoretical account. We developed a unifying model for these phenomena by training a recurrent excitatory-inhibitory neural circuit model of a visual cortical hypercolumn to perform sampling-based probabilistic inference. The optimized network displayed several key biological properties, including divisive normalization, as well as stimulus-modulated noise variability, inhibition-dominated transients at stimulus onset, and strong gamma oscillations. These dynamical features had distinct functional roles in speeding up inferences and made predictions that we confirmed in novel analyses of awake monkey recordings. Our results suggest that the basic motifs of cortical dynamics emerge as a consequence of the efficient implementation of the same computational function — fast sampling-based inference — and predict further properties of these motifs that can be tested in future experiments

How does our brain rapidly determine if something is good or bad? How do we know our place within a social group? How do we know how to behave appropriately in dynamic environments with ever-changing conditions? The Tye Lab is interested in understanding how neural circuits important for driving positive and negative motivational valence (seeking pleasure or avoiding punishment) are anatomically, genetically and functionally arranged. We study the neural mechanisms that underlie a wide range of behaviours ranging from learned to innate, including social, feeding, reward-seeking and anxiety-related behaviours. We have also become interested in “social homeostasis” -- how our brains establish a preferred set-point for social contact, and how this maintains stability within a social group. How are these circuits interconnected with one another, and how are competing mechanisms orchestrated on a neural population level? We employ optogenetic, electrophysiological, electrochemical, pharmacological and imaging approaches to probe these circuits during behaviour.

The human brain, in particular the cerebral cortex, has undergone rapid expansion and increased complexity during recent evolution. One striking feature of human corticogenesis is that it is highly protracted in time, from prenatal stages of neurogenesis (taking months instead of days in the mouse), to postnatal stages of neuronal maturation and circuit formation (taking years instead of weeks in the mouse). This prolonged development is thought to contribute in an important fashion to increased cortical size, but also enhanced circuit complexity and plasticity. Here we will discuss how the species-specific temporal patterning of corticogenesis is largely intrinsic to cortical progenitors and neurons, and involves human-specific genes and cell properties that underlie human brain evolution, as well as our selective sensitivity to certain brain diseases.

Axons in the optic nerve are arranged in bundles and conducting action potential with resistance related to their membrane. Optic nerve axons do not form absolutely independent conductive channels. They are directly coupled by gap junctions formed in majority by neuronal Cx45. Coupling of axons, except known transpassing functions, allows to reduce axonal membrane resistance of optic nerve and accelerates transduction of visual signal. This novel finding have substantial implications for understanding of the pathogenesis of various optic neuropathies and identifies a new potential target for a therapeutic approach.

We live in the age of the network: Internet social neural ecosystems. This has become one of the main metaphors for how we think about complex systems. This view also dominates the account of brain function. The role of neuronsdescribed by Cajal as the "butterflies of the soul" has become diminished to leaky integrate-and-fire point objects in many models of neural network computation. It is perhaps not surprising that networkexplanations of neural phenomena use neurons as elementary particles andascribe all their wonderful capabilities to their interactions in a network. In the network view the Connectome defines the brain and the butterflies have no role. In this talk I'd like to reclaim some key computations from the networkand return them to their rightful place at the cellular and subcellular level. I'll start with a provocative look at potential computational capacity ofdifferent kinds of brain computation: network vs. subcellular. I'll then consider different levels of pattern and sequence computationwith a glimpse of the efficiency of the subcellular solutions. Finally I propose that there is a suggestive mapping between entire nodesof deep networks to individual neurons. This in my view is how we can walk around with 1.3 litres and 20 watts of installed computational capacity still doing far more than giant AI server farms.

Electrophysiology of eye and visual pathway is useful tool in ophthalmology and neurology. It covers a few examinations to find out if defect of vision is peripheral or central. Visual evoked potentials (VEP) are most frequently used in neurology and neuroophthalmology. VEP are evoked by flash or pattern stimulations. The combination of these both examinations gives more information about the visual pathway. It is very important to remember that VEP originate in the retina and reflect its function as well. In many cases not only VEP but also electroretinography (ERG) is essential for diagnosis. The seminar presents basic electrophysiological procedures used for diagnosis and follow-up of optic neuropathies and some of central nervous system diseases which affect vision (mostly multiple sclerosis, CNS tumors, stroke, traumas, intracranial hypertension).

Africa is blessed with a rich diversity and abundance in rodent and avian populations. This natural endowment on the continent portends research opportunities to study unique anatomical profiles and investigate animal models that may confer better neural architecture to study neurodegenerative diseases, adult neurogenesis, stroke and stem cell therapies. To this end, African researchers are beginning to pay closer attention to some of her indigenous rodents and birds in an attempt to develop spontaneous laboratory models for homegrown neuroscience-based research. For this presentation, I will be showing studies in our lab, involving cellular neuroanatomy of two rodents, the African giant rat (AGR) and Greater cane rat (GCR), Eidolon Bats (EB) and also the Striped Owl (SO). Using histological stains (Cresyl violet and Rapid Golgi) and immunohistochemical biomarkers (GFAP, NeuN, CNPase, Iba-1, Collagen 2, Doublecortin, Ki67, Calbindin, etc), and Electron Microscopy, morphology and functional organizations of neuronal and glial populations of the AGR , GCR, EB and SO brains have been described, with our work ongoing. In addition, the developmental profiles of the prenatal GCR brains have been chronicled across its entire gestational period. Brains of embryos/foetuses were harvested for gross morphological descriptions and then processed using immunofluorescence biomarkers to determine the pattern, onset, duration and peak of neurogenesis (Pax6, Tbr1, Tbr2, NF, HuCD, MAP2) and the onset and peak of glial cell expressions and myelination in the prenatal GCR. The outcome of these research efforts has shown unique neuroanatomical expressions and networks amongst Africa’s rich biodiversity. It is hopeful that continuous effort in this regard will provide sufficient basic research data on neural developments and cellular neuroanatomy with subsequent translational consequences.

