Seminars

The retina is probably the most accessible part of the vertebrate central nervous system. Its computational logic can be interrogated in a dish, from patterns of lights as the natural input, to spike trains on the optic nerve as the natural output. Consequently, retinal circuits include some of the best understood computational networks in neuroscience. The retina is also ancient, and central to the emergence of neurally complex life on our planet. Alongside new locomotor strategies, the parallel evolution of image forming vision in vertebrate and invertebrate lineages is thought to have driven speciation during the Cambrian. This early investment in sophisticated vision is evident in the fossil record and from comparing the retina’s structural make up in extant species. Animals as diverse as eagles and lampreys share the same retinal make up of five classes of neurons, arranged into three nuclear layers flanking two synaptic layers. Some retina neuron types can be linked across the entire vertebrate tree of life. And yet, the functions that homologous neurons serve in different species, and the circuits that they innervate to do so, are often distinct to acknowledge the vast differences in species-specific visuo-behavioural demands. In the lab, we aim to leverage the vertebrate retina as a discovery platform for understanding the evolution of computation in the nervous system. Working on zebrafish alongside birds, frogs and sharks, we ask: How do synapses, neurons and networks enable ‘function’, and how can they rearrange to meet new sensory and behavioural demands on evolutionary timescales?

While non-invasive Brain-to-Computer interface can accurately classify the lateralization of hand moments, the distinction of fingers activation in the same hand is limited by their local and overlapping representation in the motor cortex. In particular, the low signal-to-noise ratio restrains the opportunity to identify meaningful patterns in a supervised fashion. Here we combined Magnetoencephalography (MEG) recordings with advanced decoding strategy to classify finger movements at single trial level. We recorded eight subjects performing a serial reaction time task, where they pressed four buttons with left and right index and middle fingers. We evaluated the classification performance of hand and finger movements with increasingly complex approaches: supervised common spatial patterns and logistic regression (CSP + LR) and unsupervised linear finite convolutional neural network (LF-CNN). The right vs left fingers classification performance was accurate above 90% for all methods. However, the classification of the single finger provided the following accuracy: CSP+SVM : – 68 ± 7%, LF-CNN : 71 ± 10%. CNN methods allowed the inspection of spatial and spectral patterns, which reflected activity in the motor cortex in the theta and alpha ranges. Thus, we have shown that the use of CNN in decoding MEG single trials with low signal to noise ratio is a promising approach that, in turn, could be extended to a manifold of problems in clinical and cognitive neuroscience.

Individuals vary widely in how they categorize novel phenomena. This individual variation has led canonical theories in cognitive and social science to suggest that communication in large social networks leads populations to construct divergent category systems. Yet, anthropological data indicates that large, independent societies consistently arrive at similar categories across a range of topics. How is it possible for diverse populations, consisting of individuals with significant variation in how they view the world, to independently construct similar categories? Through a series of online experiments, I show how large communication networks within cultures can promote the formation of similar categories across cultures. For this investigation, I designed an online “Grouping Game” to observe how people construct categories in both small and large populations when tasked with grouping together the same novel and ambiguous images. I replicated this design for English-speaking subjects in the U.S. and Mandarin-speaking subjects in China. In both cultures, solitary individuals and small social groups produced highly divergent category systems. Yet, large social groups separately and consistently arrived at highly similar categories both within and across cultures. These findings are accurately predicted by a simple mathematical model of critical mass dynamics. Altogether, I show how large communication networks can filter lexical diversity among individuals to produce replicable society-level patterns, yielding unexpected implications for cultural evolution. In particular, I discuss how participants in both cultures readily harnessed analogies when categorizing novel stimuli, and I examine the role of communication networks in promoting cross-cultural similarities in analogy-making as the key engine of category formation.

