Integrative Structural Biology: Bridging Fields, Techniques, and Scales
This Keystone Symposium brings structural and cellular biologists together to integrate cryo-electron microscopy, super-resolution and multiscale imaging, data science, computational modeling and deep learning. The programme connects molecular structure to tissue-level function and includes in-person, livestream and on-demand participation.
Dynamic Expectations
Reward expectations – one’s prediction about the likelihood of future outcomes - play a central role in shaping the satisfaction derived from those outcomes. Most existing research treats expectations as static, assuming they remain fixed in time. However, real-life expectations are often dynamic, fluctuating as new information becomes available. For example, during a soccer game, your expectations of seeing your team winning will likely rise and fall as the game unfolds. In the main part of this talk, I will present a series of studies demonstrating that human expectations can be tracked at sub-second timescales. Using slot machines as a case study, we leverage the continuous deceleration of the reels to elicit moment-by-moment fluctuations in rewardexpectations. To capture these dynamics, we take complementary approaches: we use the high temporal resolution of electroencephalography (EEG) to track neural signatures of evolving expectations, and we develop a novel behavioral paradigm (“Slot or Not”) designed to measure changes in expectations via betting behavior. Across four studies, we show that expectations fluctuate continuously and can be tracked both behaviorally and neurally. Extending these findings, a subsequent intracranial study shows that the human orbitofrontal cortex (OFC) encodes the moment-by-moment changes of reward expectations. In the second part of this talk, I will return to the relationship between expectations andsatisfaction. If expectations shape satisfaction, and if they are best conceptualized as dynamic trajectories rather than static quantities, a key question arises: does the trajectory leading up to an outcome influence how that outcome is evaluated? I will outline a new research direction aimed at formalizing this relationship using computational modeling. This is ongoing work, and I welcome feedback on how best to formalize these ideas. Finally, I will discuss potential extensions of this framework to psychopathology, asking whether alterations in dynamic expectations may characterize conditions such as Major Depressive Disorder and Gambling disorder. Together, this work introduces a new framework for studying expectations as dynamic processes, offering a richer understanding Presented in the van Vreeswijk Theoretical Neuroscience Seminar series (formerly WWTNS) on 2026-04-29. Recording duration: 00:42:25.
Neurobiological constraints on learning: bug or feature?
Understanding how brains learn requires bridging evidence across scales—from behaviour and neural circuits to cells, synapses, and molecules. In our work, we use computational modelling and data analysis to explore how the physical properties of neurons and neural circuits constrain learning. These include limits imposed by brain wiring, energy availability, molecular noise, and the 3D structure of dendritic spines. In this talk I will describe one such project testing if wiring motifs from fly brain connectomes can improve performance of reservoir computers, a type of recurrent neural network. The hope is that these insights into brain learning will lead to improved learning algorithms for artificial systems.
Hidden nature of seizures
How seizures emerge from the abnormal dynamics of neural networks within the epileptogenic tissue remains an enigma. Are seizures random events, or do detectable changes in brain dynamics precede them? Are mechanisms of seizure emergence identical at the onset and later stages of epilepsy? Is the risk of seizure occurrence stable, or does it change over time? A myriad of questions about seizure genesis remains to be answered to understand the core principles governing seizure genesis. The last decade has brought unprecedented insights into the complex nature of seizure emergence. It is now believed that seizure onset represents the product of the interactions between the process of a transition to seizure, long-term fluctuations in seizure susceptibility, epileptogenesis, and disease progression. During the lecture, we will review the latest observations about mechanisms of ictogenesis operating at multiple temporal scales. We will show how the latest observations contribute to the formation of a comprehensive theory of seizure genesis, and challenge the traditional perspectives on ictogenesis. Finally, we will discuss how combining conventional approaches with computational modeling, modern techniques of in vivo imaging, and genetic manipulation open prospects for exploration of yet hidden mechanisms of seizure genesis.
Building System Models of Brain-Like Visual Intelligence with Brain-Score
Research in the brain and cognitive sciences attempts to uncover the neural mechanisms underlying intelligent behavior in domains such as vision. Due to the complexities of brain processing, studies necessarily had to start with a narrow scope of experimental investigation and computational modeling. I argue that it is time for our field to take the next step: build system models that capture a range of visual intelligence behaviors along with the underlying neural mechanisms. To make progress on system models, we propose integrative benchmarking – integrating experimental results from many laboratories into suites of benchmarks that guide and constrain those models at multiple stages and scales. We show-case this approach by developing Brain-Score benchmark suites for neural (spike rates) and behavioral experiments in the primate visual ventral stream. By systematically evaluating a wide variety of model candidates, we not only identify models beginning to match a range of brain data (~50% explained variance), but also discover that models’ brain scores are predicted by their object categorization performance (up to 70% ImageNet accuracy). Using the integrative benchmarks, we develop improved state-of-the-art system models that more closely match shallow recurrent neuroanatomy and early visual processing to predict primate temporal processing and become more robust, and require fewer supervised synaptic updates. Taken together, these integrative benchmarks and system models are first steps to modeling the complexities of brain processing in an entire domain of intelligence.
Internally Organized Abstract Task Maps in the Mouse Medial Frontal Cortex
New tasks are often similar in structure to old ones. Animals that take advantage of such conserved or “abstract” task structures can master new tasks with minimal training. To understand the neural basis of this abstraction, we developed a novel behavioural paradigm for mice: the “ABCD” task, and recorded from their medial frontal neurons as they learned. Animals learned multiple tasks where they had to visit 4 rewarded locations on a spatial maze in sequence, which defined a sequence of four “task states” (ABCD). Tasks shared the same circular transition structure (… ABCDABCD …) but differed in the spatial arrangement of rewards. As well as improving across tasks, mice inferred that A followed D (i.e. completed the loop) on the very first trial of a new task. This “zero-shot inference” is only possible if animals had learned the abstract structure of the task. Across tasks, individual medial Frontal Cortex (mFC) neurons maintained their tuning to the phase of an animal’s trajectory between rewards but not their tuning to task states, even in the absence of spatial tuning. Intriguingly, groups of mFC neurons formed modules of coherently remapping neurons that maintained their tuning relationships across tasks. Such tuning relationships were expressed as replay/preplay during sleep, consistent with an internal organisation of activity into multiple, task-matched ring attractors. Remarkably, these modules were anchored to spatial locations: neurons were tuned to specific task space “distances” from a particular spatial location. These newly discovered “Spatially Anchored Task clocks” (SATs), suggest a novel algorithm for solving abstraction tasks. Using computational modelling, we show that SATs can perform zero-shot inference on new tasks in the absence of plasticity and guide optimal policy in the absence of continual planning. These findings provide novel insights into the Frontal mechanisms mediating abstraction and flexible behaviour.
