Ppar
Ppar
Astrocytes: From Metabolism to Cognition
Different brain cell types exhibit distinct metabolic signatures that link energy economy to cellular function. Astrocytes and neurons, for instance, diverge dramatically in their reliance on glycolysis versus oxidative phosphorylation, underscoring that metabolic fuel efficiency is not uniform across cell types. A key factor shaping this divergence is the structural organization of the mitochondrial respiratory chain into supercomplexes. Specifically, complexes I (CI) and III (CIII) form a CI–CIII supercomplex, but the degree of this assembly varies by cell type. In neurons, CI is predominantly integrated into supercomplexes, resulting in highly efficient mitochondrial respiration and minimal reactive oxygen species (ROS) generation. Conversely, in astrocytes, a larger fraction of CI remains unassembled, freely existing apart from CIII, leading to reduced respiratory efficiency and elevated mitochondrial ROS production. Despite this apparent inefficiency, astrocytes boast a highly adaptable metabolism capable of responding to diverse stressors. Their looser CI–CIII organization allows for flexible ROS signaling, which activates antioxidant programs via transcription factors like Nrf2. This modular architecture enables astrocytes not only to balance energy production but also to support neuronal health and influence complex organismal behaviors.
Social and non-social learning: Common, or specialised, mechanisms? (BACN Early Career Prize Lecture 2022)
The last decade has seen a burgeoning interest in studying the neural and computational mechanisms that underpin social learning (learning from others). Many findings support the view that learning from other people is underpinned by the same, ‘domain-general’, mechanisms underpinning learning from non-social stimuli. Despite this, the idea that humans possess social-specific learning mechanisms - adaptive specializations moulded by natural selection to cope with the pressures of group living - persists. In this talk I explore the persistence of this idea. First, I present dissociations between social and non-social learning - patterns of data which are difficult to explain under the domain-general thesis and which therefore support the idea that we have evolved special mechanisms for social learning. Subsequently, I argue that most studies that have dissociated social and non-social learning have employed paradigms in which social information comprises a secondary, additional, source of information that can be used to supplement learning from non-social stimuli. Thus, in most extant paradigms, social and non-social learning differ both in terms of social nature (social or non-social) and status (primary or secondary). I conclude that status is an important driver of apparent differences between social and non-social learning. When we account for differences in status, we see that social and non-social learning share common (dopamine-mediated) mechanisms.
Can a single neuron solve MNIST? Neural computation of machine learning tasks emerges from the interaction of dendritic properties
Physiological experiments have highlighted how the dendrites of biological neurons can nonlinearly process distributed synaptic inputs. However, it is unclear how qualitative aspects of a dendritic tree, such as its branched morphology, its repetition of presynaptic inputs, voltage-gated ion channels, electrical properties and complex synapses, determine neural computation beyond this apparent nonlinearity. While it has been speculated that the dendritic tree of a neuron can be seen as a multi-layer neural network and it has been shown that such an architecture could be computationally strong, we do not know if that computational strength is preserved under these qualitative biological constraints. Here we simulate multi-layer neural network models of dendritic computation with and without these constraints. We find that dendritic model performance on interesting machine learning tasks is not hurt by most of these constraints and may synergistically benefit from all of them combined. Our results suggest that single real dendritic trees may be able to learn a surprisingly broad range of tasks through the emergent capabilities afforded by their properties.
On the link between conscious function and general intelligence in humans and machines
In popular media, there is often a connection drawn between the advent of awareness in artificial agents and those same agents simultaneously achieving human or superhuman level intelligence. In this talk, I will examine the validity and potential application of this seemingly intuitive link between consciousness and intelligence. I will do so by examining the cognitive abilities associated with three contemporary theories of conscious function: Global Workspace Theory (GWT), Information Generation Theory (IGT), and Attention Schema Theory (AST), and demonstrating that all three theories specifically relate conscious function to some aspect of domain-general intelligence in humans. With this insight, we will turn to the field of Artificial Intelligence (AI) and find that, while still far from demonstrating general intelligence, many state-of-the-art deep learning methods have begun to incorporate key aspects of each of the three functional theories. Given this apparent trend, I will use the motivating example of mental time travel in humans to propose ways in which insights from each of the three theories may be combined into a unified model. I believe that doing so can enable the development of artificial agents which are not only more generally intelligent but are also consistent with multiple current theories of conscious function.
