Fear learning induces synaptic potentiation between engram neurons in the rat lateral amygdala. This study by Marios Abatis et al. demonstrates how fear conditioning strengthens synaptic connections between engram cells in the lateral amygdala, revealed through optogenetic identification of neuronal ensembles and electrophysiological measurements. The work provides crucial insights into memory formation mechanisms at the synaptic level, with implications for understanding anxiety disorders and developing targeted interventions. Presented by Dr. Kenneth Hayworth, this journal club will explore the paper's methodology linking engram cell reactivation with synaptic plasticity measurements, and discuss implications for memory decoding research.

Early in development the nervous system is constructed with far too many neurons that make an excessive number of synaptic connections.  Later, a wave of neuronal remodeling radically reshapes nervous system wiring and cell numbers through the selective elimination of excess synapses, axons and dendrites, and even whole neurons.  This remodeling is widespread across the nervous system, extensive in terms of how much individual brain regions can change (e.g. in some cases 50% of neurons integrated into a brain circuit are eliminated), and thought to be essential for optimizing nervous system function.  Perturbations of neuronal remodeling are thought to underlie devastating neurodevelopmental disorders including autism spectrum disorder, schizophrenia, and epilepsy.  This seminar will discuss our efforts to use the relatively simple nervous system of Drosophila to understand the mechanistic basis by which cells, or parts of cells, are specified for removal and eliminated from the nervous system.

Discriminating distinct objects and concepts from sensory stimuli is essential for survival. Our brains accomplish this feat by forming meaningful internal representations in deep sensory networks with plastic synaptic connections. Experience-dependent plasticity presumably exploits temporal contingencies between sensory inputs to build these internal representations. However, the precise mechanisms underlying plasticity remain elusive. We derive a local synaptic plasticity model inspired by self-supervised machine learning techniques that shares a deep conceptual connection to Bienenstock-Cooper-Munro (BCM) theory and is consistent with experimentally observed plasticity rules. We show that our plasticity model yields disentangled object representations in deep neural networks without the need for supervision and implausible negative examples. In response to altered visual experience, our model qualitatively captures neuronal selectivity changes observed in the monkey inferotemporal cortex in-vivo. Our work suggests a plausible learning rule to drive learning in sensory networks while making concrete testable predictions.

Microglia, traditionally classified as immune-responsive, adjust synaptic connections during development and disease. However, their role in the adult nervous system has been mostly diminished to an observer. In my research group, we are interested in how microglia are involved in establishing and maintaining accurate neuronal circuit function in the retina and in the visual cortex. In my talk, I will introduce our strategies how to decipher the microglia’s functional identity and how this information guided us to microglia enabled extracellular matrix remodeling and reinstatment of juvenile-like plasticity in the adult brain.

Simply put, the goal of my research is to describe the neuronal circuitry of the retina. The organization of the mammalian retina is certainly complex but it is not chaotic. Although there are many cell types, most adhere to a relatively constant morphology and they are distributed in non-random mosaics. Furthermore, each cell type ramifies at a characteristic depth in the retina and makes a stereotyped set of synaptic connections. In other words, these neurons form a series of local circuits across the retina. The next step is to identify the simplest and commonest of these repeating neural circuits. They are the building blocks of retinal function. If we think of it in this way, the retina is a fabulous model for the rest of the CNS. We are interested in identifying specific circuits and cell types that support the different functions of the retina. For example, there appear to be specific pathways for rod and cone mediated vision. Rods are used under low light conditions and rod circuitry is specialized for high sensitivity when photons are scarce (when you’re out camping, starlight). The hallmark of the rod-mediated system is monochromatic vision. In contrast, the cone circuits are specialized for high acuity and color vision under relatively bright or daylight conditions. Individual neurons may be filled with fluorescent dyes under visual control. This is achieved by impaling the cell with a glass microelectrode using a 3D micromanipulator. We are also interested in the diffusion of dye through coupled neuronal networks in the retina. The dye filled cells are also combined with antibody labeling to reveal neuronal connections and circuits. This triple-labeled material may be viewed and reconstructed in 3 dimensions by multi-channel confocal microscopy. We have our own confocal microscope facility in the department and timeslots are available to students in my lab.