Learning does not occur in isolation. From parent-child interactions to formal classroom environments, humans explore, learn, and communicate in rich, diverse social contexts. Rather than simply observing and copying their conspecifics, humans engage in a range of epistemic practices that actively recruit those around them. What makes human social learning so distinctive, powerful, and smart? In this talk, I will present a series of studies that reveal the remarkably sophisticated inferential abilities that young children show not only in how they learn from others but also in how they help others learn. Children interact with others as learners and as teachers to learn and communicate about the world, about others, and even about the self. The results collectively paint a picture of human social learning that is far more than copying and imitation: It is active, bidirectional, and cooperative. I will end by discussing ongoing work that extends this picture beyond what we typically call “social learning”, with implications for building better machines that learn from and interact with humans.

The complex morphology of neurons, with synapses located hundreds of microns from the cell body, necessitates the localization of important cell biological machines, including ribosomes, within dendrites and axons. Local translation of mRNAs is important for the function and plasticity of synapses. Using advanced sequencing and imaging techniques we have updated our understanding of the local transcriptome and identified the local translatome- identifying over 800 transcripts for which local translation is the dominant source of protein. In addition, we have explored the unique mechanisms neurons use to meet protein demands at synapses, identifying surprising features of neuronal and synaptic protein synthesis.

Many engineered and biological systems must trade off performance and energy use, and the brain is no exception. While there are theories on how activity levels are controlled in biological networks through feedback control (homeostasis), it is not clear what the effects on population coding are, and therefore how performance and energy can be traded off. In this talk we will consider this tradeoff in auto-encoding networks, in which there is a clear definition of performance (the coding loss). We first show how SNNs follow a characteristic trade-off curve between activity levels and coding loss, but that standard networks need to be retrained to achieve different tradeoff points. We next formalize this tradeoff with a joint loss function incorporating coding loss (performance) and activity loss (energy use). From this loss we derive a class of spiking networks which coordinates its spiking to minimize both the activity and coding losses -- and as a result can dynamically adjust its coding precision and energy use. The network utilizes several known activity control mechanisms for this --- threshold adaptation and feedback inhibition --- and elucidates their potential function within neural circuits. Using geometric intuition, we demonstrate how these mechanisms regulate coding precision, and thereby performance. Lastly, we consider how these insights could be transferred to trained SNNs. Overall, this work addresses a key energy-coding trade-off which is often overlooked in network studies, expands on our understanding of homeostasis in biological SNNs, as well as provides a clear framework for considering performance and energy use in artificial SNNs.

Fly Escape Behaviors: Flexible and Modular We have identified a set of escape maneuvers performed by a fly when confronted by a looming object. These escape responses can be divided into distinct behavioral modules. Some of the modules are very stereotyped, as when the fly rapidly extends its middle legs to jump off the ground. Other modules are more complex and require the fly to combine information about both the location of the threat and its own body posture. In response to an approaching object, a fly chooses some varying subset of these behaviors to perform. We would like to understand the neural process by which a fly chooses when to perform a given escape behavior. Beyond an appealing set of behaviors, this system has two other distinct advantages for probing neural circuitry. First, the fly will perform escape behaviors even when tethered such that its head is fixed and neural activity can be imaged or monitored using electrophysiology. Second, using Drosophila as an experimental animal makes available a rich suite of genetic tools to activate, silence, or image small numbers of cells potentially involved in the behaviors. Neural Circuits for Escape Until recently, visually induced escape responses have been considered a hardwired reflex in Drosophila. White-eyed flies with deficient visual pigment will perform a stereotyped middle-leg jump in response to a light-off stimulus, and this reflexive response is known to be coordinated by the well-studied giant fiber (GF) pathway. The GFs are a pair of electrically connected, large-diameter interneurons that traverse the cervical connective. A single GF spike results in a stereotyped pattern of muscle potentials on both sides of the body that extends the fly's middle pair of legs and starts the flight motor. Recently, we have found that a fly escaping a looming object displays many more behaviors than just leg extension. Most of these behaviors could not possibly be coordinated by the known anatomy of the GF pathway. Response to a looming threat thus appears to involve activation of numerous different neural pathways, which the fly may decide if and when to employ. Our goal is to identify the descending pathways involved in coordinating these escape behaviors as well as the central brain circuits, if any, that govern their activation. Automated Single-Fly Screening We have developed a new kind of high-throughput genetic screen to automatically capture fly escape sequences and quantify individual behaviors. We use this system to perform a high-throughput genetic silencing screen to identify cell types of interest. Automation permits analysis at the level of individual fly movements, while retaining the capacity to screen through thousands of GAL4 promoter lines. Single-fly behavioral analysis is essential to detect more subtle changes in behavior during the silencing screen, and thus to identify more specific components of the contributing circuits than previously possible when screening populations of flies. Our goal is to identify candidate neurons involved in coordination and choice of escape behaviors. Measuring Neural Activity During Behavior We use whole-cell patch-clamp electrophysiology to determine the functional roles of any identified candidate neurons. Flies perform escape behaviors even when their head and thorax are immobilized for physiological recording. This allows us to link a neuron's responses directly to an action.