A predictive-processing account of psychosis
There has been increasing interest in the neurocomputational mechanisms underlying psychotic disorders in recent years. One promising approach is based on the theoretical framework of predictive processing, which proposes that inferences regarding the state of the world are made by combining prior beliefs with sensory signals. Delusions and hallucinations are the core symptoms of psychosis and often co-occur. Yet, different predictive-processing alterations have been proposed for these two symptom dimensions, according to which the relative weighting of prior beliefs in perceptual inference is decreased or increased, respectively. I will present recent behavioural, neuroimaging, and computational work that investigated perceptual decision-making under uncertainty and ambiguity to elucidate the changes in predictive processing that may give rise to psychotic experiences. Based on the empirical findings presented, I will provide a more nuanced predictive-processing account that suggests a common mechanism for delusions and hallucinations at low levels of the predictive-processing hierarchy, but still has the potential to reconcile apparently contradictory findings in the literature. This account may help to understand the heterogeneity of psychotic phenomenology and explain changes in symptomatology over time.
A draft connectome for ganglion cell types of the mouse retina
The visual system of the brain is highly parallel in its architecture. This is clearly evident in the outputs of the retina, which arise from neurons called ganglion cells. Work in our lab has shown that mammalian retinas contain more than a dozen distinct types of ganglion cells. Each type appears to filter the retinal image in a unique way and to relay this processed signal to a specific set of targets in the brain. My students and I are working to understand the meaning of this parallel organization through electrophysiological and anatomical studies. We record from light-responsive ganglion cells in vitro using the whole-cell patch method. This allows us to correlate directly the visual response properties, intrinsic electrical behavior, synaptic pharmacology, dendritic morphology and axonal projections of single neurons. Other methods used in the lab include neuroanatomical tracing techniques, single-unit recording and immunohistochemistry. We seek to specify the total number of ganglion cell types, the distinguishing characteristics of each type, and the intraretinal mechanisms (structural, electrical, and synaptic) that shape their stimulus selectivities. Recent work in the lab has identified a bizarre new ganglion cell type that is also a photoreceptor, capable of responding to light even when it is synaptically uncoupled from conventional (rod and cone) photoreceptors. These ganglion cells appear to play a key role in resetting the biological clock. It is just this sort of link, between a specific cell type and a well-defined behavioral or perceptual function, that we seek to establish for the full range of ganglion cell types. My research concerns the structural and functional organization of retinal ganglion cells, the output cells of the retina whose axons make up the optic nerve. Ganglion cells exhibit great diversity both in their morphology and in their responses to light stimuli. On this basis, they are divisible into a large number of types (>15). Each ganglion-cell type appears to send its outputs to a specific set of central visual nuclei. This suggests that ganglion cell heterogeneity has evolved to provide each visual center in the brain with pre-processed representations of the visual scene tailored to its specific functional requirements. Though the outline of this story has been appreciated for some time, it has received little systematic exploration. My laboratory is addressing in parallel three sets of related questions: 1) How many types of ganglion cells are there in a typical mammalian retina and what are their structural and functional characteristics? 2) What combination of synaptic networks and intrinsic membrane properties are responsible for the characteristic light responses of individual types? 3) What do the functional specializations of individual classes contribute to perceptual function or to visually mediated behavior? To pursue these questions, we label retinal ganglion cells by retrograde transport from the brain; analyze in vitro their light responses, intrinsic membrane properties and synaptic pharmacology using the whole-cell patch clamp method; and reveal their morphology with intracellular dyes. Recently, we have discovered a novel ganglion cell in rat retina that is intrinsically photosensitive. These ganglion cells exhibit robust light responses even when all influences from classical photoreceptors (rods and cones) are blocked, either by applying pharmacological agents or by dissociating the ganglion cell from the retina. These photosensitive ganglion cells seem likely to serve as photoreceptors for the photic synchronization of circadian rhythms, the mechanism that allows us to overcome jet lag. They project to the circadian pacemaker of the brain, the suprachiasmatic nucleus of the hypothalamus. Their temporal kinetics, threshold, dynamic range, and spectral tuning all match known properties of the synchronization or "entrainment" mechanism. These photosensitive ganglion cells innervate various other brain targets, such as the midbrain pupillary control center, and apparently contribute to a host of behavioral responses to ambient lighting conditions. These findings help to explain why circadian and pupillary light responses persist in mammals, including humans, with profound disruption of rod and cone function. Ongoing experiments are designed to elucidate the phototransduction mechanism, including the identity of the photopigment and the nature of downstream signaling pathways. In other studies, we seek to provide a more detailed characterization of the photic responsiveness and both morphological and functional evidence concerning possible interactions with conventional rod- and cone-driven retinal circuits. These studies are of potential value in understanding and designing appropriate therapies for jet lag, the negative consequences of shift work, and seasonal affective disorder.