Successes and failures of current AI as a model of visual cognition
From Computation to Large-scale Neural Circuitry in Human Belief Updating
Many decisions under uncertainty entail dynamic belief updating: multiple pieces of evidence informing about the state of the environment are accumulated across time to infer the environmental state, and choose a corresponding action. Traditionally, this process has been conceptualized as a linear and perfect (i.e., without loss) integration of sensory information along purely feedforward sensory-motor pathways. Yet, natural environments can undergo hidden changes in their state, which requires a non-linear accumulation of decision evidence that strikes a tradeoff between stability and flexibility in response to change. How this adaptive computation is implemented in the brain has remained unknown. In this talk, I will present an approach that my laboratory has developed to identify evidence accumulation signatures in human behavior and neural population activity (measured with magnetoencephalography, MEG), across a large number of cortical areas. Applying this approach to data recorded during visual evidence accumulation tasks with change-points, we find that behavior and neural activity in frontal and parietal regions involved in motor planning exhibit hallmarks signatures of adaptive evidence accumulation. The same signatures of adaptive behavior and neural activity emerge naturally from simulations of a biophysically detailed model of a recurrent cortical microcircuit. The MEG data further show that decision dynamics in parietal and frontal cortex are mirrored by a selective modulation of the state of early visual cortex. This state modulation is (i) specifically expressed in the alpha frequency-band, (ii) consistent with feedback of evolving belief states from frontal cortex, (iii) dependent on the environmental volatility, and (iv) amplified by pupil-linked arousal responses during evidence accumulation. Together, our findings link normative decision computations to recurrent cortical circuit dynamics and highlight the adaptive nature of decision-related long-range feedback processing in the brain.
Feedforward and feedback processes in visual recognition
Progress in deep learning has spawned great successes in many engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching – and sometimes even surpassing – human accuracy on a variety of visual recognition tasks. In this talk, however, I will show that these neural networks and their recent extensions exhibit a limited ability to solve seemingly simple visual reasoning problems involving incremental grouping, similarity, and spatial relation judgments. Our group has developed a recurrent network model of classical and extra-classical receptive field circuits that is constrained by the anatomy and physiology of the visual cortex. The model was shown to account for diverse visual illusions providing computational evidence for a novel canonical circuit that is shared across visual modalities. I will show that this computational neuroscience model can be turned into a modern end-to-end trainable deep recurrent network architecture that addresses some of the shortcomings exhibited by state-of-the-art feedforward networks for solving complex visual reasoning tasks. This suggests that neuroscience may contribute powerful new ideas and approaches to computer science and artificial intelligence.
How neural circuits organize and learn during development
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.
Can I be bothered? Neural and computational mechanisms underlying the dynamics of effort processing (BACN Early-career Prize Lecture 2021)
From a workout at the gym to helping a colleague with their work, everyday we make decisions about whether we are willing to exert effort to obtain some sort of benefit. Increases in how effortful actions and cognitive processes are perceived to be has been linked to clinically severe impairments to motivation, such as apathy and fatigue, across many neurological and psychiatric conditions. However, the vast majority of neuroscience research has focused on understanding the benefits for acting, the rewards, and not on the effort required. As a result, the computational and neural mechanisms underlying how effort is processed are poorly understood. How do we compute how effortful we perceive a task to be? How does this feed into our motivation and decisions of whether to act? How are such computations implemented in the brain? and how do they change in different environments? I will present a series of studies examining these questions using novel behavioural tasks, computational modelling, fMRI, pharmacological manipulations, and testing in a range of different populations. These studies highlight how the brain represents the costs of exerting effort, and the dynamic processes underlying how our sensitivity to effort changes as a function of our goals, traits, and socio-cognitive processes. This work provides new computational frameworks for understanding and examining impaired motivation across psychiatric and neurological conditions, as well as why all of us, sometimes, can’t be bothered.
Computational modelling of neurotransmitter release
Synaptic transmission provides the basis for neuronal communication. When an action-potential propagates through the axonal arbour, it activates voltage-gated Ca2+ channels located in the vicinity of release-ready synaptic vesicles docked at the presynaptic active zone. Ca2+ ions enter the presynaptic terminal and activate the vesicular Ca2+ sensor, thereby triggering neurotransmitter release. This whole process occurs on a timescale of a few milliseconds. In addition to fast, synchronous release, which keeps pace with action potentials, many synapses also exhibit delayed asynchronous release that persists for tens to hundreds of milliseconds. In this talk I will demonstrate how experimentally constrained computational modelling of underlying biological processes can complement laboratory studies (using electrophysiology and imaging techniques) and provide insights into the mechanisms of synaptic transmission.
How do protein-RNA condensates form and contribute to disease?
In recent years, it has become clear that intrinsically disordered regions (IDRs) of RBPs, and the structure of RNAs, often contribute to the condensation of RNPs. To understand the transcriptomic features of such RNP condensates, we’ve used an improved individual nucleotide resolution CLIP protocol (iiCLIP), which produces highly sensitive and specific data, and thus enables quantitative comparisons of interactions across conditions (Lee et al., 2021). This showed how the IDR-dependent condensation properties of TDP-43 specify its RNA binding and regulatory repertoire (Hallegger et al., 2021). Moreover, we developed software for discovery and visualisation of RNA binding motifs that uncovered common binding patterns of RBPs on long multivalent RNA regions that are composed of dispersed motif clusters (Kuret et al, 2021). Finally, we used hybrid iCLIP (hiCLIP) to characterise the RNA structures mediating the assembly of Staufen RNPs across mammalian brain development, which demonstrated the roles of long-range RNA duplexes in the compaction of long 3’UTRs. I will present how the combined analysis of the characteristics of IDRs in RBPs, multivalent RNA regions and RNA structures is required to understand the formation and functions of RNP condensates, and how they change in diseases.
The Problem of Testimony
The talk will detail work drawing on behavioural results, formal analysis, and computational modelling with agent-based simulations to unpack the scale of the challenge humans face when trying to work out and factor in the reliability of their sources. In particular, it is shown how and why this task admits of no easy solution in the context of wider communication networks, and how this will affect the accuracy of our beliefs. The implications of this for the shift in the size and topology of our communication networks through the uncontrolled rise of social media are discussed.
Functional Divergence at the Mouse Bipolar Cell Terminal
Research in our lab focuses on the circuit mechanisms underlying sensory computation. We use the mouse retina as a model system because it allows us to stimulate the circuit precisely with its natural input, patterns of light, and record its natural output, the spike trains of retinal ganglion cells. We harness the power of genetic manipulations and detailed information about cell types to uncover new circuits and discover their role in visual processing. Our methods include electrophysiology, computational modeling, and circuit tracing using a variety of imaging techniques.
