Functional effects of mutations in proteins can be predicted and interpreted by guided selection of sequence covariation information.

Predicting the effects of one or more mutations to the in vivo or in vitro properties of a wild-type protein is a major computational challenge, due to the presence of epistasis, that is, of interactions between amino acids in the sequence. We introduce a computationally efficient procedure to build minimal epistatic models to predict mutational effects by combining evolutionary (homologous sequence) and few mutational-scan data. Mutagenesis measurements guide the selection of links in a sparse graphical model, while the parameters on the nodes and the edges are inferred from sequence data. We show, on 10 mutational scans, that our pipeline exhibits performances comparable to state-of-the-art deep networks trained on many more data, while requiring much less parameters and being hence more interpretable. In particular, the identified interactions adapt to the wild-type protein and to the fitness or biochemical property experimentally measured, mostly focus on key functional sites, and are not necessarily related to structural contacts. Therefore, our method is able to extract information relevant for one mutational experiment from homologous sequence data reflecting the multitude of structural and functional constraints acting on proteins throughout evolution.

Infer global, predict local: Quantity-relevance trade-off in protein fitness predictions from sequence data.

Predicting the effects of mutations on protein function is an important issue in evolutionary biology and biomedical applications. Computational approaches, ranging from graphical models to deep-learning architectures, can capture the statistical properties of sequence data and predict the outcome of high-throughput mutagenesis experiments probing the fitness landscape around some wild-type protein. However, how the complexity of the models and the characteristics of the data combine to determine the predictive performance remains unclear. Here, based on a theoretical analysis of the prediction error, we propose descriptors of the sequence data, characterizing their quantity and relevance relative to the model. Our theoretical framework identifies a trade-off between these two quantities, and determines the optimal subset of data for the prediction task, showing that simple models can outperform complex ones when inferred from adequately-selected sequences. We also show how repeated subsampling of the sequence data is informative about how much epistasis in the fitness landscape is not captured by the computational model. Our approach is illustrated on several protein families, as well as on in silico solvable protein models.

Integration and multiplexing of positional and contextual information by the hippocampal network.

The hippocampus is known to store cognitive representations, or maps, that encode both positional and contextual information, critical for episodic memories and functional behavior. How path integration and contextual cues are dynamically combined and processed by the hippocampus to maintain these representations accurate over time remains unclear. To answer this question, we propose a two-way data analysis and modeling approach to CA3 multi-electrode recordings of a moving rat submitted to rapid changes of contextual (light) cues, triggering back-and-forth instabitilies between two cognitive representations ("teleportation" experiment of Jezek et al). We develop a dual neural activity decoder, capable of independently identifying the recalled cognitive map at high temporal resolution (comparable to theta cycle) and the position of the rodent given a map. Remarkably, position can be reconstructed at any time with an accuracy comparable to fixed-context periods, even during highly unstable periods. These findings provide evidence for the capability of the hippocampal neural activity to maintain an accurate encoding of spatial and contextual variables, while one of these variables undergoes rapid changes independently of the other. To explain this result we introduce an attractor neural network model for the hippocampal activity that process inputs from external cues and the path integrator. Our model allows us to make predictions on the frequency of the cognitive map instability, its duration, and the detailed nature of the place-cell population activity, which are validated by a further analysis of the data. Our work therefore sheds light on the mechanisms by which the hippocampal network achieves and updates multi-dimensional neural representations from various input streams.

Functional connectivity models for decoding of spatial representations from hippocampal CA1 recordings.

Hippocampus stores spatial representations, or maps, which are recalled each time a subject is placed in the corresponding environment. Across different environments of similar geometry, these representations show strong orthogonality in CA3 of hippocampus, whereas in the CA1 subfield a considerable overlap between the maps can be seen. The lower orthogonality decreases reliability of various decoders developed in an attempt to identify which of the stored maps is active at the moment. Especially, the problem with decoding emerges with a need to analyze data at high temporal resolution. Here, we introduce a functional-connectivity-based decoder, which accounts for the pairwise correlations between the spiking activities of neurons in each map and does not require any positional information, i.e. any knowledge about place fields. We first show, on recordings of hippocampal activity in constant environmental conditions, that our decoder outperforms existing decoding methods in CA1. Our decoder is then applied to data from teleportation experiments, in which an instantaneous switch between the environment identity triggers a recall of the corresponding spatial representation . We test the sensitivity of our approach on the transition dynamics between the respective memory states (maps). We find that the rate of spontaneous state shifts (flickering) after a teleportation event is increased not only within the first few seconds as already reported, but this instability is sustained across much longer (> 1 min.) periods.