Intrinsic Rhythms in a Giant Single-Celled Organism and the Interplay with Time-Dependent Drive, Explored via Self-Organized Macroscopic Waves
Living Systems often seem to follow, in addition to external constraints and interactions, an intrinsic predictive model of the world — a defining trait of Anticipatory Systems. Here we study rhythmic behaviour in Caulerpa, a marine green alga, which appears to predict the day/night light cycle. Caulerpa consists of differentiated organs resembling leaves, stems and roots. While an individual can exceed a meter in size, it is a single multinucleated giant cell. Active transport has been hypothesized to play a key role in organismal development. It has been an open question in the literature whether rhythmic transport phenomena in this organism are of autonomous circadian nature. Using Raspberry-Pi cameras, we track over weeks the morphogenesis of tens of samples concurrently, while tracing at resolution of tens of seconds the variation of the green coverage. The latter reveals waves propagating over centimeters within few hours, and is attributed to chloroplast redistribution at whole-organism scale. Our observations of algal segments regenerating under 12-hour light/dark cycles indicate that the initiation of the waves precedes the external light change. Using time-frequency analysis, we find that the temporal spectrum of these green pulses contains a circadian period. The latter persists over days even under constant illumination, indicative of its autonomous nature. We further explore the system under non-circadian periods, to reveal how the spectral content changes in response. Time-keeping and synchronization are recurring themes in biological research at various levels of description — from subcellular components to ecological systems. We present a seemingly primitive living system that exhibits apparent anticipatory behaviour. This research offers quantitative constraints for theoretical frameworks of such systems.
Metabolic spikes: from rogue electrons to Parkinson's
Conventionally, neurons are thought to be cellular units that process synaptic inputs into synaptic spikes. However, it is well known that neurons can also spike spontaneously and display a rich repertoire of firing properties with no apparent functional relevance e.g. in in vitro cortical slice preparations. In this talk, I will propose a hypothesis according to which intrinsic excitability in neurons may be a survival mechanism to minimize toxic byproducts of the cell’s energy metabolism. In neurons, this toxicity can arise when mitochondrial ATP production stalls due to limited ADP. Under these conditions, electrons deviate from the electron transport chain to produce reactive oxygen species, disrupting many cellular processes and challenging cell survival. To mitigate this, neurons may engage in ADP-producing metabolic spikes. I will explore the validity of this hypothesis using computational models that illustrate the implications of synaptic and metabolic spiking, especially in the context of substantia nigra pars compacta dopaminergic neurons and their degeneration in Parkinson's disease.
How does a neuron decide when and where to make a synapse?