Deception, ExoNETs, SmushWare & Organic Data: Tech-facilitated neurorehabilitation & human-machine training
Making use of visual display technology and human-robotic interfaces, many researchers have illustrated various opportunities to distort visual and physical realities. We have had success with interventions such as error augmentation, sensory crossover, and negative viscosity. Judicial application of these techniques leads to training situations that enhance the learning process and can restore movement ability after neural injury. I will trace out clinical studies that have employed such technologies to improve the health and function, as well as share some leading-edge insights that include deceiving the patient, moving the "smarts" of software into the hardware, and examining clinical effectiveness
Hearing in an acoustically varied world
In order for animals to thrive in their complex environments, their sensory systems must form representations of objects that are invariant to changes in some dimensions of their physical cues. For example, we can recognize a friend’s speech in a forest, a small office, and a cathedral, even though the sound reaching our ears will be very different in these three environments. I will discuss our recent experiments into how neurons in auditory cortex can form stable representations of sounds in this acoustically varied world. We began by using a normative computational model of hearing to examine how the brain may recognize a sound source across rooms with different levels of reverberation. The model predicted that reverberations can be removed from the original sound by delaying the inhibitory component of spectrotemporal receptive fields in the presence of stronger reverberation. Our electrophysiological recordings then confirmed that neurons in ferret auditory cortex apply this algorithm to adapt to different room sizes. Our results demonstrate that this neural process is dynamic and adaptive. These studies provide new insights into how we can recognize auditory objects even in highly reverberant environments, and direct further research questions about how reverb adaptation is implemented in the cortical circuit.
The self-consistent nature of visual perception
Vision provides us with a holistic interpretation of the world that is, with very few exceptions, coherent and consistent across multiple levels of abstraction, from scene to objects to features. In this talk I will present results from past and ongoing work in my laboratory that investigates the role top-down signals play in establishing such coherent perceptual experience. Based on the results of several psychophysical experiments I will introduce a theory of “self-consistent inference” and show how it can account for human perceptual behavior. The talk will close with a discussion of how the theory can help us understand more cognitive processes.
NMC4 Short Talk: Two-Photon Imaging of Norepinephrine in the Prefrontal Cortex Shows that Norepinephrine Structures Cell Firing Through Local Release
Norepinephrine (NE) is a neuromodulator that is released from projections of the locus coeruleus via extra-synaptic vesicle exocytosis. Tonic fluctuations in NE are involved in brain states, such as sleep, arousal, and attention. Previously, NE in the PFC was thought to be a homogenous field created by bulk release, but it remains unknown whether phasic (fast, short-term) fluctuations in NE can produce a spatially heterogeneous field, which could then structure cell firing at a fine spatial scale. To understand how spatiotemporal dynamics of norepinephrine (NE) release in the prefrontal cortex affect neuronal firing, we performed a novel in-vivo two-photon imaging experiment in layer ⅔ of the prefrontal cortex using a green fluorescent NE sensor and a red fluorescent Ca2+ sensor, which allowed us to simultaneously observe fine-scale neuronal and NE dynamics in the form of spatially localized fluorescence time series. Using generalized linear modeling, we found that the local NE field differs from the global NE field in transient periods of decorrelation, which are influenced by proximal NE release events. We used optical flow and pattern analysis to show that release and reuptake events can occur at the same location but at different times, and differential recruitment of release and reuptake sites over time is a potential mechanism for creating a heterogeneous NE field. Our generalized linear models predicting cellular dynamics show that the heterogeneous local NE field, and not the global field, drives cell firing dynamics. These results point to the importance of local, small-scale, phasic NE fluctuations for structuring cell firing. Prior research suggests that these phasic NE fluctuations in the PFC may play a role in attentional shifts, orienting to sensory stimuli in the environment, and in the selective gain of priority representations during stress (Mather, Clewett et al. 2016) (Aston-Jones and Bloom 1981).
NMC4 Short Talk: Multiscale and extended retrieval of associative memory structures in a cortical model of local-global inhibition balance
Inhibitory neurons take on many forms and functions. How this diversity contributes to memory function is not completely known. Previous formal studies indicate inhibition differentiated by local and global connectivity in associative memory networks functions to rescale the level of retrieval of excitatory assemblies. However, such studies lack biological details such as a distinction between types of neurons (excitatory and inhibitory), unrealistic connection schemas, and non-sparse assemblies. In this study, we present a rate-based cortical model where neurons are distinguished (as excitatory, local inhibitory, or global inhibitory), connected more realistically, and where memory items correspond to sparse excitatory assemblies. We use this model to study how local-global inhibition balance can alter memory retrieval in associative memory structures, including naturalistic and artificial structures. Experimental studies have reported inhibitory neurons and their sub-types uniquely respond to specific stimuli and can form sophisticated, joint excitatory-inhibitory assemblies. Our model suggests such joint assemblies, as well as a distribution and rebalancing of overall inhibition between two inhibitory sub-populations – one connected to excitatory assemblies locally and the other connected globally – can quadruple the range of retrieval across related memories. We identify a possible functional role for local-global inhibitory balance to, in the context of choice or preference of relationships, permit and maintain a broader range of memory items when local inhibition is dominant and conversely consolidate and strengthen a smaller range of memory items when global inhibition is dominant. This model therefore highlights a biologically-plausible and behaviourally-useful function of inhibitory diversity in memory.
NMC4 Short Talk: Predictive coding is a consequence of energy efficiency in recurrent neural networks
Predictive coding represents a promising framework for understanding brain function, postulating that the brain continuously inhibits predictable sensory input, ensuring a preferential processing of surprising elements. A central aspect of this view on cortical computation is its hierarchical connectivity, involving recurrent message passing between excitatory bottom-up signals and inhibitory top-down feedback. Here we use computational modelling to demonstrate that such architectural hard-wiring is not necessary. Rather, predictive coding is shown to emerge as a consequence of energy efficiency, a fundamental requirement of neural processing. When training recurrent neural networks to minimise their energy consumption while operating in predictive environments, the networks self-organise into prediction and error units with appropriate inhibitory and excitatory interconnections and learn to inhibit predictable sensory input. We demonstrate that prediction units can reliably be identified through biases in their median preactivation, pointing towards a fundamental property of prediction units in the predictive coding framework. Moving beyond the view of purely top-down driven predictions, we demonstrate via virtual lesioning experiments that networks perform predictions on two timescales: fast lateral predictions among sensory units and slower prediction cycles that integrate evidence over time. Our results, which replicate across two separate data sets, suggest that predictive coding can be interpreted as a natural consequence of energy efficiency. More generally, they raise the question which other computational principles of brain function can be understood as a result of physical constraints posed by the brain, opening up a new area of bio-inspired, machine learning-powered neuroscience research.