Inhibitory microcircuits play an important role regulating cortical responses to sensory stimuli. Interneurons that inhibit dendritic or somatic integration are gatekeepers for neural activity, synaptic plasticity and the formation of sensory representations. We have been investigating the synaptic plasticity mechanisms underlying the formation of ensembles in olfactory and orbitofrontal cortex. We have been focusing on the roles of three inhibitory neuron classes in gating excitatory synaptic plasticity in olfactory cortex- somatostatin (SST-INs), parvalbumin (PV-INs), and vasoactive intestinal polypeptide (VIP-INs) interneurons. Further, we are investigating the rules for inhibitory plasticity and a potential role in stabilizing ensembles in associative cortices. I will present new findings to support distinct roles for different interneuron classes in the gating and stabilization of ensemble representations of olfactory responses.

In the last 500 million years, the dorsal telencephalon changed like no other region of the vertebrate brain. Differences range from the six-layered neocortex of mammals, to the small three-layered cortex of reptiles, and the complete absence of lamination in birds. These anatomical differences have prompted endless discussions on the origins and evolution of the cerebral cortex. We have approached this problem from a cell type and transcriptomics perspective. This reveals a more granular picture, where different cell types and classes have followed independent trajectories of evolutionary change. In this presentation, I will discuss how the molecular analysis of cell types in the brains of turtles, lizards and amphibians is updating our views on the evolution of the cerebral cortex, and the new questions emerging from these results.

Popular notions of how the retina encodes visual stimuli typically focus on the center-surround receptive fields of retinal ganglion cells, the output neurons of the retina. In this view, the receptive field acts as a linear filter on the visual stimulus, highlighting spatial contrast and providing efficient representations of natural images. Yet, we also know that many ganglion cells respond vigorously to fine spatial gratings that should not activate the linear filter of the receptive field. Thus, ganglion cells may integrate visual signals nonlinearly across space. In this talk, I will discuss how these (and other) nonlinearities relate to the encoding of natural visual stimuli in the retina. Based on electrophysiological recordings of ganglion and bipolar cells from mouse and salamander retina, I will present methods for assessing nonlinear processing in different cell types and examine their importance and potential function under natural stimulation.

How nervous systems control behaviour is the main question we seek to answer in neuroscience. Although visual systems have been a popular entry point into the brain, we don’t understand—in any deep sense—how visual perception guides navigation in flies (or any organism). I will present recent progress towards this goal from our lab. We are using anatomical insights from connectomics, genetic methods for labelling and manipulating identified cell types, neurophysiology, behaviour, and computational modeling to explain how the fly brain processes visual motion to regulate behaviour.

Many visual systems can process information in dynamically changing environments. In general, visual perception scales with changes in the visual stimulus, or contrast, irrespective of background illumination. This is achieved by adaptation. However, visual perception is challenged when adaptation is not fast enough to deal with sudden changes in overall illumination, for example when gaze follows a moving object from bright sunlight into a shaded area. We have recently shown that the visual system of the fly found a solution by propagating a corrective luminance-sensitive signal to higher processing stages. Using in vivo two-photon imaging and behavioural analyses we showed that distinct OFF-pathway inputs encode contrast and luminance. The luminance-sensitive pathway is particularly required when processing visual motion in contextual dim light, when pure contrast sensitivity underestimates the salience of a stimulus. Recent work in the lab has addressed the question how two visual pathways obtain such fundamentally different sensitivities, given common photoreceptor input. We are furthermore currently working out the network-based strategies by which luminance- and contrast-sensitive signals are combined to guide appropriate visual behaviour. Together, I will discuss the molecular, cellular, and circuit mechanisms that ensure contrast computation, and therefore robust vision, in fast changing visual scenes.

During conversations we rapidly switch between listening and speaking which often requires withholding or delaying our speech in order to hear others and avoid overlapping. This capacity for vocal turn-taking is exhibited by non-linguistic species as well, however the neural circuit mechanisms that enable us to regulate the precise timing of our vocalizations during interactions are unknown. We aim to identify the neural mechanisms underlying the coordination of vocal interactions. Therefore, we paired zebra finches with a vocal robot (1Hz call playback) and measured the bird’s call response times. We found that individual birds called with a stereotyped delay in respect to the robot call. Pharmacological inactivation of the premotor nucleus HVC revealed its necessity for the temporal coordination of calls. We further investigated the contributing neural activity within HVC by performing intracellular recordings from premotor neurons and inhibitory interneurons in calling zebra finches. We found that inhibition is preceding excitation before and during call onset. To test whether inhibition guides call timing we pharmacologically limited the impact of inhibition on premotor neurons. As a result zebra finches converged on a similar delay time i.e. birds called more rapidly after the vocal robot call suggesting that HVC inhibitory interneurons regulate the coordination of social contact calls. In addition, we aim to investigate the vocal turn-taking capabilities of the common nightingale. Male nightingales learn over 100 different song motifs which are being used in order to attract mates or defend territories. Previously, it has been shown that nightingales counter-sing with each other following a similar temporal structure to human vocal turn-taking. These animals are also able to spontaneously imitate a motif of another nightingale. The neural mechanisms underlying this behaviour are not yet understood. In my lab, we further probe the capabilities of these animals in order to access the dynamic range of their vocal turn taking flexibility.