Precise synaptic connectivity is a prerequisite for the function of neural circuits, yet individual neurons, taken out of their developmental context, readily form unspecific synapses. How does genetically encoded brain wiring deal with this apparent contradiction? Brain wiring is a developmental growth process that is not only characterized by precision, but also flexibility and robustness. As in any other growth process, cellular interactions are restricted in space and time. Correspondingly, molecular and cellular interactions are restricted to those that 'get to see' each other during development. This seminar will explore the question how neurons decide when and where to make synapses using the Drosophila visual system as a model. New findings reveal that pattern formation during growth and the kinetics of live neuronal interactions restrict synapse formation and partner choice for neurons that are not otherwise prevented from making incorrect synapses in this system. For example, cell biological mechanisms like autophagy as well as developmental temperature restrict inappropriate partner choice through a process of kinetic exclusion that critically contributes to wiring specificity. The seminar will explore these and other neuronal strategies when and where to make synapses during developmental growth that contribute to precise, flexible and robust outcomes in brain wiring.
Body Representation in Virtual Reality
How the brain represents the body is a fundamental question in cognitive neuroscience. Experimental studies are difficult because ‘the body is always there’ (William James). In recent years immersive virtual reality techniques have been introduced that deliver apparent changes to the body extending earlier techniques such as the rubber hand illusion, or substituting the whole body by a virtual one visually collocated with the real body, and seen from a normal first person perspective. This talk will introduce these techniques, and concentrate on how changing the body can change the mind and behaviour, especially in the context of combatting aggression based on gender or race.
Reverse engineering Hydra
Hydra is an extraordinary creature. Continuously replacing itself, it can live indefinitely, performing a stable repertoire of reasonably sophisticated behaviors. This remarkable stability under plasticity may be due to the uniform nature of its nervous system, which consists of two apparently noncommunicating nerve net layers. We use modeling to understand the role of active muscles and biomechanics interact with neural activity to shape Hydra behaviour. We will discuss our findings and thoughts on how this simple nervous system may self-organize to produce purposeful behavior.
What are the consequences of directing attention within working memory?
The role of attention in working memory remains controversial, but there is some agreement on the notion that the focus of attention holds mnemonic representations in a privileged state of heightened accessibility in working memory, resulting in better memory performance for items that receive focused attention during retention. Closely related, representations held in the focus of attention are often observed to be robust and protected from degradation caused by either perceptual interference (e.g., Makovski & Jiang, 2007; van Moorselaar et al., 2015) or decay (e.g., Barrouillet et al., 2007). Recent findings indicate, however, that representations held in the focus of attention are particularly vulnerable to degradation, and thus, appear to be particularly fragile rather than robust (e.g., Hitch et al., 2018; Hu et al., 2014). The present set of experiments aims at understanding the apparent paradox of information in the focus of attention having a protected vs. vulnerable status in working memory. To that end, we examined the effect of perceptual interference on memory performance for information that was held within vs. outside the focus of attention, across different ways of bringing items in the focus of attention and across different time scales.
Autopilot v0.4.0 - Distributing development of a distributed experimental framework
Autopilot is a Python framework for performing complex behavioral neuroscience experiments by coordinating a swarm of Raspberry Pis. It was designed to not only give researchers a tool that allows them to perform the hardware-intensive experiments necessary for the next generation of naturalistic neuroscientific observation, but also to make it easier for scientists to be good stewards of the human knowledge project. Specifically, we designed Autopilot as a framework that lets its users contribute their technical expertise to a cumulative library of hardware interfaces and experimental designs, and produce data that is clean at the time of acquisition to lower barriers to open scientific practices. As autopilot matures, we have been progressively making these aspirations a reality. Currently we are preparing the release of Autopilot v0.4.0, which will include a new plugin system and wiki that makes use of semantic web technology to make a technical and contextual knowledge repository. By combining human readable text and semantic annotations in a wiki that makes contribution as easy as possible, we intend to make a communal knowledge system that gives a mechanism for sharing the contextual technical knowledge that is always excluded from methods sections, but is nonetheless necessary to perform cutting-edge experiments. By integrating it with Autopilot, we hope to make a first of its kind system that allows researchers to fluidly blend technical knowledge and open source hardware designs with the software necessary to use them. Reciprocally, we also hope that this system will support a kind of deep provenance that makes abstract "custom apparatus" statements in methods sections obsolete, allowing the scientific community to losslessly and effortlessly trace a dataset back to the code and hardware designs needed to replicate it. I will describe the basic architecture of Autopilot, recent work on its community contribution ecosystem, and the vision for the future of its development.