NMC4 Short Talk: A theory for the population rate of adapting neurons disambiguates mean vs. variance-driven dynamics and explains log-normal response statistics
Recently, the field of computational neuroscience has seen an explosion of the use of trained recurrent network models (RNNs) to model patterns of neural activity. These RNN models are typically characterized by tuned recurrent interactions between rate 'units' whose dynamics are governed by smooth, continuous differential equations. However, the response of biological single neurons is better described by all-or-none events - spikes - that are triggered in response to the processing of their synaptic input by the complex dynamics of their membrane. One line of research has attempted to resolve this discrepancy by linking the average firing probability of a population of simplified spiking neuron models to rate dynamics similar to those used for RNN units. However, challenges remain to account for complex temporal dependencies in the biological single neuron response and for the heterogeneity of synaptic input across the population. Here, we make progress by showing how to derive dynamic rate equations for a population of spiking neurons with multi-timescale adaptation properties - as this was shown to accurately model the response of biological neurons - while they receive independent time-varying inputs, leading to plausible asynchronous activity in the network. The resulting rate equations yield an insightful segregation of the population's response into dynamics that are driven by the mean signal received by the neural population, and dynamics driven by the variance of the input across neurons, with respective timescales that are in agreement with slice experiments. Further, these equations explain how input variability can shape log-normal instantaneous rate distributions across neurons, as observed in vivo. Our results help interpret properties of the neural population response and open the way to investigating whether the more biologically plausible and dynamically complex rate model we derive could provide useful inductive biases if used in an RNN to solve specific tasks.
NMC4 Short Talk: Different hypotheses on the role of the PFC in solving simple cognitive tasks
Low-dimensional population dynamics can be observed in neural activity recorded from the prefrontal cortex (PFC) of subjects performing simple cognitive tasks. Many studies have shown that recurrent neural networks (RNNs) trained on the same tasks can reproduce qualitatively these state space trajectories, and have used them as models of how neuronal dynamics implement task computations. The PFC is also viewed as a conductor that organizes the communication between cortical areas and provides contextual information. It is then not clear what is its role in solving simple cognitive tasks. Do the low-dimensional trajectories observed in the PFC really correspond to the computations that it performs? Or do they indirectly reflect the computations occurring within the cortical areas projecting to the PFC? To address these questions, we modelled cortical areas with a modular RNN and equipped it with a PFC-like cognitive system. When trained on cognitive tasks, this multi-system brain model can reproduce the low-dimensional population responses observed in neuronal activity as well as classical RNNs. Qualitatively different mechanisms can emerge from the training process when varying some details of the architecture such as the time constants. In particular, there is one class of models where it is the dynamics of the cognitive system that is implementing the task computations, and another where the cognitive system is only necessary to provide contextual information about the task rule as task performance is not impaired when preventing the system from accessing the task inputs. These constitute two different hypotheses about the causal role of the PFC in solving simple cognitive tasks, which could motivate further experiments on the brain.
NMC4 Short Talk: Systematic exploration of neuron type differences in standard plasticity protocols employing a novel pathway based plasticity rule
Spike Timing Dependent Plasticity (STDP) is argued to modulate synaptic strength depending on the timing of pre- and postsynaptic spikes. Physiological experiments identified a variety of temporal kernels: Hebbian, anti-Hebbian and symmetrical LTP/LTD. In this work we present a novel plasticity model, the Voltage-Dependent Pathway Model (VDP), which is able to replicate those distinct kernel types and intermediate versions with varying LTP/LTD ratios and symmetry features. In addition, unlike previous models it retains these characteristics for different neuron models, which allows for comparison of plasticity in different neuron types. The plastic updates depend on the relative strength and activation of separately modeled LTP and LTD pathways, which are modulated by glutamate release and postsynaptic voltage. We used the 15 neuron type parametrizations in the GLIF5 model presented by Teeter et al. (2018) in combination with the VDP to simulate a range of standard plasticity protocols including standard STDP experiments, frequency dependency experiments and low frequency stimulation protocols. Slight variation in kernel stability and frequency effects can be identified between the neuron types, suggesting that the neuron type may have an effect on the effective learning rule. This plasticity model builds a middle ground between biophysical and phenomenological models allowing not just for the combination with more complex and biophysical neuron models, but is also computationally efficient so can be used in network simulations. Therefore it offers the possibility to explore the functional role of the different kernel types and electrophysiological differences in heterogeneous networks in future work.
NMC4 Short Talk: Brain-inspired spiking neural network controller for a neurorobotic whisker system
It is common for animals to use self-generated movements to actively sense the surrounding environment. For instance, rodents rhythmically move their whiskers to explore the space close to their body. The mouse whisker system has become a standard model to study active sensing and sensorimotor integration through feedback loops. In this work, we developed a bioinspired spiking neural network model of the sensorimotor peripheral whisker system, modelling trigeminal ganglion, trigeminal nuclei, facial nuclei, and central pattern generator neuronal populations. This network was embedded in a virtual mouse robot, exploiting the Neurorobotics Platform, a simulation platform offering a virtual environment to develop and test robots driven by brain-inspired controllers. Eventually, the peripheral whisker system was properly connected to an adaptive cerebellar network controller. The whole system was able to drive active whisking with learning capability, matching neural correlates of behaviour experimentally recorded in mice.
NMC4 Short Talk: Sensory intermixing of mental imagery and perception
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.
NMC4 Short Talk: The complete connectome of an insect brain
Brains must integrate complex sensory information and compare to past events to generate appropriate behavioral responses. The neural circuit basis of these computations is unclear and the underlying structure unknown. Here, we mapped the comprehensive synaptic wiring diagram of the fruit fly larva brain, which contains 3,013 neurons and 544K synaptic sites. It is the most complete insect connectome to date: 1) Both brain hemispheres are reconstructed, allowing investigation of neural pathways that include contralateral axons, which we found in 37% of brain neurons. 2) All sensory neurons and descending neurons are reconstructed, allowing one to follow signals in an uninterrupted chain—from the sensory periphery, through the brain, to motor neurons in the nerve cord. We developed novel computational tools, allowing us to cluster the brain and investigate how information flows through it. We discovered that feedforward pathways from sensory to descending neurons are multilayered and highly multimodal. Robust feedback was observed at almost all levels of the brain, including descending neurons. We investigated how the brain hemispheres communicate with each other and the nerve cord, leading to identification of novel circuit motifs. This work provides the complete blueprint of a brain and a strong foundation to study the structure-function relationship of neural circuits.
