Biology subjects

Lajoie, G.

Publications and source records attributed to Lajoie, G..

28 records · Page 2Linked to original sources

bioRxiv2022

Goal-driven optimization of single-neuron properties in artificial networks reveals regularization role of neural diversity and adaptation

Neurons in the brain have rich and adaptive input-output properties. Features such as heterogeneous f-I curves and spike frequency adaptation are known to place single neurons in optimal coding regimes when facing changing stimuli. Yet, it is still unclear how brain circuits exploit single-neuron flexibility, and how network-level requirements may have shaped such cellular function. To answer this question, a multi-scaled approach is needed where the computations of single neurons and neural circuits must be considered as a complete system. In this work, we use artificial neural networks to systematically investigate single-neuron input-output adaptive mechanisms, optimized in an end-to-end fashion. Throughout the optimization process, each neuron has the liberty to modify its nonlinear activation function, parametrized to mimic f-I curves of biological neurons, and to learn adaptation strategies to modify activation functions in real-time during a task. We find that such networks show much-improved robustness to noise and changes in input statistics. Importantly, we find that this procedure recovers precise coding strategies found in biological neurons, such as gain scaling and fractional order differentiation/integration. Using tools from dynamical systems theory, we analyze the role of these emergent single-neuron properties and argue that neural diversity and adaptation play an active regularization role, enabling neural circuits to optimally propagate information across time.

neuroscience↗

bioRxiv2022

A connectomics-based taxonomy of mammals

Mammalian taxonomies are conventionally defined by morphological traits and genetics. How species differ in terms of neural circuits and whether inter-species differences in neural circuit organization conform to these taxonomies is unknown. The main obstacle for the comparison of neural architectures have been differences in network reconstruction techniques, yielding species-specific connectomes that are not directly comparable to one another. Here we comprehensively chart connectome organization across the mammalian phylogenetic spectrum using a common reconstruction protocol. We analyze the mammalian MRI (MaMI) data set, a database that encompasses high-resolution ex vivo structural and diffusion magnetic resonance imaging (MRI) scans of 124 species across 12 taxonomic orders and 5 superorders, collected using a single protocol on a single scanner. We assess similarity between species connectomes using two methods: similarity of Laplacian eigenspectra and similarity of multiscale topological features. We find greater inter-species similarities among species within the same taxonomic order, suggesting the connectome organization recapitulates traditional taxonomies defined by morphology and genetics. While all connectomes retain hallmark global features and relative proportions of connection classes, inter-species variation is driven by local regional connectivity profiles. By encoding connectomes into a common frame of reference, these findings establish a foundation for investigating how neural circuits change over phylogeny, forging a link from genes to circuits to behaviour.

neuroscience↗

bioRxiv2021

Deliberation gated by opportunity cost adapts to context with urgency

Finding the right amount of deliberation, between insufficient and excessive, is a hard decision making problem that depends on the value we place on our time. Average-reward, putatively encoded by tonic dopamine, serves in existing reinforcement learning theory as the stationary opportunity cost of time, and of deliberation in particular. However, this cost often varies with environmental context that can change over time. Here, we introduce an opportunity cost of deliberation estimated adaptively on multiple timescales to account for non-stationary contextual factors. We use it in a simple decision-making heuristic based on average-reward reinforcement learning (AR-RL) that we call Performance-Gated Deliberation (PGD). We propose PGD as a strategy used by animals wherein deliberation cost is implemented directly as urgency, a previously characterized neural signal effectively controlling the speed of the decision-making process. We show PGD outperforms AR-RL solutions in explaining behaviour and urgency of non-human primates in a context-varying random walk prediction task and is consistent with relative performance and urgency in a context-varying random dot motion task. We make readily testable predictions for both neural activity and behaviour and call for an integrated research program in cognitive and systems neuroscience around the value of time.

neuroscience↗

bioRxiv2020

Learning function from structure in neuromorphic networks

The connection patterns of neural circuits in the brain form a complex network. Collective signaling within the network manifests as patterned neural activity, and is thought to support human cognition and adaptive behavior. Recent technological advances permit macro-scale reconstructions of biological brain networks. These maps, termed connectomes, display multiple non-random architectural features, including heavy-tailed degree distributions, segregated communities and a densely interconnected core. Yet, how computation and functional specialization emerge from network architecture remains unknown. Here we reconstruct human brain connectomes using in vivo diffusion-weighted imaging, and use reservoir computing to implement these connectomes as artificial neural networks. We then train these neuromorphic networks to learn a cognitive task. We show that biologically realistic neural architectures perform optimally when they display critical dynamics. We find that performance is driven by network topology, and that the modular organization of large-scale functional systems is computationally relevant. Throughout, we observe a prominent interaction between network structure and dynamics, such that the same underlying architecture can support a wide range of learning capacities across dynamical regimes. This work opens new opportunities to discover how the network organization of the brain optimizes cognitive capacity, conceptually bridging neuroscience and artificial intelligence.