Age-related changes in visual perception – decline or experience?
In Europe, the number of people aged 65 and older is increasing dramatically, and research related to ageing is more crucial than ever. The main research dedicated to age-related changes concentrates on cognitive or sensory deficits. This is also the case in vision research. However, the majority of older adults ages without major cognitive or optical or deficits. These are foremost good news, but even in the absence of neurodegenerative or eye diseases changes in visual perception occur. It has been suggested that age-related changes are due to a general decline of cognitive, perceptual and sensory functions. However, more recent studies reveal large individual differences within the ageing population and whereas some functions show age-related deterioration, others are surprisingly unaffected. Overall, it becomes increasingly apparent that perceptual changes in healthy ageing cannot be attributed to one single underlying factor. I will present studies from various areas of visual perception that challenge the view that age-related changes are primarily related to decline. Instead, our findings suggest that age-related changes are the result of visual experience, such that the brain ages optimally given the input it receives.
The neuroscience of color and what makes primates special
Among mammals, excellent color vision has evolved only in certain non-human primates. And yet, color is often assumed to be just a low-level stimulus feature with a modest role in encoding and recognizing objects. The rationale for this dogma is compelling: object recognition is excellent in grayscale images (consider black-and-white movies, where faces, places, objects, and story are readily apparent). In my talk I will discuss experiments in which we used color as a tool to uncover an organizational plan in inferior temporal cortex (parallel, multistage processing for places, faces, colors, and objects) and a visual-stimulus functional representation in prefrontal cortex (PFC). The discovery of an extensive network of color-biased domains within IT and PFC, regions implicated in high-level object vision and executive functions, compels a re-evaluation of the role of color in behavior. I will discuss behavioral studies prompted by the neurobiology that uncover a universal principle for color categorization across languages, the first systematic study of the color statistics of objects and a chromatic mechanism by which the brain may compute animacy, and a surprising paradoxical impact of memory on face color. Taken together, my talk will put forward the argument that color is not primarily for object recognition, but rather for the assessment of the likely behavioral relevance, or meaning, of the stuff we see.
Herbert Jasper Lecture
There is a long-standing tension between the notion that the hippocampal formation is essentially a spatial mapping system, and the notion that it plays an essential role in the establishment of episodic memory and the consolidation of such memory into structured knowledge about the world. One theory that resolves this tension is the notion that the hippocampus generates rather arbitrary 'index' codes that serve initially to link attributes of episodic memories that are stored in widely dispersed and only weakly connected neocortical modules. I will show how an essentially 'spatial' coding mechanism, with some tweaks, provides an ideal indexing system and discuss the neural coding strategies that the hippocampus apparently uses to overcome some biological constraints affecting the possibility of shipping the index code out widely to the neocortex. Finally, I will present new data suggesting that the hippocampal index code is indeed transferred to layer II-III of the neocortex.
Sparse expansion in cerebellum favours learning speed and performance in the context of motor control
The cerebellum contains more than half of the brain’s neurons and it is essential for motor control. Its neural circuits have a distinctive architecture comprised of a large, sparse expansion from the input mossy fibres to the granule cell layer. For years, theories of how cerebellar architectural features relate to cerebellar function have been formulated. It has been shown that some of these features can facilitate pattern separation. However, these theories don’t consider the need for it to learn fast in order to control smooth and accurate movements. Here, we confront this gap. This talk will show that the expansion to the granule cell layer in the cerebellar cortex improves learning speed and performance in the context of motor control by considering a cerebellar-like network learning an internal model of a motor apparatus online. By expressing the general form of the learning rate for such a system, this talk will provide a calculation of how increasing the number of granule cells diminishes the effect of noise and increases the learning speed. The researchers propose that the particular architecture of cerebellar circuits modifies the geometry of the error function in a favourable way for learning faster. Their results illuminate a new link between cerebellar structure and function.