NMC4 Short Talk: Hypothesis-neutral response-optimized models of higher-order visual cortex reveal strong semantic selectivity
Modeling neural responses to naturalistic stimuli has been instrumental in advancing our understanding of the visual system. Dominant computational modeling efforts in this direction have been deeply rooted in preconceived hypotheses. In contrast, hypothesis-neutral computational methodologies with minimal apriorism which bring neuroscience data directly to bear on the model development process are likely to be much more flexible and effective in modeling and understanding tuning properties throughout the visual system. In this study, we develop a hypothesis-neutral approach and characterize response selectivity in the human visual cortex exhaustively and systematically via response-optimized deep neural network models. First, we leverage the unprecedented scale and quality of the recently released Natural Scenes Dataset to constrain parametrized neural models of higher-order visual systems and achieve novel predictive precision, in some cases, significantly outperforming the predictive success of state-of-the-art task-optimized models. Next, we ask what kinds of functional properties emerge spontaneously in these response-optimized models? We examine trained networks through structural ( feature visualizations) as well as functional analysis (feature verbalizations) by running `virtual' fMRI experiments on large-scale probe datasets. Strikingly, despite no category-level supervision, since the models are solely optimized for brain response prediction from scratch, the units in the networks after optimization act as detectors for semantic concepts like `faces' or `words', thereby providing one of the strongest evidences for categorical selectivity in these visual areas. The observed selectivity in model neurons raises another question: are the category-selective units simply functioning as detectors for their preferred category or are they a by-product of a non-category-specific visual processing mechanism? To investigate this, we create selective deprivations in the visual diet of these response-optimized networks and study semantic selectivity in the resulting `deprived' networks, thereby also shedding light on the role of specific visual experiences in shaping neuronal tuning. Together with this new class of data-driven models and novel model interpretability techniques, our study illustrates that DNN models of visual cortex need not be conceived as obscure models with limited explanatory power, rather as powerful, unifying tools for probing the nature of representations and computations in the brain.
NMC4 Keynote: A network perspective on cognitive effort
Cognitive effort has long been an important explanatory factor in the study of human behavior in health and disease. Yet, the biophysical nature of cognitive effort remains far from understood. In this talk, I will offer a network perspective on cognitive effort. I will begin by canvassing a recent perspective that casts cognitive effort in the framework of network control theory, developed and frequently used in systems engineering. The theory describes how much energy is required to move the brain from one activity state to another, when activity is constrained to pass along physical pathways in a connectome. I will then turn to empirical studies that link this theoretical notion of energy with cognitive effort in a behaviorally demanding task, and with a metabolic notion of energy as accessible to FDG-PET imaging. Finally, I will ask how this structurally-constrained activity flow can provide us with insights about the brain’s non-equilibrium nature. Using a general tool for quantifying entropy production in macroscopic systems, I will provide evidence to suggest that states of marked cognitive effort are also states of greater entropy production. Collectively, the work I discuss offers a complementary view of cognitive effort as a dynamical process occurring atop a complex network.
Networking—the key to success… especially in the brain
In our everyday lives, we form connections and build up social networks that allow us to function successfully as individuals and as a society. Our social networks tend to include well-connected individuals who link us to other groups of people that we might otherwise have limited access to. In addition, we are more likely to befriend individuals who a) live nearby and b) have mutual friends. Interestingly, neurons tend to do the same…until development is perturbed. Just like social networks, neuronal networks require highly connected hubs to elicit efficient communication at minimal cost (you can’t befriend everybody you meet, nor can every neuron wire with every other!). This talk will cover some of Alex’s work showing that microscopic (cellular scale) brain networks inferred from spontaneous activity show similar complex topology to that previously described in macroscopic human brain scans. The talk will also discuss what happens when neurodevelopment is disrupted in the case of a monogenic disorder called Rett Syndrome. This will include simulations of neuronal activity and the effects of manipulation of model parameters as well as what happens when we manipulate real developing networks using optogenetics. If functional development can be restored in atypical networks, this may have implications for treatment of neurodevelopmental disorders like Rett Syndrome.
Adaptive bottleneck to pallium for sequence memory, path integration and mixed selectivity representation
Spike-driven adaptation involves intracellular mechanisms that are initiated by neural firing and lead to the subsequent reduction of spiking rate followed by a recovery back to baseline. We report on long (>0.5 second) recovery times from adaptation in a thalamic-like structure in weakly electric fish. This adaptation process is shown via modeling and experiment to encode in a spatially invariant manner the time intervals between event encounters, e.g. with landmarks as the animal learns the location of food. These cells also come in two varieties, ones that care only about the time since the last encounter, and others that care about the history of encounters. We discuss how the two populations can share in the task of representing sequences of events, supporting path integration and converting from ego-to-allocentric representations. The heterogeneity of the population parameters enables the representation and Bayesian decoding of time sequences of events which may be put to good use in path integration and hilus neuron function in hippocampus. Finally we discuss how all the cells of this gateway to the pallium exhibit mixed selectivity of social features of their environment. The data and computational modeling further reveal that, in contrast to a long-held belief, these gymnotiform fish are endowed with a corollary discharge, albeit only for social signalling.
The bounded rationality of probability distortion
In decision-making under risk (DMR) participants' choices are based on probability values systematically different from those that are objectively correct. Similar systematic distortions are found in tasks involving relative frequency judgments (JRF). These distortions limit performance in a wide variety of tasks and an evident question is, why do we systematically fail in our use of probability and relative frequency information? We propose a Bounded Log-Odds Model (BLO) of probability and relative frequency distortion based on three assumptions: (1) log-odds: probability and relative frequency are mapped to an internal log-odds scale, (2) boundedness: the range of representations of probability and relative frequency are bounded and the bounds change dynamically with task, and (3) variance compensation: the mapping compensates in part for uncertainty in probability and relative frequency values. We compared human performance in both DMR and JRF tasks to the predictions of the BLO model as well as eleven alternative models each missing one or more of the underlying BLO assumptions (factorial model comparison). The BLO model and its assumptions proved to be superior to any of the alternatives. In a separate analysis, we found that BLO accounts for individual participants’ data better than any previous model in the DMR literature. We also found that, subject to the boundedness limitation, participants’ choice of distortion approximately maximized the mutual information between objective task-relevant values and internal values, a form of bounded rationality.
Learning to see Stuff
Materials with complex appearances, like textiles and foodstuffs, pose challenges for conventional theories of vision. How does the brain learn to see properties of the world—like the glossiness of a surface—that cannot be measured by any other senses? Recent advances in unsupervised deep learning may help shed light on material perception. I will show how an unsupervised deep neural network trained on an artificial environment of surfaces that have different shapes, materials and lighting, spontaneously comes to encode those factors in its internal representations. Most strikingly, the model makes patterns of errors in its perception of material that follow, on an image-by-image basis, the patterns of errors made by human observers. Unsupervised deep learning may provide a coherent framework for how many perceptual dimensions form, in material perception and beyond.
Characterising the brain representations behind variations in real-world visual behaviour
Not all individuals are equally competent at recognizing the faces they interact with. Revealing how the brains of different individuals support variations in this ability is a crucial step to develop an understanding of real-world human visual behaviour. In this talk, I will present findings from a large high-density EEG dataset (>100k trials of participants processing various stimulus categories) and computational approaches which aimed to characterise the brain representations behind real-world proficiency of “super-recognizers”—individuals at the top of face recognition ability spectrum. Using decoding analysis of time-resolved EEG patterns, we predicted with high precision the trial-by-trial activity of super-recognizers participants, and showed that evidence for face recognition ability variations is disseminated along early, intermediate and late brain processing steps. Computational modeling of the underlying brain activity uncovered two representational signatures supporting higher face recognition ability—i) mid-level visual & ii) semantic computations. Both components were dissociable in brain processing-time (the first around the N170, the last around the P600) and levels of computations (the first emerging from mid-level layers of visual Convolutional Neural Networks, the last from a semantic model characterising sentence descriptions of images). I will conclude by presenting ongoing analyses from a well-known case of acquired prosopagnosia (PS) using similar computational modeling of high-density EEG activity.
