Bases comportementales, moléculaires et cellulaires gouvernant l'apprentissage ambigu et la mémoire chez la drosophile

Extraire les liens prédictifs au sein d'un environnement permet d'appréhender la structure logique du monde. Ceci constitue la base des phénomènes d'apprentissage qui permettent d'établir des liens associatifs entre des évènements de notre entourage. Tout environnement naturel englobe une grande diversité de stimuli composés (i.e. intégrant plusieurs éléments). La façon dont ces stimuli composés sont appréhendés et associés à un renforcement éventuel (i.e. évènement plaisant ou aversif) est un thème fondamental de l'apprentissage associatif. Théoriquement, un stimulus composé AB peut être appris comme la somme de ses composants (A+B), un traitement dit élémentaire, comme un stimulus à part entière (traitement configural, AB=X) ou encore comme une entité comportant à la fois certaines caractéristiques de ses composants ainsi que des propriétés uniques (ou Indice Unique, AB = A+B+u). Ces deux dernières théories permettent notamment d'expliquer la résolution de problèmes ambigus tels que le Negative Patterning (NP) au cours duquel les composants du stimulus AB sont renforcés lorsque présentés seuls mais pas lorsqu'ils sont présentés en tant que composé. Bien que les réseaux neuronaux impliqués dans l'apprentissage associatif élémentaire soient bien connus, les mécanismes permettant la résolution d'apprentissages non élémentaires sont encore peu compris. Dans cette étude, nous démontrons pour la première fois que la Drosophile est capable d'apprentissage non-élémentaire de type NP. L'étude comportementale de la résolution du NP par les mouches montre qu'il passe par la répétition de cycles de conditionnement conduisant à un changement de représentation du mélange AB, s'éloignant peu à peu de la représentation de ses composants A et B. Nous développons ensuite un modèle computationnel à partir de données in vivo sur l'architecture et le fonctionnement des réseaux neuronaux de l'apprentissage olfactif chez la Drosophile, ce qui nous permet de proposer un mécanisme théorique permettant d'expliquer l'apprentissage du NP et dont la validité peut être testée grâce à des outils neurogénétiques. Lors d'un apprentissage de NP, les mouches acquièrent tout d'abord un premier lien associatif entre les composants A et B associés au renforcement, créant par la même occasion une ambiguïté avec leur mélange AB, présenté sans renforcement. Au cours des cycles de conditionnement, les représentations de A et B vis-à-vis de AB sont modulées de façon différentielle, inhibant progressivement la réponse neuronale au stimulus non renforcé tout en renforçant la réponse aux stimuli renforcés. Cette modulation augmente le contraste entre A, B et AB et permet aux drosophiles de résoudre la tâche de NP. Nous identifions les neurones APL (Anterior Paired Lateral) comme implémentation plausible de ce mécanisme, car l'engagement de leur activité inhibitrice spécifiquement durant la présentation de AB est nécessaire pour acquérir le NP sans altérer leurs capacités d'apprentissage dans des tâches non-ambiguës. Nous explorons ensuite l'implication des neurones APL dans un contexte plus général de résolution d'apprentissages ambigus. Pour conclure, notre travail établit la Drosophile comme modèle d'étude d'apprentissage non élémentaire, en proposant une première exploration des réseaux neuronaux sous-jacents à l'aide d'outils uniques à ce modèle. Il ouvre la voie à de nombreux projets dédiés à la compréhension des mécanismes neuronaux permettant aux animaux d'extraire des liens associatifs robustes dans un environnement complexe.Animals' survival heavily relies on their ability to establish causal relationships within their environment. That is made possible through learning experiences during which animals build associative links between the events they are exposed to. Most of the encountered stimuli are actually compounds, the constituents of which may have been reinforced (i.e., associated with a pleasant or unpleasant stimulus) in a different, sometimes opposed way. How compounds are perceived and processed is a central topic in the field of associative learning. In theory, a given compound AB may be learnt as the sum of its components (A+B), which is referred to as "Elemental learning", but it may also be learnt as a distinct stimulus (which Is called "Configural learning"). Finally, AB may bear both constituent-related and compound-specific features called "Unique Cues" (AB = A+B+u). Configural and unique cue processing enable the resolution of ambiguous tasks such as Negative Patterning (NP), during which A and B are reinforced when presented alone but not in a compound AB. Although neural correlates of simple associative learning are well described, those involved in non-elemental learning remain unclear. In this project, we rework a typical olfactory conditioning protocol based on semi-automated olfactory/electric shocks association, allowing us to demonstrate for the first time that Drosophila is able to solve NP tasks. Behavioural study of NP solving shows that its resolution relies on training repetition leading to a gradual change in the compound AB representation, shifting away from its constituents and thus becoming easier to distinguish. Next, we develop a computational model of olfactory associative learning in drosophila based on structural and functional in vivo data. Exploratory simulations of the model allow us to identify a theoretical mechanism enabling NP acquisition, the validity of which can be tested in vivo using neurogenetical tools only available in Drosophila. We propose that during a NP training, flies first acquire associative links between A, B and their reinforcement, which induces an ambiguity as the compound AB is presented without reinforcement. However, over the course of training cycles, non-reinforced stimuli representation is inhibited while the reinforced stimuli representation is consolidated. This differential modulation eventually leads to a shift in odours representation allowing flies to better distinguish between the constituents and their compound thus facilitating NP resolution. We identify APL (Anterior Paired Lateral) neurons as a plausible implementation of this theoretical mechanism, as APL inhibitory activity is specifically engaged during the non-reinforced stimulus presentation, which is necessary for NP acquisition but dispensable for non-ambiguous forms of learning. Lastly, we explore APL role in a broader context of ambiguity resolution. In conclusion, our work validates Drosophila as a robust model to investigate non-elementary learning, and present a promising model of the underlying neural mechanisms using a combination of behaviour, modelling and neurogenetical tools. We believe this opens the way to numerous interesting projects focused on understanding how animals extract robust associations in a complex world