Free Will and the COINTOB Model of Decision-Making
The COINTOB (conditional intention and integration to bound) model provides a heuristic framework of processes in Libet-style experiments. The model is based on three assumptions. First, brain activation preceding conscious intentions in Libet-style experiments does not reflect an unconscious decision but rather the unfolding of a decision process. Second, the time of conscious decision (W) reflects the moment in time when the decision boundary is crossed. This interpretation of W is consistent with our apparent intuition that we decide in the moment we experience the conscious intention to act. Third, the decision process is configured by conscious intentions that participants form at the beginning of the experiment based on the experimental instruction. Brass and Mele discuss the model, conceptual background for it, and the model’s bearing on free will.
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.
What is serially-dependent perception good for?
Perception can be strongly serially-dependent (i.e. biased toward previously seen stimuli). Recently, serial dependencies in perception were proposed as a mechanism for perceptual stability, increasing the apparent continuity of the complex environments we experience in everyday life. For example, stable scene perception can be actively achieved by the visual system through global serial dependencies, a special kind of serial dependence between summary statistical representations. Serial dependence occurs also between emotional expressions, but it is highly selective for the same identity. Overall, these results further support the notion of serial dependence as a global, highly specialized, and purposeful mechanism. However, serial dependence could also be a deleterious phenomenon in unnatural or unpredictable situations, such as visual search in radiological scans, biasing current judgments toward previous ones even when accurate and unbiased perception is needed. For example, observers make consistent perceptual errors when classifying a tumor- like shape on the current trial, seeing it as more similar to the shape presented on the previous trial. In a separate localization test, observers make consistent errors when reporting the perceived position of an objects on the current trial, mislocalizing it toward the position in the preceding trial. Taken together, these results show two opposite sides of serial dependence; it can be a beneficial mechanism which promotes perceptual stability, but at the same time a deleterious mechanism which impairs our percept when fine recognition is needed.
A novel hypothesis on the role of olfactory bulb granule cells
The role of granule cells in olfactory processing is surrounded by several enigmatic observations, such as the existence of reciprocal spines and the mechanisms for GABA release from them, the missing evidence for functional reciprocal connectivity, and the apparently low inhibitory drive of granule cells, both with respect to recurrent and lateral inhibition. Here, I summarize recent results with regard to GABA release, leading to a novel hypothesis on granule cell function that has the potential to resolve most of these enigmas. I predict that granule cells provide dynamically switched lateral inhibition between coactive glomerular columns and thus possibly a means of olfactory combinatorial coding.
A computational explanation for domain specificity in the human brain
Many regions of the human brain conduct highly specific functions, such as recognizing faces, understanding language, and thinking about other people’s thoughts. Why might this domain specific organization be a good design strategy for brains, and what is the origin of domain specificity in the first place? In this talk, I will present recent work testing whether the segregation of face and object perception in human brains emerges naturally from an optimization for both tasks. We trained artificial neural networks on face and object recognition, and found that networks were able to perform both tasks well by spontaneously segregating them into distinct pathways. Critically, networks neither had prior knowledge nor any inductive bias about the tasks. Furthermore, networks optimized on tasks which apparently do not develop specialization in the human brain, such as food or cars, and object categorization showed less task segregation. These results suggest that functional segregation can spontaneously emerge without a task-specific bias, and that the domain-specific organization of the cortex may reflect a computational optimization for the real-world tasks humans solve.
Microenvironment role in axonal regeneration- looking beyond the neurons
After an injury in the adult mammalian central nervous system, lesioned axons fail to regenerate. This failure to regenerate contrasts with the remarkable potential of axons to grow during embryonic development and after an injury in the peripheral nervous system. Peripheral sensory neurons with cell soma in dorsal root ganglia (DRG) switch to a regenerative state after nerve injury to enable axon regeneration and functional recovery. Decades of research have focused on the signaling pathways elicited by injury in sensory neurons and in Schwann cells that insulate axons as central mechanisms regulating nerve repair. However, neuronal microenvironment is far more complex and is composed of multiple cell types including endothelial, immune and glial cells. Whether the microenvironment surrounding neuronal soma contribute to the poor regenerative outcomes following central injuries remains largely unexplored. To answer this question, we performed a single cell transcriptional profiling of the DRG neuronal microenvironment response to peripheral and central injuries. In dissecting the roles of the microenvironment contribution, we have focused on a poorly studied population of Satellite Glial Cells (SGC) surrounding the neuronal cell soma. This study has uncovered a previously unknown role for SGC in nerve regeneration and defined SGC as transcriptionally distinct from Schwann cells while sharing similarities with astrocytes. Upon a peripheral injury, SGC contribute to axon regeneration via Fatty acid synthase (Fasn)-PPARα signaling pathway. Through repurposing fenofibrate, an FDA- approved PPARα agonist used for dyslipidemia treatment, we were able to rescue the impaired regeneration in mice lacking Fasn in SGC. Our analysis reveals that in response to central injuries, SGC do not activate the PPAR signaling pathway. However, induction of this pathway with fenofibrate treatment, rescued axon regeneration following an injury to the central nerves. Collectively, our results uncovered a previously unappreciated role of the neuronal microenvironment differential response in central and peripheral injuries.