Behavioral and neurobiological mechanisms of social cooperation
Human society operates on large-scale cooperation and shared norms of fairness. However, individual differences in cooperation and incentives to free-riding on others’ cooperation make large-scale cooperation fragile and can lead to reduced social-welfare. Deciphering the neural codes representing potential rewards/costs for self and others is crucial for understanding social decision-making and cooperation. I will first talk about how we integrate computational modeling with functional magnetic resonance imaging to investigate the neural representation of social value and the modulation by oxytocin, a nine-amino acid neuropeptide, in participants evaluating monetary allocations to self and other (self-other allocations). Then I will introduce our recent studies examining the neurobiological mechanisms underlying intergroup decision-making using hyper-scanning, and share with you how we alter intergroup decisions using psychological manipulations and pharmacological challenge. Finally, I will share with you our on-going project that reveals how individual cooperation spreads through human social networks. Our results help to better understand the neurocomputational mechanism underlying interpersonal and intergroup decision-making.
Learning under uncertainty in autism and anxiety
Optimally interacting with a changeable and uncertain world requires estimating and representing uncertainty. Psychiatric and neurodevelopmental conditions such as anxiety and autism are characterized by an altered response to uncertainty. I will review the evidence for these phenomena from computational modelling, and outline the planned experiments from our lab to add further weight to these ideas. If time allows, I will present results from a control sample in a novel task interrogating a particular type of uncertainty and their associated transdiagnostic psychiatric traits.
Capacitance clamp - artificial capacitance in biological neurons via dynamic clamp
A basic time scale in neural dynamics from single cells to the network level is the membrane time constant - set by a neuron’s input resistance and its capacitance. Interestingly, the membrane capacitance appears to be more dynamic than previously assumed with implications for neural function and pathology. Indeed, altered membrane capacitance has been observed in reaction to physiological changes like neural swelling, but also in ageing and Alzheimer's disease. Importantly, according to theory, even small changes of the capacitance can affect neuronal signal processing, e.g. increase network synchronization or facilitate transmission of high frequencies. In experiment, robust methods to modify the capacitance of a neuron have been missing. Here, we present the capacitance clamp - an electrophysiological method for capacitance control based on an unconventional application of the dynamic clamp. In its original form, dynamic clamp mimics additional synaptic or ionic conductances by injecting their respective currents. Whereas a conductance directly governs a current, the membrane capacitance determines how fast the voltage responds to a current. Accordingly, capacitance clamp mimics an altered capacitance by injecting a dynamic current that slows down or speeds up the voltage response (Fig 1 A). For the required dynamic current, the experimenter only has to specify the original cell and the desired target capacitance. In particular, capacitance clamp requires no detailed model of present conductances and thus can be applied in every excitable cell. To validate the capacitance clamp, we performed numerical simulations of the protocol and applied it to modify the capacitance of cultured neurons. First, we simulated capacitance clamp in conductance based neuron models and analysed impedance and firing frequency to verify the altered capacitance. Second, in dentate gyrus granule cells from rats, we could reliably control the capacitance in a range of 75 to 200% of the original capacitance and observed pronounced changes in the shape of the action potentials: increasing the capacitance reduced after-hyperpolarization amplitudes and slowed down repolarization. To conclude, we present a novel tool for electrophysiology: the capacitance clamp provides reliable control over the capacitance of a neuron and thereby opens a new way to study the temporal dynamics of excitable cells.
Models of Core Knowledge (Physics, Really)
Even young children seem to have an early understanding of the world around them, and the people in it. Before children can reliably say "ball", "wall", or "Saul", they expect balls to not go through walls, and for Saul to go right for a ball (if there's no wall). What is the formal conceptual structure underlying this commonsense reasoning about objects and agents? I will raise several possibilities for models underlying core intuitive physics as a way of talking about models of core knowledge and intuitive theories more generally. In particular, I will present some recent ML work trying to capture early expectations about object solidly, cohesion, and permanence, that relies on a rough-derendering approach.
A dynamical model of the visual cortex
In the past several years, I have been involved in building a biologically realistic model of the monkey visual cortex. Work on one of the input layers (4Ca) of the primary visual cortex (V1) is now nearly complete, and I would like to share some of what I have learned with the community. After a brief overview of the model and its capabilities, I would like to focus on three sets of results that represent three different aspects of the modeling. They are: (i) emergent E-I dynamics in local circuits; (ii) how visual cortical neurons acquire their ability to detect edges and directions of motion, and (iii) a view across the cortical surface: nonequilibrium steady states (in analogy with statistical mechanics) and beyond.
A macaque connectome for simulating large-scale network dynamics in The VirtualBrain
TheVirtualBrain (TVB; thevirtualbrain.org) is a software platform for simulating whole-brain network dynamics. TVB models link biophysical parameters at the cellular level with systems-level functional neuroimaging signals. Data available from animal models can provide vital constraints for the linkage across spatial and temporal scales. I will describe the construction of a macaque cortical connectome as an initial step towards a comprehensive multi-scale macaque TVB model. I will also describe our process of validating the connectome and show an example simulation of macaque resting-state dynamics using TVB. This connectome opens the opportunity for the addition of other available data from the macaque, such as electrophysiological recordings and receptor distributions, to inform multi-scale models of brain dynamics. Future work will include extensions to neurological conditions and other nonhuman primate species.
How dendrites help solve biological and machine learning problems
Dendrites are thin processes that extend from the cell body of neurons, the main computing units of the brain. The role of dendrites in complex brain functions has been investigated for several decades, yet their direct involvement in key behaviors such as for example sensory perception has only recently been established. In my presentation I will discuss how computational modelling has helped us illuminate dendritic function. I will present the main findings of a number of projects in lab dealing with dendritic nonlinearities in excitatory and inhibitory and their consequences on neuronal tuning and memory formation, the role of dendrites in solving nonlinear problems in human neurons and recent efforts to advance machine learning algorithms by adopting dendritic features.