Modeling olfactory processing and insights on optimal learning in constrained neural networks: learning from the anatomy of the Drosophila mushroom body.

Animals adapt their systems to optimise for different competing goals at the same time. Ideally, they will reach an optimal state of equilibrium where the outcome from any goal cannot get better without at the same time making another worse off, similar to the state of Pareto optimaility (Mock 2011). Animals can seek different goals like, to maintain their systems’ stability and robustness, or improving their performances in a given computational task, which is reflected in their memory capacity and ability to make more rewarding decisions. Many species are capable of forming associative memories, they can learn to contextualise sensory stimuli as good, bad or neutral, when they are associated by a shortly upcoming salient outcome and bias their behaviours to approach or avoid these cues in the future. In this work I will focus on modelling the associative learning in the mushroom body circuit of the fruit fly, its center of olfactory associative learning. Flies can learn to associate an odor (sensory experience) with an appetitive or aversive outcome. They do so by modifying the connections between the mushroom body intrinsic neurons, called Kenyon cells (KCs), and their downstream mushroom body output neurons (MBONs). The fly motor behaviour was found to be biased by the activity of the MBONs to either approach or avoid an odor (Aso et al. 2014). Although many studies uncovered the molecular mechanisms and the neurons underpinning associative learning in different species, there has been no work done to answer some specific questions: (a) Why do the neurons in the same circuit within the same animal exhibit variability among each others in their intrinsic properties? It is unknown how variability among the same types of neurons in the same circuit and animal would eventually affect the animal’s optimal behaviour in a computational task. Even previous studies that tackled inter-neuronal variability were trying to study its effect on circuits stability and were dealing with inter-neuronal variability across animals and not within an individual circuit (Marder and Goaillard 2006; Golowasch et al. 2002; Schulz, Goaillard, and Marder 2006; Schulz, Goaillard, and Marder 2007). Can the observed inter-neuronal variability be a result of some optimisation protocol that enhances the circuit computational performance, for example, memory or data performance? Or has it just happened at random? (b) Learning in the cerebellum (and its alike structures in other animals like the fruit fly mushroom body) happen by long term depression (weakening) between its intrinsic neurons -encoding the sensory input- and the downstream neurons that guide the animal’s motor behaviour (Ito 1989). Like in (a), I ask if this learning rule has been conserved across species for optimising some computational aspects of learning In this 3 Chapters thesis, I will present a computational model of associative learning in the fruit fly mushroom body using realistic input odors statistics, as well as putting some constraints on the model network that were observed experimentally in the real mushroom body (e.g. the level of KCs sparse coding, the level of KCs sparse coding when their inhibitory inputs are silenced). In Chapter 2, I will answer the first question, the first aim, of this thesis and show that random variability between the KCs in their intrinsic parameters will impair the fly’s memory performance. I find that the random inter-KCs variability will result in a high variability among the neurons in their sparsity values, which results in very few neurons being specifically active for some odors whilst the vast majority are activated by all incoming odors, that reduces the fly’s ability to distinguish between odors and their identity as ’good’ rewarded or ’bad’ punished odors. However, I show that compensatory variability mechanisms will rescue the memory performance. I present 4 different models (activity-independent and activity-dependent rules) for how this compensatory variability can take place in real neurons. Last but not least, I show that the data from the newly released fly connectome actually reveal compensatory variability in the KCs which agree with my models’ predictions. In Chapter 3, I will answer the second question in this thesis and show that, under some conditions, learning by depression can be more optimal than by potentiation. I will show that if the fly’s decision making policy integrates the information from the MBONs in a divisive normalisation like manner (I explain more about divisive normalisation in Chapter 3), then learning by depression will lead to a higher memory performance. I also suggest a biologically plausible implementation for this normalisation decision policy using a winner-take-all (WTA) circuit model. I predict that in a WTA circuit that integrates the MBONs outputs, the fly’s memory performance will be higher under learning by depression than under potentiation if the noise in the MBONs responses is of multiplicative nature (that is, if the noise in the MBONs responses across different trials is higher at higher MBONs firing rates)