Rapid State Changes Account for Apparent Brain and Behavior Variability
Neural and behavioral responses to sensory stimuli are notoriously variable from trial to trial. Does this mean the brain is inherently noisy or that we don’t completely understand the nature of the brain and behavior? Here we monitor the state of activity of the animal through videography of the face, including pupil and whisker movements, as well as walking, while also monitoring the ability of the animal to perform a difficult auditory or visual task. We find that the state of the animal is continuously changing and is never stable. The animal is constantly becoming more or less activated (aroused) on a second and subsecond scale. These changes in state are reflected in all of the neural systems we have measured, including cortical, thalamic, and neuromodulatory activity. Rapid changes in cortical activity are highly correlated with changes in neural responses to sensory stimuli and the ability of the animal to perform auditory or visual detection tasks. On the intracellular level, these changes in forebrain activity are associated with large changes in neuronal membrane potential and the nature of network activity (e.g. from slow rhythm generation to sustained activation and depolarization). Monitoring cholinergic and noradrenergic axonal activity reveals widespread correlations across the cortex. However, we suggest that a significant component of these rapid state changes arise from glutamatergic pathways (e.g. corticocortical or thalamocortical), owing to their rapidity. Understanding the neural mechanisms of state-dependent variations in brain and behavior promises to significantly “denoise” our understanding of the brain.
A Rare Visuospatial Disorder
Cases with visuospatial abnormalities provide opportunities for understanding the underlying cognitive mechanisms. Three cases of visual mirror-reversal have been reported: AH (McCloskey, 2009), TM (McCloskey, Valtonen, & Sherman, 2006) and PR (Pflugshaupt et al., 2007). This research reports a fourth case, BS -- with focal occipital cortical dysgenesis -- who displays highly unusual visuospatial abnormalities. They initially produced mirror reversal errors similar to those of AH, who -- like the patient in question -- showed a selective developmental deficit. Extensive examination of BS revealed phenomena such as: mirror reversal errors (sometimes affecting only parts of the visual fields) in both horizontal and vertical planes; subjective representation of visual objects and words in distinct left and right visual fields; subjective duplication of objects of visual attention (not due to diplopia); uncertainty regarding the canonical upright orientation of everyday objects; mirror reversals during saccadic eye movements on oculomotor tasks; and failure to integrate visual with other sensory inputs (e.g., they feel themself moving backwards when visual information shows they are moving forward). Fewer errors are produced under conditions of certain visual variables. These and other findings have led the researchers to conclude that BS draws upon a subjective representation of visual space that is structured phenomenally much as it is anatomically in early visual cortex (i.e., rotated through 180 degrees, split into left and right fields, etc.). Despite this, BS functions remarkably well in their everyday life, apparently due to extensive compensatory mechanisms deployed at higher (executive) processing levels beyond the visual modality.
Swimming in the third domain: archaeal extremophiles
Archaea have evolved to survive in some of the most extreme environments on earth. Life in extreme, nutrient-poor conditions gives the opportunity to probe fundamental energy limitations on movement and response to stimuli, two essential markers of living systems. Here we use three-dimensional holographic microscopy and computer simulations to show that halophilic archaea achieve chemotaxis with power requirements one hundred-fold lower than common eubacterial model systems. Their swimming direction is stabilised by their flagella (archaella), enhancing directional persistence in a manner similar to that displayed by eubacteria, albeit with a different motility apparatus. Our experiments and simulations reveal that the cells are capable of slow but deterministic chemotaxis up a chemical gradient, in a biased random walk at the thermodynamic limit.