Neural circuit parameter variability, robustness, and homeostasis
Neurons and neural circuits can produce stereotyped and reliable output activity on the basis of highly variable cellular, synaptic, and circuit properties. This is crucial for proper nervous system function throughout an animal’s life in the face of growth, perturbations, and molecular turnover. But how can reliable output arise from neurons and synapses whose parameter vary between individuals in a population, and within an individual over time? I will review how a combination of experimental and computational methods can be used to examine how neuron and network function depends on the underlying parameters, such as neuronal membrane conductances and synaptic strengths. Within the high-dimensional parameter space of a neural system, the subset of parameter combinations that produce biologically functional neuron or circuit activity is captured by the notion of a ‘solution space’. I will describe solution space structures determined from electrophysiology data, ion channel expression levels across populations of neurons and animals, and computational parameter space explorations. A key finding centers on experimental and computational evidence for parameter correlations that give structure to solution spaces. Computational modeling suggests that such parameter correlations can be beneficial for constraining neuron and circuit properties to functional regimes, while experimental results indicate that neural circuits may have evolved to implement some of these beneficial parameter correlations at the cellular level. Finally, I will review modeling work and experiments that seek to illuminate how neural systems can homeostatically navigate their parameter spaces to stably remain within their solution space and reliably produce functional output, or to return to their solution space after perturbations that temporarily disrupt proper neuron or network function.
Cortical networks for flexible decisions during spatial navigation
My lab seeks to understand how the mammalian brain performs the computations that underlie cognitive functions, including decision-making, short-term memory, and spatial navigation, at the level of the building blocks of the nervous system, cell types and neural populations organized into circuits. We have developed methods to measure, manipulate, and analyze neural circuits across various spatial and temporal scales, including technology for virtual reality, optical imaging, optogenetics, intracellular electrophysiology, molecular sensors, and computational modeling. I will present recent work that uses large scale calcium imaging to reveal the functional organization of the mouse posterior cortex for flexible decision-making during spatial navigation in virtual reality. I will also discuss work that uses optogenetics and calcium imaging during a variety of decision-making tasks to highlight how cognitive experience and context greatly alter the cortical circuits necessary for navigation decisions.
Modelling affective biases in rodents: behavioural and computational approaches
My research focuses, broadly speaking, on how emotions impact decision making. Specifically, I am interested in affective biases, a phenomenon known to be important in depression. Using a rodent decision-making task, combined with computational modelling I have investigated how different antidepressant and pro-depressant manipulations that are known to alter mood in humans alter judgement bias, and provided insight into the decision processes that underlie these behaviours. I will also highlight how the combination of behaviour and modelling can provide a truly translation approach, enabling comparison and interpretation of the same cognitive processes between animal and human research.
Mice alternate between discrete strategies during perceptual decision-making
Classical models of perceptual decision-making assume that animals use a single, consistent strategy to integrate sensory evidence and form decisions during an experiment. In this talk, I aim to convince you that this common view is incorrect. I will show results from applying a latent variable framework, the “GLM-HMM”, to hundreds of thousands of trials of mouse choice data. Our analysis reveals that mice don’t lapse. Instead, mice switch back and forth between engaged and disengaged behavior within a single session, and each mode of behavior lasts tens to hundreds of trials.
Theory-driven probabilistic modeling of language use: a case study on quantifiers, logic and typicality
Theoretical linguistics postulates abstract structures that successfully explain key aspects of language. However, the precise relation between abstract theoretical ideas and empirical data from language use is not always apparent. Here, we propose to empirically test abstract semantic theories through the lens of probabilistic pragmatic modelling. We consider the historically important case of quantity words (e.g., `some', `all'). Data from a large-scale production study seem to suggest that quantity words are understood via prototypes. But based on statistical and empirical model comparison, we show that a probabilistic pragmatic model that embeds a strict truth-conditional notion of meaning explains the data just as well as a model that encodes prototypes into the meaning of quantity words.
A multiscale approach to brain disorders
Exploration beyond bandits
Machine learning researchers frequently focus on human-level performance, in particular in games. However, in these applications human (or human-level) behavior is commonly reduced to a simple dot on a performance graph. Cognitive science, in particular theories of learning and decision making, could hold the key to unlock what is behind this dot, thereby gaining further insights into human cognition and the design principles of intelligent algorithms. However, cognitive experiments commonly focus on relatively simple paradigms such as restricted multi-armed bandit tasks. In this talk, I will argue that cognitive science can turn its lens to more complex scenarios to study exploration in real-world domains and online games. I will show in one large data set of online food delivery orders and across many online games how current cognitive theories of learning and exploration can describe human behavior in the wild, but also how these tasks demand us to expand our theoretical toolkit to describe a rich repertoire of real-world behaviors such as empowerment and fun.
How to simulate and analyze drift-diffusion models of timing and decision making
My talk will discuss the use of some of these four, simple Matlab functions to simulate models of timing, and to fit models to empirical data. Feel free to examine the code and the relatively brief book chapter that explains the code before the talk if you would like to learn more about computational/mathematical modeling.
Uncertainty in learning and decision making
Uncertainty plays a critical role in reinforcement learning and decision making. However, exactly how subjective uncertainty influences behaviour remains unclear. Multi-armed bandits are a useful framework to gain more insight into this. Paired with computational tools such as Kalman filters, they allow us to closely characterize the interplay between trial-by-trial value, uncertainty, learning, and choice. In this talk, I will present recent research where we also measured participants visual fixations on the options in a multi-armed bandit task. The estimated value of each option, and the uncertainty in these estimations, influenced what subjects looked at in the period before making a choice and their subsequent choice, as additionally did fixation itself. Uncertainty also determined how long participants looked at the obtained outcomes. Our findings clearly show the importance of uncertainty in learning and decision making.
Cognitive Psychometrics: Statistical Modeling of Individual Differences in Latent Processes
Many psychological theories assume that qualitatively different cognitive processes can result in identical responses. Multinomial processing tree (MPT) models allow researchers to disentangle latent cognitive processes based on observed response frequencies. Recently, MPT models have been extended to explicitly account for participant and item heterogeneity. These hierarchical Bayesian MPT models provide the opportunity to connect two traditionally isolated disciplines. Whereas cognitive psychology has often focused on the experimental validation of MPT model parameters on the group level, psychometrics provides the necessary concepts and tools for measuring differences in MPT parameters on the item or person level. Moreover, MPT parameters can be regressed on covariates to model latent processes as a function of personality traits or other person characteristics.
Generalization guided exploration
How do people learn in real-world environments where the space of possible actions can be vast or even infinite? The study of human learning has made rapid progress in past decades, from discovering the neural substrate of reward prediction errors, to building AI capable of mastering the game of Go. Yet this line of research has primarily focused on learning through repeated interactions with the same stimuli. How are humans able to rapidly adapt to novel situations and learn from such sparse examples? I propose a theory of how generalization guides human learning, by making predictions about which unobserved options are most promising to explore. Inspired by Roger Shepard’s law of generalization, I show how a Bayesian function learning model provides a mechanism for generalizing limited experiences to a wide set of novel possibilities, based on the simple principle that similar actions produce similar outcomes. This model of generalization generates predictions about the expected reward and underlying uncertainty of unexplored options, where both are vital components in how people actively explore the world. This model allows us to explain developmental differences in the explorative behavior of children, and suggests a general principle of learning across spatial, conceptual, and structured domains.
