Synaptic turnover promotes efficient learning in bio-realistic spiking neural networks.

While artificial machine learning systems achieve superhuman performance in specific tasks such as language processing, image and video recognition, they do so use extremely large datasets and huge amounts of power. On the other hand, the brain remains superior in several cognitively challenging tasks while operating with the energy of a small lightbulb. We use a biologically constrained spiking neural network model to explore how the neural tissue achieves such high efficiency and assess its learning capacity on discrimination tasks. We found that synaptic turnover, a form of structural plasticity, which is the ability of the brain to form and eliminate synapses continuously, increases both the speed and the performance of our network on all tasks tested. Moreover, it allows accurate learning using a smaller number of examples. Importantly, these improvements are most significant under conditions of resource scarcity, such as when the number of trainable parameters is halved and when the task difficulty is increased. Our findings provide new insights into the mechanisms that underlie efficient learning in the brain and can inspire the development of more efficient and flexible machine learning algorithms.

Lateral entorhinal cortex inputs modulate hippocampal dendritic excitability by recruiting a local disinhibitory microcircuit.

The lateral entorhinal cortex (LEC) provides multisensory information to the hippocampus, directly to the distal dendrites of CA1 pyramidal neurons. LEC neurons perform important functions for episodic memory processing, coding for contextually salient elements of an environment or experience. However, we know little about the functional circuit interactions between the LEC and the hippocampus. We combine functional circuit mapping and computational modeling to examine how long-range glutamatergic LEC projections modulate compartment-specific excitation-inhibition dynamics in hippocampal area CA1. We demonstrate that glutamatergic LEC inputs can drive local dendritic spikes in CA1 pyramidal neurons, aided by the recruitment of a disinhibitory VIP interneuron microcircuit. Our circuit mapping and modeling further reveal that LEC inputs also recruit CCK interneurons that may act as strong suppressors of dendritic spikes. These results highlight a cortically driven GABAergic microcircuit mechanism that gates nonlinear dendritic computations, which may support compartment-specific coding of multisensory contextual features within the hippocampus.

Introducing the Dendrify framework for incorporating dendrites to spiking neural networks.

Computational modeling has been indispensable for understanding how subcellular neuronal features influence circuit processing. However, the role of dendritic computations in network-level operations remains largely unexplored. This is partly because existing tools do not allow the development of realistic and efficient network models that account for dendrites. Current spiking neural networks, although efficient, are usually quite simplistic, overlooking essential dendritic properties. Conversely, circuit models with morphologically detailed neuron models are computationally costly, thus impractical for large-network simulations. To bridge the gap between these two extremes and facilitate the adoption of dendritic features in spiking neural networks, we introduce Dendrify, an open-source Python package based on Brian 2. Dendrify, through simple commands, automatically generates reduced compartmental neuron models with simplified yet biologically relevant dendritic and synaptic integrative properties. Such models strike a good balance between flexibility, performance, and biological accuracy, allowing us to explore dendritic contributions to network-level functions while paving the way for developing more powerful neuromorphic systems.

Modeling Dendrites and Spatially-Distributed Neuronal Membrane Properties.

The first step toward understanding the brain is to learn how individual neurons process incoming signals, the vast majority of which arrive in their dendrites. Dendrites were first discovered at the beginning of the twentieth century and were characterized by great anatomical variability, both within and across species. Over the past years, a rich repertoire of active and passive dendritic mechanisms has been unveiled, which greatly influences their integrative power. Yet, our understanding of how dendrites compute remains limited, mainly because technological limitations make it difficult to record from dendrites directly and manipulate them. Computational modeling, on the other hand, is perfectly suited for this task. Biophysical models that account for the morphology as well as passive and active neuronal properties can explain a wide variety of experimental findings, shedding new light on how dendrites contribute to neuronal and circuit computations. This chapter aims to help the interested reader build biophysical models incorporating dendrites by detailing how their electrophysiological properties can be described using simple mathematical frameworks. We start by discussing the passive properties of dendrites and then give an overview of how active conductances can be incorporated, leading to realistic in silico replicas of biological neurons.

Evolutionary models of amino acid substitutions based on the tertiary structure of their neighborhoods.

Intra-protein residual vicinities depend on the involved amino acids. Energetically favorable vicinities (or interactions) have been preserved during evolution, while unfavorable vicinities have been eliminated. We describe, statistically, the interactions between amino acids using resolved protein structures. Based on the frequency of amino acid interactions, we have devised an amino acid substitution model that implements the following idea: amino acids that have similar neighbors in the protein tertiary structure can replace each other, while substitution is more difficult between amino acids that prefer different spatial neighbors. Using known tertiary structures for α-helical membrane (HM) proteins, we build evolutionary substitution matrices. We constructed maximum likelihood phylogenies using our amino acid substitution matrices and compared them to widely-used methods. Our results suggest that amino acid substitutions are associated with the spatial neighborhoods of amino acid residuals, providing, therefore, insights into the amino acid substitution process.

Drawing inspiration from biological dendrites to empower artificial neural networks.

This article highlights specific features of biological neurons and their dendritic trees, whose adoption may help advance artificial neural networks used in various machine learning applications. Advancements could take the form of increased computational capabilities and/or reduced power consumption. Proposed features include dendritic anatomy, dendritic nonlinearities, and compartmentalized plasticity rules, all of which shape learning and information processing in biological networks. We discuss the computational benefits provided by these features in biological neurons and suggest ways to adopt them in artificial neurons in order to exploit the respective benefits in machine learning.