neuroscience↗

bioRxiv2020

ADAM protease inhibition overcomes resistance of breast cancer stem-like cells to γδ T cell immunotherapy

Breast cancer stem cells (BCSC) are highly resistant to current therapies, and are responsible for metastatic burden and relapse. Gamma delta T cells ({gamma}{delta}Tc) are immunosurveillance cells with tremendous anti-tumoral activity, and a growing number of clinical trials have confirmed the safety of {gamma}{delta}Tc immunotherapy for various malignancies. Herein, we demonstrate that {gamma}{delta}Tc can kill BCSC, but to a lesser extent than non-cancer stem cells (NSC). Immune evasion was orchestrated by several mechanisms. The BCSC secretome rendered {gamma}{delta}Tc hypo-responsive by reducing proliferation, cytotoxicity and IFN-{gamma} production, while increasing expression of co-inhibitory receptors on {gamma}{delta}Tc. BCSC and target cells surviving {gamma}{delta}Tc cytotoxicity had higher PD-L1 co-inhibitory ligand expression, and blocking PD-1 on {gamma}{delta}Tc significantly overcame BCSC resistance to {gamma}{delta}Tc killing. Fas/FasL signaling was dysfunctional in BCSC due to upregulation of the anti-apoptotic protein MCL-1, which could be partially overcome using dMCL1-2, an MCL-1 degrader. Moreover, the BCSC fraction shed higher levels of the NKG2D ligand MICA compared to NSC. Inhibiting MICA shedding using the ADAM inhibitor GW280264X overcame BCSC resistance to {gamma}{delta}Tc killing, rendering BCSC as sensitive to {gamma}{delta}Tc cytotoxicity as NSC. Collectively, our data unravel multiple mechanisms exploited by BCSC to evade {gamma}{delta}Tc killing, which may also come into play in BCSC resistance to other cytotoxic lymphocytes. Developing strategies to overcome this resistance will increase the efficacy of cancer immunotherapy and lead to improved outcomes for cancer patients. One Sentence SummaryBreast cancer stem-like cells are resistant to {gamma}{delta} T cell targeting, which can be overcome by inhibiting ADAM proteases that facilitate MICA/B shedding.

immunology↗

bioRxiv2019

Drivers of phyllosphere microbial functional diversity in a neotropical forest

BackgroundThe phyllosphere is an important microbial habitat but our understanding of how plant hosts drive the composition of their associated leaf microbial communities and whether taxonomic associations between plants and phyllosphere microbes represent adaptive matching remains limited. In this study we quantify bacterial functional diversity in the phyllosphere of 17 tree species in a diverse neotropical forest using metagenomic shotgun sequencing. We ask how hosts drive the functional composition of phyllosphere communities and their turnover across tree species, using host functional traits and phylogeny. We compare functional predictions inferred from 16S gene sequencing with functions estimated from metagenomic shotgun sequencing. ResultsNeotropical tree phyllosphere communities are dominated by functions related to the metabolism of carbohydrates, amino acids and energy acquisition, along with environmental signalling pathways involved in membrane transport. While most functional variation was observed within communities, there is non-random assembly of microbial functions across host species possessing different leaf traits. Metabolic functions related to biosynthesis and degradation of secondary compounds, along with signal transduction and cell-cell adhesion were particularly important in driving the match between microbial functions and host traits. These microbial functions were also evolutionarily conserved across the host phylogeny. Functional predictions inferred from 16S gene sequences were weakly correlated with functional annotations from the same samples through metagenomic shotgun sequencing, especially for finer-scale functional annotations. ConclusionsFunctional profiling based on metagenomic shotgun sequencing offers evidence for the presence of a core functional microbiome across phyllosphere communities of neotropical trees. While functional turnover across phyllosphere communities is relatively small, the association between microbial functions and leaf trait gradients among host species supports a significant role for plant hosts as selective filters on phyllosphere community assembly. This interpretation is supported by the presence of phylogenetic signal for the microbial traits driving inter-community variation across the host phylogeny. Our comparison of functional annotations derived from 16S genes versus metagenomic shotgun sequencing suggests caution in using functions inferred from 16S genes for studying ecological dynamics in phyllosphere communities. Taken together, our results suggest that there is adaptive matching between phyllosphere microbes and their plant hosts.

ecology↗

bioRxiv2019

Learning to evoke complex motor outputs with spatiotemporal neurostimulation using a hierarchical and adaptive optimization algorithm.