Decision-Making and Action Selection in Honeybees: a Theoretical and Experimental Study

Decision-making is an integral part of everyday life for animals of all species. Some decisions are rapid and based on sensory input alone, others rely on factors such as context and internal motivation. The possibilities for the experimental investigation of choice behaviour in mammals, especially in humans, are seemingly endless. However, neuroscience has struggled to define the neural circuitry behind decision-making processes due to the complex structure of the mammalian brain. For this work we turn to the honeybee for inspiration. With a brain composed of approximately one million neurons and sized at a tiny 1mm3, it may be assumed that such an insect produces mere `programmed' behaviours, yet, the honeybee exhibits a rich, elaborate behavioural repertoire and a large capacity for learning in a variety of different paradigms. Indeed, the honeybee has been identified as a powerful model for decision-making. Sequential sampling models, originating in psychology, have been used to explain rapid decision-making behaviours. Such models assume that noisy sensory evidence is integrated over time until a threshold is reached, whereby a decision is made. These models have proven popular because they are able to fit biological data and are furthermore supported by neural evidence. Additionally, they explain the speed-accuracy trade-off, a behavioural phenomenon also demonstrated in bees. For this work we examine honeybee choice behaviour in different levels of satiation, and show that hungry bees are faster and less accurate than partially satiated bees in a simple choice task. We suggest that differences in choice behaviour may be attributed to a simple mechanism which alters the level of the decision threshold according to how satiated the bee is. We further speculate that the honeybee olfactory system may be a drift-diffusion channel, and develop a simple computational model, based on honeybee neurobiology, with simulations that match behavioural results

Mechanistic Models of Neural Computation in the Fruit Fly Brain

Understanding the operating principles of the brain functions is the key to building novel computing architectures for mimicking human intelligence. Neural activities at different scales lead to different levels of brain functions. For example, cellular functions, such as sensory transduction, occur in the molecular reactions, and cognitive functions, such as recognition, emerge in neural systems across multiple brain regions. To bridge the gap between neuroscience and artificial computation, we need systematic development of mechanistic models for neural computation across multiple scales. Existing models of neural computation are often independently developed for a specific scale and hence not compatible with others. In this thesis, we investigate the neural computations in the fruit fly brain and devise mechanistic models at different scales in a systematic manner so that models at one scale constitute functional building blocks for the next scale. Our study spans from the molecular and circuit computations in the olfactory system to the system-level computation of the central complex in the fruit fly. First, we study how the two key aspects of odorant, identity and concentration, are encoded by the odorant transduction process at the molecular scale. We mathematically quantify the odorant space and propose a biophysical model of the olfactory sensory neuron (OSN). To validate our modeling approaches, we examine the OSN model with a multitude of odorant waveforms and demonstrate that the model output reproduces the temporal responses of OSNs obtained from in vivo electrophysiology recordings. In addition, we evaluate the model at the OSN population level and quantify the combinatorial complexity of the transformation taking place between the odorant space and the OSNs. The resulting concentration-dependent combinatorial code determines the complexity of the input space driving olfactory processing in the downstream neuropil, the antennal lobe. Second, we investigate the neural information processing in the antennal lobe across the molecule scale and the circuit scale. The antennal lobe encodes the output of the OSN population from a concentration-dependent code into a concentration-independent combinatorial code. To study the transformation of the combinatorial code, we construct a computational model of the antennal lobe that consists of two sub circuits, a predictive coding circuit and an on-off circuit, realized by two distinct local neuron networks, respectively. By examining the entire circuit model with both monomolecular odorant and odorant mixtures, we demonstrate that the predictive coding circuit encodes the odorant identity into concentration invariant code and the on-off circuit encodes the onset and the offset of a unique odorant identity. Third, we investigate the odorant representation inherent in the Kenyon cell activities in the mushroom body. The Kenyon cells encodes the output of the antennal lobe into a high-dimensional, sparse neural code that is immediately used for learning and memory formation. We model the Kenyon cell circuitry as a real-time feedback normalization circuit converting odorant information into a time-dependent hash codes. The resultant real-time hash code represents odorants, pure or mixture alike, in a way conducive to classifications, and suggests an intrinsic partition of the odorant space with similar hash codes. Forth, we study at the system scale the neural coding of the central complex. The central complex is a set of neuropils in the center of the fly brain that integrates multiple sensory information and play an important role in locomotor control. We create an application that enables simultaneous graphical querying and construction of executable model of the central complex neural circuitry. By reconfiguring the circuitry and generating different executable models, we compare the model response of the wild type and mutant fly strains. Finally, we show that the multi-scale study of the fruit fly brain is made possible by the Fruit Fly Brain Observatory (FFBO), an open-source platform to support open, collaborative fruit fly neuroscience research. The software architecture of the FFBO and its key application are highlighted along with several examples