Tuned geometries of hippocampal representations meet the computational demands of social memory.

Social memory consists of two processes: the detection of familiar compared with novel conspecifics and the detailed recollection of past social episodes. We investigated the neural bases for these processes using calcium imaging of dorsal CA2 hippocampal pyramidal neurons, known to be important for social memory, during social/spatial encounters with novel conspecifics and familiar littermates. Whereas novel individuals were represented in a low-dimensional geometry that allows for generalization of social identity across different spatial locations and of location across different identities, littermates were represented in a higher-dimensional geometry that supports high-capacity memory storage. Moreover, familiarity was represented in an abstract format, independent of individual identity. The degree to which familiarity increased the dimensionality of CA2 representations for individual mice predicted their performance in a social novelty recognition memory test. Thus, by tuning the geometry of structured neural activity, CA2 is able to meet the demands of distinct social memory processes.

The representational geometry of emotional states in basolateral amygdala.

Sensory stimuli associated with aversive outcomes can cause multiple behavioral responses related to an animal's evolving emotional state. We employed chemogenetic inactivation and two-photon imaging to reveal how the basolateral amygdala (BLA) mediates these state changes. Mice were presented stimuli in a virtual burrow, causing two responses reflecting fear and flight to safety: tremble and ingress into the burrow. Inactivation eliminated differential tremble and ingress to aversive and neutral stimuli without eliminating responses themselves. Multiple variables, including stimulus valence and identity, and being in the tremble or ingressed state, typically modulated each neuron's activity (mixed-selectivity). BLA neural ensembles represented these variables even after neurons with apparent specialized selectivity were eliminated from analyses. Thus, implementing different readouts of BLA ensembles comprised of mixed-selectivity neurons can identify distinct emotional states defined by responses, like tremble for fear and ingress for safety. This mechanism relies on BLA's representational geometry, not its circuit specialization.

Adolescent thalamic inhibition leads to long-lasting impairments in prefrontal cortex function.

Impaired cortical maturation is a postulated mechanism in the etiology of neurodevelopmental disorders, including schizophrenia. In the sensory cortex, activity relayed by the thalamus during a postnatal sensitive period is essential for proper cortical maturation. Whether thalamic activity also shapes prefrontal cortical maturation is unknown. We show that inhibiting the mediodorsal and midline thalamus in mice during adolescence leads to a long-lasting decrease in thalamo-prefrontal projection density and reduced excitatory drive to prefrontal neurons. It also caused prefrontal-dependent cognitive deficits during adulthood associated with disrupted prefrontal cross-correlations and task outcome encoding. Thalamic inhibition during adulthood had no long-lasting consequences. Exciting the thalamus in adulthood during a cognitive task rescued prefrontal cross-correlations, task outcome encoding and cognitive deficits. These data point to adolescence as a sensitive window of thalamocortical circuit maturation. Furthermore, by supporting prefrontal network activity, boosting thalamic activity provides a potential therapeutic strategy for rescuing cognitive deficits in neurodevelopmental disorders.

A synaptic signal for novelty processing in the hippocampus.

Episodic memory formation and recall are complementary processes that rely on opposing neuronal computations in the hippocampus. How this conflict is resolved in hippocampal circuits is unclear. To address this question, we obtained in vivo whole-cell patch-clamp recordings from dentate gyrus granule cells in head-fixed mice trained to explore and distinguish between familiar and novel virtual environments. We find that granule cells consistently show a small transient depolarisation upon transition to a novel environment. This synaptic novelty signal is sensitive to local application of atropine, indicating that it depends on metabotropic acetylcholine receptors. A computational model suggests that the synaptic response to novelty may bias granule cell population activity, which can drive downstream attractor networks to a new state, favouring the switch from recall to new memory formation when faced with novelty. Such a novelty-driven switch may enable flexible encoding of new memories while preserving stable retrieval of familiar ones.

Differential Relation between Neuronal and Behavioral Discrimination during Hippocampal Memory Encoding.

How are distinct memories formed and used for behavior? To relate neuronal and behavioral discrimination during memory formation, we use in vivo 2-photon Ca2+ imaging and whole-cell recordings from hippocampal subregions in head-fixed mice performing a spatial virtual reality task. We find that subthreshold activity as well as population codes of dentate gyrus neurons robustly discriminate across different spatial environments, whereas neuronal remapping in CA1 depends on the degree of difference between visual cues. Moreover, neuronal discrimination in CA1, but not in the dentate gyrus, reflects behavioral performance. Our results suggest that CA1 weights the decorrelated information from the dentate gyrus according to its relevance, producing a map of memory representations that can be used by downstream circuits to guide learning and behavior.