The development of neurostimulation techniques to evoke motor patterns is an active area of research. It serves as a crucial experimental tool to probe computation in neural circuits, and it has applications in neuroprostheses used to aid recovery of function after stroke or injury. There are two important challenges when designing algorithms to unveil and control neurostimulation-to-motor mappings, thereby linking spatiotemporal patterns of neural stimulation to muscle activation: (1) the exploration of motor maps needs to be fast and efficient (exhaustive search is to be avoided for clinical and experimental reasons) (2) online learning needs to be flexible enough to track ongoing changes in these maps. We propose a stimulation search algorithm to address these issues, and demonstrate its efficacy with experiments in non-human primate models. Our solution is a novel iterative process using Bayesian Optimization via Gaussian Processes on increasingly complex signal spaces. We show that our algorithm can successfully and rapidly learn mappings between complex stimulation patterns and evoked muscle activation patterns, where standard approaches fail. Importantly, we uncover nonlinear circuit-level computations in M1 that would not have been possible to identify using conventional mapping techniques.

neuroscience↗

bioRxiv2019

Dynamic compression and expansion in a classifying recurrent network

Recordings of neural circuits in the brain reveal extraordinary dynamical richness and high variability. At the same time, dimensionality reduction techniques generally uncover low-dimensional structures underlying these dynamics when tasks are performed. In general, it is still an open question what determines the dimensionality of activity in neural circuits, and what the functional role of this dimensionality in task learning is. In this work we probe these issues using a recurrent artificial neural network (RNN) model trained by stochastic gradient descent to discriminate inputs. The RNN family of models has recently shown promise in revealing principles behind brain function. Through simulations and mathematical analysis, we show how the dimensionality of RNN activity depends on the task parameters and evolves over time and over stages of learning. We find that common solutions produced by the network naturally compress dimensionality, while variability-inducing chaos can expand it. We show how chaotic networks balance these two factors to solve the discrimination task with high accuracy and good generalization properties. These findings shed light on mechanisms by which artificial neural networks solve tasks while forming compact representations that may generalize well.

neuroscience↗

bioRxiv2019

Cortical network mechanisms of anodal and cathodal transcranial direct current stimulation in awake primates

Transcranial direct current stimulation (tDCS) is a non-invasive neuromodulation technique that is widely used to stimulate the sensorimotor cortex, and yet the mechanism by which it influences the natural activity of cortical networks is still under debate. Here, we characterize the effects of anodal and cathodal tDCS on underlying neurons in active macaque sensorimotor cortex across a range of doses. We find changes in spike rates that are sensitive to both current intensity and polarity, behavioral state, and that are cell-type specific. At high currents, effects persist after the offset of stimulation, and the spatiotemporal activity associated with motor activity of the contralateral limb, measured by dynamics of neural ensembles, are altered. These data suggest that tDCS induces reproducible and noticeable changes in cortical neuron activity and support the theory that it affects brain activity through a combination of single neuron polarization and network interactions.

neuroscience↗

bioRxiv2018

Signatures and mechanisms of low-dimensional neural predictive manifolds

Artificial neural networks have recently achieved many successes in solving sequential processing and planning tasks. Their success is often ascribed to the emergence of the tasks low-dimensional latent structure in the network activity - i.e., in the learned neural representations. Here, we investigate the hypothesis that a means for generating representations with easily accessed low-dimensional latent structure, possibly reflecting an underlying semantic organization, is through learning to predict observations about the world. Specifically, we ask whether and when network mechanisms for sensory prediction coincide with those for extracting the underlying latent variables. Using a recurrent neural network model trained to predict a sequence of observations we show that network dynamics exhibit low-dimensional but nonlinearly transformed representations of sensory inputs that map the latent structure of the sensory environment. We quantify these results using nonlinear measures of intrinsic dimensionality and linear decodability of latent variables, and provide mathematical arguments for why such useful predictive representations emerge. We focus throughout on how our results can aid the analysis and interpretation of experimental data.

neuroscience↗