In this lecture, Dr Stevens will discuss recent work that implicates brain immune cells, called microglia, in sculpting of synaptic connections during development and their relevance to autism, schizophrenia and other brain disorders. Her recent work revealed a key role for microglia and a group of immune related molecules called complement in normal developmental synaptic pruning, a normal process required to establish precise brain wiring. Emerging evidence suggests aberrant regulation of this pruning pathway may contribute to synaptic and cognitive dysfunction in a host of brain disorders, including schizophrenia. Recent research has revealed that a person’s risk of schizophrenia is increased if they inherit specific variants in complement C4, gene plays a well-known role in the immune system but also helps sculpt developing synapses in the mouse visual system (Sekar et al., 2016). Together these findings may help explain known features of schizophrenia, including reduced numbers of synapses in key cortical regions and an adolescent age of onset that corresponds with developmentally timed waves of synaptic pruning in these regions. Stevens will discuss this and ongoing work to understand the mechanisms by which complement and microglia prune specific synapses in the brain. A deeper understanding of how these immune mechanisms mediate synaptic pruning may provide novel insight into how to protect synapses in autism and other brain disorders, including Alzheimer’s and Huntington’s Disease.

I am working on the vertebrate retina, with a main focus on the mouse and bird retina. Currently my work is focused on three major topics: Functional and molecular analysis of electrical synapses in the retina Circuitry and functional role of retinal interneurons: horizontal cells Circuitry for light-dependent magnetoreception in the bird retina Electrical synapses Electrical synapses (gap junctions) permit fast transmission of electrical signals and passage of metabolites by means of channels, which directly connect the cytoplasm of adjoining cells. A functional gap junction channel consists of two hemichannels (one provided by each of the cells), each comprised of a set of six protein subunits, termed connexins. These building blocks exist in a variety of different subtypes, and the connexin composition determines permeability and gating properties of a gap junction channel, thereby enabling electrical synapses to meet a diversity of physiological requirements. In the retina, various connexins are expressed in different cell types. We study the cellular distribution of different connexins as well as the modulation induced by transmitter action or change of ambient light levels, which leads to altered electrical coupling properties. We are also interested in exploiting them as therapeutic avenue for retinal degeneration diseases. Horizontal cells Horizontal cells receive excitatory input from photoreceptors and provide feedback inhibition to photoreceptors and feedforward inhibition to bipolar cells. Because of strong electrical coupling horizontal cells integrate the photoreceptor input over a wide area and are thought to contribute to the antagonistic organization of bipolar cell and ganglion cell receptive fields and to tune the photoreceptor–bipolar cell synapse with respect to the ambient light conditions. However, the extent to which this influence shapes retinal output is unclear, and we aim to elucidate the functional importance of horizontal cells for retinal signal processing by studying various transgenic mouse models. Retinal circuitry for light-dependent magnetoreception in the bird We are studying which neuronal cell types and pathways in the bird retina are involved in the processing of magnetic signals. Likely, magnetic information is detected in cryptochrome-expressing photoreceptors and leaves the retina through ganglion cell axons that project via the thalamofugal pathway to Cluster N, a part of the visual wulst essential for the avian magnetic compass. Thus, we aim to elucidate the synaptic connections and retinal signaling pathways from putatively magnetosensitive photoreceptors to thalamus-projecting ganglion cells in migratory birds using neuroanatomical and electrophysiological techniques.

The regions of the insect brain devoted to spatial navigation are beautifully orderly, with a remarkably precise pattern of synaptic connections. Thus, we can learn much about the neural mechanisms of spatial navigation by targeting identifiable neurons in these networks for in vivo patch clamp recording and calcium imaging. Our lab has recently discovered that the "compass system" in the Drosophila brain is anchored to not only visual landmarks, but also the prevailing wind direction. Moreover, we found that the compass system can re-learn the relationship between these external sensory cues and internal self-motion cues, via rapid associative synaptic plasticity. Postsynaptic to compass neurons, we found neurons that conjunctively encode heading direction and body-centric translational velocity. We then showed how this representation of travel velocity is transformed from body- to world-centric coordinates at the subsequent layer of the network, two synapses downstream from compass neurons. By integrating this world-centric vector-velocity representation over time, it should be possible for the brain to form a stored representation of the body's path through the environment.