Large-Scale 3D Two-Photon Imaging of Molecularly Identified CA1 Interneuron Dynamics in Behaving Mice.

Cortical computations are critically reliant on their local circuit, GABAergic cells. In the hippocampus, a large body of work has identified an unprecedented diversity of GABAergic interneurons with pronounced anatomical, molecular, and physiological differences. Yet little is known about the functional properties and activity dynamics of the major hippocampal interneuron classes in behaving animals. Here we use fast, targeted, three-dimensional (3D) two-photon calcium imaging coupled with immunohistochemistry-based molecular identification to retrospectively map in vivo activity onto multiple classes of interneurons in the mouse hippocampal area CA1 during head-fixed exploration and goal-directed learning. We find examples of preferential subtype recruitment with quantitative differences in response properties and feature selectivity during key behavioral tasks and states. These results provide new insights into the collective organization of local inhibitory circuits supporting navigational and mnemonic functions of the hippocampus.

Breakdown of spatial coding and interneuron synchronization in epileptic mice.

Temporal lobe epilepsy causes severe cognitive deficits, but the circuit mechanisms remain unknown. Interneuron death and reorganization during epileptogenesis may disrupt the synchrony of hippocampal inhibition. To test this, we simultaneously recorded from the CA1 and dentate gyrus in pilocarpine-treated epileptic mice with silicon probes during head-fixed virtual navigation. We found desynchronized interneuron firing between the CA1 and dentate gyrus in epileptic mice. Since hippocampal interneurons control information processing, we tested whether CA1 spatial coding was altered in this desynchronized circuit, using a novel wire-free miniscope. We found that CA1 place cells in epileptic mice were unstable and completely remapped across a week. This spatial instability emerged around 6 weeks after status epilepticus, well after the onset of chronic seizures and interneuron death. Finally, CA1 network modeling showed that desynchronized inputs can impair the precision and stability of CA1 place cells. Together, these results demonstrate that temporally precise intrahippocampal communication is critical for spatial processing.

Vasoactive Intestinal Polypeptide-Expressing Interneurons in the Hippocampus Support Goal-Oriented Spatial Learning.

Diverse computations in the neocortex are aided by specialized GABAergic interneurons (INs), which selectively target other INs. However, much less is known about how these canonical disinhibitory circuit motifs contribute to network operations supporting spatial navigation and learning in the hippocampus. Using chronic two-photon calcium imaging in mice performing random foraging or goal-oriented learning tasks, we found that vasoactive intestinal polypeptide-expressing (VIP+), disinhibitory INs in hippocampal area CA1 form functional subpopulations defined by their modulation by behavioral states and task demands. Optogenetic manipulations of VIP+ INs and computational modeling further showed that VIP+ disinhibition is necessary for goal-directed learning and related reorganization of hippocampal pyramidal cell population dynamics. Our results demonstrate that disinhibitory circuits in the hippocampus play an active role in supporting spatial learning. VIDEO ABSTRACT.

Pattern separation in the hippocampus through the eyes of computational modeling.

Pattern separation is a mnemonic process that has been extensively studied over the years. It entails the ability -of primarily hippocampal circuits- to distinguish between highly similar inputs, via generating different neuronal activity (output) patterns. The dentate gyrus (DG) in particular has long been hypothesized to implement pattern separation by detecting and storing similar inputs as distinct representations. The ways in which these distinct representations can be generated have been explored in a number of theoretical and computational modeling studies. Here, we review two categories of pattern separation models: those that address the phenomenon in an abstract mathematical fashion and those that delve into the underlying biological mechanisms by taking into account the anatomy and/or physiology of hippocampal circuits. We summarize the strategies, findings and limitations of these modeling approaches in the light of new experimental findings and propose a unifying framework whereby different network, cellular and sub-cellular mechanisms converge to a common goal: controlling sparsity, the key determinant of pattern separation in the DG.

In Vivo Imaging of Dentate Gyrus Mossy Cells in Behaving Mice.

Mossy cells in the hilus of the dentate gyrus constitute a major excitatory principal cell type in the mammalian hippocampus; however, it remains unknown how these cells behave in vivo. Here, we have used two-photon Ca2+ imaging to monitor the activity of mossy cells in awake, behaving mice. We find that mossy cells are significantly more active than dentate granule cells in vivo, exhibit spatial tuning during head-fixed spatial navigation, and undergo robust remapping of their spatial representations in response to contextual manipulation. Our results provide a functional characterization of mossy cells in the behaving animal and demonstrate their active participation in spatial coding and contextual representation.

Dendrites of dentate gyrus granule cells contribute to pattern separation by controlling sparsity.

The hippocampus plays a key role in pattern separation, the process of transforming similar incoming information to highly dissimilar, nonverlapping representations. Sparse firing granule cells (GCs) in the dentate gyrus (DG) have been proposed to undertake this computation, but little is known about which of their properties influence pattern separation. Dendritic atrophy has been reported in diseases associated with pattern separation deficits, suggesting a possible role for dendrites in this phenomenon. To investigate whether and how the dendrites of GCs contribute to pattern separation, we build a simplified, biologically relevant, computational model of the DG. Our model suggests that the presence of GC dendrites is associated with high pattern separation efficiency while their atrophy leads to increased excitability and performance impairments. These impairments can be rescued by restoring GC sparsity to control levels through various manipulations. We predict that dendrites contribute to pattern separation as a mechanism for controlling sparsity. © 2016 The Authors Hippocampus Published by Wiley Periodicals, Inc.