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.
Neural coding in the auditory cortex - "Emergent Scientists Seminar Series
Dr Jennifer Lawlor Title: Tracking changes in complex auditory scenes along the cortical pathway Complex acoustic environments, such as a busy street, are characterised by their everchanging dynamics. Despite their complexity, listeners can readily tease apart relevant changes from irrelevant variations. This requires continuously tracking the appropriate sensory evidence while discarding noisy acoustic variations. Despite the apparent simplicity of this perceptual phenomenon, the neural basis of the extraction of relevant information in complex continuous streams for goal-directed behavior is currently not well understood. As a minimalistic model for change detection in complex auditory environments, we designed broad-range tone clouds whose first-order statistics change at a random time. Subjects (humans or ferrets) were trained to detect these changes.They were faced with the dual-task of estimating the baseline statistics and detecting a potential change in those statistics at any moment. To characterize the extraction and encoding of relevant sensory information along the cortical hierarchy, we first recorded the brain electrical activity of human subjects engaged in this task using electroencephalography. Human performance and reaction times improved with longer pre-change exposure, consistent with improved estimation of baseline statistics. Change-locked and decision-related EEG responses were found in a centro-parietal scalp location, whose slope depended on change size, consistent with sensory evidence accumulation. To further this investigation, we performed a series of electrophysiological recordings in the primary auditory cortex (A1), secondary auditory cortex (PEG) and frontal cortex (FC) of the fully trained behaving ferret. A1 neurons exhibited strong onset responses and change-related discharges specific to neuronal tuning. PEG population showed reduced onset-related responses, but more categorical change-related modulations. Finally, a subset of FC neurons (dlPFC/premotor) presented a generalized response to all change-related events only during behavior. We show using a Generalized Linear Model (GLM) that the same subpopulation in FC encodes sensory and decision signals, suggesting that FC neurons could operate conversion of sensory evidence to perceptual decision. All together, these area-specific responses suggest a behavior-dependent mechanism of sensory extraction and generalization of task-relevant event. Aleksandar Ivanov Title: How does the auditory system adapt to different environments: A song of echoes and adaptation
Domain Specificity in the Human Brain: What, Whether, and Why?
The last quarter century has provided extensive evidence that some regions of the human cortex are selectively engaged in processing a single specific domain of information, from faces, places, and bodies to language, music, and other people’s thoughts. This work dovetails with earlier theories in cognitive science highlighting domain specificity in human cognition, development, and evolution. But many questions remain unanswered about even the clearest cases of domain specificity in the brain, the selective engagement of the FFA, PPA, and EBA in the perception of faces, places, and bodies, respectively. First, these claims lack precision, saying little about what is computed and how, and relying on human judgements to decide what counts as a face, place, or body. Second, they provide no account of the reliably varying responses of these regions across different “preferred” images, or across different “nonpreferred” images for each category. Third, the category selectivity of each region is vulnerable to refutation if any of the vast set of as-yet-untested nonpreferred images turns out to produce a stronger response than preferred images for that region. Fourth, and most fundamentally, they provide no account of why, from a computational point of view, brains should exhibit this striking degree of functional specificity in the first place, and why we should have the particular visual specializations we do, for faces, places, and bodies, but not (apparently) for food or snakes. The advent of convolutional neural networks (CNNs) to model visual processing in the ventral pathway has opened up many opportunities to address these long-standing questions in new ways. I will describe ongoing efforts in our lab to harness CNNs to do just that.
Apparently high-dimensional spontaneous neural activity is locally low-dimensional in time
COSYNE 2023
Effect of PPARγ modulation on the molecular and functional outcome after peripheral nerve injury
FENS Forum 2024
An ergonomic rodent head fixation apparatus for closed-loop cursor control
FENS Forum 2024