Subcellular information processing in the olfactory system

The nervous system is tasked with the challenge of processing a variety of sensory stimuli from the environment with limited coding space and energy consumption. Recent findings challenge the traditional view of the neuron as the elementary functional unit of the nervous system, in which dendrites mainly serve as input sites, and action potential propagation through axons generates output. Instead, individual neurites have emerged as the single functional unit capable of computing inputs and generating outputs locally. Despite recent advances, the link between the mechanisms that facilitate local computations and their behavioural relevance remains unclear. I addressed this problem in Drosophila Melanogaster. The anatomical organisation of the mushroom body, a brain region associated with learning, has a compartmentalised architecture that forms the basis for local computations. My project studied subcellular signalling in the mushroom body and its role in memory formation, with emphasis on the non-spiking APL neuron that is involved in sparse odour coding and memory formation, to determine if it operates locally. To investigate this, I addressed the following points. 1. I investigated the nature of activity spread in the APL neuron. I found that input to the APL neuron evokes activity that attenuates as it propagates, supporting local computations. 2. I characterised the spatial nature of inhibition from the APL neuron onto mushroom body neurons. I found that the inhibition had a strong local effect that diminished with distance. 3. I sought to determine if there are spatial differences in the APL neuron’s response to electric shock, and if plasticity in the APL neuron is similarly spatially distinct. I found that electric shock responses are spatially distinct, but my data on plasticity was inconclusive. 4. I investigated the effects of local muscarine signalling on Kenyon cell odour responses. I found that muscarine signalling has spatially distinct effects

Sparse algorithms for decoding and identification of neural circuits

The brain, as an information processing machine, surpasses any man-made computational device, both in terms of its capabilities and its efficiency. Neuroscience research has made great strides since the foundational works of Cajal and Golgi. However, we still have very little understanding about the algorithmic underpinnings of the brain as an information processor. Identifying mechanistic models of the functional building blocks of the brain will have significant impact not just on neuroscience, but also on artificial computational systems. This provides the main motivation for the work presented in this thesis, summarily i) biologically-inspired algorithms that can be efficiently implemented in silico, ii) functional identification of the processing in certain types of neural circuits, and iii) a collaborative ecosystem for brain research in a model organism, towards the synergistic goal of understanding functional mechanisms employed by the brain. First, this thesis provides a highly parallelizable, biologically-inspired, motion detection algorithm that is based upon the temporal processing of the local (spatial) phase of a visual stimulus. The relation of the phase based motion detector to the widely studied Reichardt detector model, is discussed. Examples are provided comparing the performance of the proposed algorithm with the Reichardt detector as well as the optic flow algorithm, which is the workhorse for motion detection in computer vision. Further, it is shown through examples that the phase based motion detection model provides intuitive explanations for reverse-phi based illusory motion percepts. Then, tractable algorithms are presented for decoding with and identification of neural circuits, comprised of processing that can be described by a second-order Volterra kernel (quadratic filter). It is shown that the Reichardt detector, as well as models of cortical complex cells, can be described by this structure. Examples are provided for decoding of visual stimuli encoded by a population of Reichardt detector cells and complex cells, as well as their identification from observed spike times. Further, the phase based motion detection model is shown to be equivalent to a second-order Volterra kernel acting on two normalized inputs. Subsequently, a general model that computes the ratio of two non-linear functionals, each comprising linear (first order Volterra kernel) and quadratic (second-order Volterra kernel) filters, is proposed. It is shown that, even under these highly non-linear operations, a population of cells can encode stimuli faithfully using a number of measurements that are proportional to the bandwidth of the input stimulus. Tractable algorithms are devised to identify the divisive normalization model and examples of identification are provided for both simulated and biological data. Additionally, an extended framework, comprising parallel channels of divisively normalized cells each subjected to further divisive normalization from lateral feedback connections, is proposed. An algorithm is formulated for identifying all the components in this extended framework from controlled stimulus presentation and observed outputs samples. Finally, the thesis puts forward the Fruit Fly Brain Observatory (FFBO), an initiative to enable a collaborative ecosystem for fruit fly brain research. Key applications in FFBO, and the software and computational infrastructure enabling them, are described along with case studies