The ability to discriminate different sensory patterns by disentangling their neural representations is an important property of neural networks. While a variety of learning rules are known to be highly effective at fine-tuning synapses to achieve this, less is known about how different cell types in the brain can facilitate this process by providing architectural priors that bias the network towards sparse, selective, and discriminable representations. We studied this by simulating a neuronal network modelled on the dentate gyrus—an area characterised by sparse activity associated with pattern separation in spatial memory tasks. To test the contribution of different cell types to these functions, we presented the model with a wide dynamic range of input patterns and systematically added or removed different circuit elements. We found that recruiting feedback inhibition indirectly via recurrent excitatory neurons proved particularly helpful in disentangling patterns, and show that simple alignment principles for excitatory and inhibitory connections are a highly effective strategy.

Climbing fiber inputs to Purkinje cells provide instructive signals critical for cerebellum-dependent associative learning. Studying these signals in head-fixed mice facilitates the use of imaging, electrophysiological, and optogenetic methods. Here, a low-cost behavioral platform (~$1000) was developed that allows tracking of associative learning in head-fixed mice that locomote freely on a running wheel. The platform incorporates two common associative learning paradigms: eyeblink conditioning and delayed tactile startle conditioning. Behavior is tracked using a camera and the wheel movement by a detector. We describe the components and setup and provide a detailed protocol for training and data analysis. This platform allows the incorporation of optogenetic stimulation and fluorescence imaging. The design allows a single host computer to control multiple platforms for training multiple animals simultaneously.

Reservoir computing is a promising framework to study cortical computation, as it is based on continuous, online processing and the requirements and operating principles are compatible with cortical circuit dynamics. However, the framework has issues that limit its scope as a generic model for cortical processing. The most obvious of these is that, in traditional models, learning is restricted to the output projections and takes place in a fully supervised manner. If such an output layer is interpreted at face value as downstream computation, this is biologically questionable. If it is interpreted merely as a demonstration that the network can accurately represent the information, this immediately raises the question of what would be biologically plausible mechanisms for transmitting the information represented by a reservoir and incorporating it in downstream computations. Another major issue is that we have as yet only modest insight into how the structural and dynamical features of a network influence its computational capacity, which is necessary not only for gaining an understanding of those features in biological brains, but also for exploiting reservoir computing as a neuromorphic application. In this talk, I will first demonstrate a method for quantifying the representational capacity of reservoirs without training them on tasks. Based on this technique, which allows systematic comparison of systems, I then present our recent work towards understanding the roles of heterogeneity and connectivity patterns in enhancing both the computational properties of a network and its ability to reliably transmit to downstream networks. Finally, I will give a brief taster of our current efforts to apply the reservoir computing framework to magnetic systems as an approach to neuromorphic computing.