‘Optimistic’ and ‘pessimistic’ decision-making as an indicator of animal emotion and welfare
Reliable and validated measures of emotion in animals are of great import; they are crucial to better understanding and developing treatments for human mood disorders, and they are necessary for ensuring good animal welfare. We have developed a novel measure of emotion in animals that is grounded in theory and psychological research – decision-making under ambiguity. Specifically, we consider that more ‘optimistic’ decisions about ambiguous stimuli reflect more positive emotional states, while the opposite is true for more ‘pessimistic’ decisions. In this talk, we will outline the background behind and implementation of this measure, meta-analyses that have been conducted to validate the measure, and discuss how computational modelling has been used to further understand the cognitive processes underlying ‘optimistic’ and ‘pessimistic’ decision-making as an indicator of animal emotion and welfare.
Awakening: Predicting external stimulation to force transitions between different brain states
Cones with character: An in vivo circuit implementation of efficient coding
In this talk I will summarize some of our recent unpublished work on spectral coding in the larval zebrafish retina. Combining 2p imaging, hyperspectral stimulation, computational modeling and connectomics, we take a renewed look at the spectral tuning of cone photoreceptors in the live eye. We find that already cones optimally rotate natural colour space in a PCA-like fashion to disambiguate greyscale from "colour" information. We then follow this signal through the retinal layers and ultimately into the brain to explore the major spectral computations performed by the visual system at its consecutive stages. We find that by and large, zebrafish colour vision can be broken into three major spectral zones: long wavelength grey-scale-like vision, short-wavelength prey capture circuits, and spectrally diverse mid-wavelength circuits which possibly support the bulk of "true colour vision" in this tetrachromate vertebrate.
Self-organisation in interneuron circuits
Inhibitory interneurons come in different classes and form intricate circuits. While our knowledge of these circuits has advanced substantially over the last decades, it is not fully understood how the structure of these circuits relates to their function. I will present some of our recent attempts to “understand” the structure of interneuron circuits by means of computational modeling. Surprisingly (at least for us), we found that prominent features of inhibitory circuitry can be accounted for by an optimisation for excitation-inhibition (E/I) balance. In particular, we find that such an optimisation generates networks that resemble mouse V1 in terms of the structure of synaptic efficacies between principal cells and parvalbumin-positive interneurons. Moreover, an optimisation for E/I balance across neuronal compartments promotes a functional diversification of interneurons into two classes that resemble parvalbumin and somatostatin-positive interneurons. Time permitting, I may briefly touch on recent work in which we link E/I balance to prediction error coding in V1.
Interneuron desynchronization and breakdown of long-term place cell stability in temporal lobe epilepsy
Temporal lobe epilepsy is associated with memory deficits but the circuit mechanisms underlying these cognitive disabilities are not understood. We used electrophysiological recordings, open-source wire-free miniaturized microscopy and computational modeling to probe these deficits in a model of temporal lobe epilepsy. We find desynchronization of dentate gyrus interneurons with CA1 interneurons during theta oscillations and a loss of precision and stability of place fields. We also find that emergence of place cell dysfunction is delayed, providing a potential temporal window for treatments. Computation modeling shows that desynchronization rather than interneuron cell loss can drive place cell dysfunction. Future studies will uncover cell types driving these changes and transcriptional changes that may be driving dysfunction.
Who can turn faster? Comparison of the head direction circuit of two species
Ants, bees and other insects have the ability to return to their nest or hive using a navigation strategy known as path integration. Similarly, fruit flies employ path integration to return to a previously visited food source. An important component of path integration is the ability of the insect to keep track of its heading relative to salient visual cues. A highly conserved brain region known as the central complex has been identified as being of key importance for the computations required for an insect to keep track of its heading. However, the similarities or differences of the underlying heading tracking circuit between species are not well understood. We sought to address this shortcoming by using reverse engineering techniques to derive the effective underlying neural circuits of two evolutionary distant species, the fruit fly and the locust. Our analysis revealed that regardless of the anatomical differences between the two species the essential circuit structure has not changed. Both effective neural circuits have the structural topology of a ring attractor with an eight-fold radial symmetry (Fig. 1). However, despite the strong similarities between the two ring attractors, there remain differences. Using computational modelling we found that two apparently small anatomical differences have significant functional effect on the ability of the two circuits to track fast rotational movements and to maintain a stable heading signal. In particular, the fruit fly circuit responds faster to abrupt heading changes of the animal while the locust circuit maintains a heading signal that is more robust to inhomogeneities in cell membrane properties and synaptic weights. We suggest that the effects of these differences are consistent with the behavioural ecology of the two species. On the one hand, the faster response of the ring attractor circuit in the fruit fly accommodates the fast body saccades that fruit flies are known to perform. On the other hand, the locust is a migratory species, so its behaviour demands maintenance of a defined heading for a long period of time. Our results highlight that even seemingly small differences in the distribution of dendritic fibres can have a significant effect on the dynamics of the effective ring attractor circuit with consequences for the behavioural capabilities of each species. These differences, emerging from morphologically distinct single neurons highlight the importance of a comparative approach to neuroscience.
Natural visual stimuli for mice
During the course of evolution, a species’ environment shapes its sensory abilities, as individuals with more optimized sensory abilities are more likely survive and procreate. Adaptations to the statistics of the natural environment can be observed along the early visual pathway and across species. Therefore, characterising the properties of natural environments and studying the representation of natural scenes along the visual pathway is crucial for advancing our understanding of the structure and function of the visual system. In the past 20 years, mice have become an important model in vision research, but the fact that they live in a different environment than primates and have different visual needs is rarely considered. One particular challenge for characterising the mouse’s visual environment is that they are dichromats with photoreceptors that detect UV light, which the typical camera does not record. This also has consequences for experimental visual stimulation, as the blue channel of computer screens fails to excite mouse UV cone photoreceptors. In my talk, I will describe our approach to recording “colour” footage of the habitat of mice – from the mouse’s perspective – and to studying retinal circuits in the ex vivo retina with natural movies.
Cholinergic regulation of learning in the olfactory system
In the olfactory system, cholinergic modulation has been associated with contrast modulation and changes in receptive fields in the olfactory bulb, as well the learning of odor associations in the olfactory cortex. Computational modeling and behavioral studies suggest that cholinergic modulation could improve sensory processing and learning while preventing pro-active interference when task demands are high. However, how sensory inputs and/or learning regulate incoming modulation has not yet been elucidated. We here use a computational model of the olfactory bulb, piriform cortex (PC) and horizontal limb of the diagonal band of Broca (HDB) to explore how olfactory learning could regulate cholinergic inputs to the system in a closed feedback loop. In our model, the novelty of an odor is reflected in firing rates and sparseness of cortical neurons in response to that odor and these firing rates can directly regulate learning in the system by modifying cholinergic inputs to the system.
Motion vision in Drosophila: from single neuron computation to behaviour
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.