Due to high homology to the human genome and rapid development, zebrafish have been successfully used to model diseases of the neuromuscular system. In this seminar, I will present current advances in modeling genetic causes of Amyotrophic Lateral Sclerosis (ALS), the most common motor neuron degeneration and show how epistatic interaction studies in zebrafish have helped elucidate synergistic effects of major ALS genes and their cellular targets.

Brain aging leads to cognitive decline and is the main risk factor for sporadic forms of neurodegenerative diseases including Alzheimer’s disease. While brain cell- and tissue-intrinsic factors are likely key determinants of the aging process recent studies document a remarkable susceptibility of the brain to circulatory factors. Thus, blood borne factors from young mice or humans are sufficient to slow aspects of brain aging and improve cognitive function in old mice and, vice versa, factors from old mice are detrimental for young mice and impair cognition. We found evidence that the cerebrovasculature is an important target of circulatory factors and that brain endothelial cells show prominent age-related transcriptional changes in response to plasma. Furthermore, plasma proteins are taken up broadly into the young brain through receptor mediated transport which declines with aging. At the same time, brain derived proteins are detectable in plasma allowing us to measure physiological changes linked to brain aging in plasma. We are exploring the relevance of these findings for neurodegeneration and potential applications towards therapies.

Connections between nerve cells called synapses are the fundamental units of communication and information processing in the brain. The accurate wiring of neurons through synapses into neural networks or circuits is essential for brain organization. Neuronal networks are sculpted and refined throughout life by constant adjustment of the strength of synaptic communication by neuronal activity, a process known as synaptic plasticity. Deficits in the development or plasticity of synapses underlie various neuropsychiatric disorders, including autism, schizophrenia and intellectual disability. The Siddiqui lab research program comprises three major themes. One, to assess how biochemical switches control the activity of synapse organizing proteins, how these switches act through their binding partners and how these processes are regulated to correct impaired synaptic function in disease. Two, to investigate how synapse organizers regulate the specificity of neuronal circuit development and how defined circuits contribute to cognition and behaviour. Three, to address how synapses are formed in the developing brain and maintained in the mature brain and how microcircuits formed by synapses are refined to fine-tune information processing in the brain. Together, these studies have generated fundamental new knowledge about neuronal circuit development and plasticity and enabled us to identify targets for therapeutic intervention.

In this talk I will introduce the central notions of the theory of autonomy, as it is being currently developed in biology and cognitive science. The theory of autonomy puts forward the capacity of self-determination of organisms as whole systems, and constitutes thereby an alternative to more reductionist and mechanistic approaches. I will discuss how the theory of autonomy provides a justification for the scientific use of notions as function, norm, agency and teleology, whose epistemological legitimacy is highly debated. I will conclude by describing the difficult challenges that poses the transition from biological to cognitive autonomy.

We hardly notice when there is a speck on our glasses, the obstructed visual information seems to be magically filled in. The visual system uses visual context to predict the content of the stimulus. What enables neurons in the visual system to respond to context when the stimulus is not available? In cortex, sensory processing is based on a combination of feedforward information arriving from sensory organs, and feedback information that originates in higher-order areas. Whereas feedforward information drives the activity in cortex, feedback information is thought to provide contextual signals that are merely modulatory. We have made the exciting discovery that mouse primary visual cortical neurons are strongly driven by feedback projections from higher visual areas, in particular when their feedforward sensory input from the retina is missing. This drive is so strong that it makes visual cortical neurons fire as much as if they were receiving a direct sensory input.

Scientific studies of consciousness can be roughly categorized into two directions: (1) How/where does consciousness emerge? (the mechanism of consciousness) and (2) Why is there consciousness? (the function of consciousness). I will summarize my past research on the quest for consciousness in these two directions.

In the Learning Salon, we will discuss the similarities and differences between biological and machine learning, including individuals with diverse perspectives and backgrounds, so we can all learn from one another.