The Stabilized Supralinear Network is a model of recurrently connected excitatory (E) and inhibitory (I) neurons with a supralinear input-output relation. It can explain cortical computations such as response normalization and inhibitory stabilization. However, the network's connectivity is designed by hand, based on experimental measurements. How the recurrent synaptic weights can be learned from the sensory input statistics in a biologically plausible way is unknown. Earlier theoretical work on plasticity focused on single neurons and the balance of excitation and inhibition but did not consider the simultaneous plasticity of recurrent synapses and the formation of receptive fields. Here we present a recurrent E-I network model where all synaptic connections are simultaneously plastic, and E neurons self-stabilize by recruiting co-tuned inhibition. Motivated by experimental results, we employ a local Hebbian plasticity rule with multiplicative normalization for E and I synapses. We develop a theoretical framework that explains how plasticity enables inhibition balanced excitatory receptive fields that match experimental results. We show analytically that sufficiently strong inhibition allows neurons' receptive fields to decorrelate and distribute themselves across the stimulus space. For strong recurrent excitation, the network becomes stabilized by inhibition, which prevents unconstrained self-excitation. In this regime, external inputs integrate sublinearly. As in the Stabilized Supralinear Network, this results in response normalization and winner-takes-all dynamics: when two competing stimuli are presented, the network response is dominated by the stronger stimulus while the weaker stimulus is suppressed. In summary, we present a biologically plausible theoretical framework to model plasticity in fully plastic recurrent E-I networks. While the connectivity is derived from the sensory input statistics, the circuit performs meaningful computations. Our work provides a mathematical framework of plasticity in recurrent networks, which has previously only been studied numerically and can serve as the basis for a new generation of brain-inspired unsupervised machine learning algorithms.

Biological neural networks can represent information in the collective action potential discharge of neurons, and store that information amongst the synaptic connections between the neurons that both comprise the network and govern its function. The strength and organization of synaptic connections adjust during learning, but many cognitive neural systems are multifunctional, making it unclear how continuous activity alternates between the transient and discrete cognitive functions like encoding current information and recollecting past information, without changing the connections amongst the neurons. This lecture will first summarize our investigations of the molecular and biochemical mechanisms that change synaptic function to persistently store spatial memory in the rodent hippocampus. I will then report on how entorhinal cortex-hippocampus circuit function changes during cognitive training that creates memory, as well as learning to learn in mice. I will then describe how the hippocampus system operates like a competitive winner-take-all network, that, based on the dominance of its current inputs, self organizes into either the encoding or recollection information processing modes. We find no evidence that distinct cells are dedicated to those two distinct functions, rather activation of the hippocampus information processing mode is controlled by a subset of dentate spike events within the network of learning-modified, entorhinal-hippocampus excitatory and inhibitory synapses.

Biological neural networks can represent information in the collective action potential discharge of neurons, and store that information amongst the synaptic connections between the neurons that both comprise the network and govern its function. The strength and organization of synaptic connections adjust during learning, but many cognitive neural systems are multifunctional, making it unclear how continuous activity alternates between the transient and discrete cognitive functions like encoding current information and recollecting past information, without changing the connections amongst the neurons. This lecture will first summarize our investigations of the molecular and biochemical mechanisms that change synaptic function to persistently store spatial memory in the rodent hippocampus. I will then report on how entorhinal cortex-hippocampus circuit function changes during cognitive training that creates memory, as well as learning to learn in mice. I will then describe how the hippocampus system operates like a competitive winner-take-all network, that, based on the dominance of its current inputs, self organizes into either the encoding or recollection information processing modes. We find no evidence that distinct cells are dedicated to those two distinct functions, rather activation of the hippocampus information processing mode is controlled by a subset of dentate spike events within the network of learning-modified, entorhinal-hippocampus excitatory and inhibitory synapses.

The synapse is the core component of the nervous system and synapse formation is the critical step in the assembly of neuronal circuits. The assembly and maturation of synapses requires the contribution of secreted and membrane-associated proteins, with neuronal activity playing crucial roles in regulating synaptic strength, neuronal membrane properties, and neural circuit refinement. The molecular mechanisms of synapse assembly and refinement have been so far largely examined on a gene-by-gene basis and with a perspective fully centered on neuronal cells. However, in the last years, the involvement of non-neuronal cells has emerged. Among these, microglia, the resident immune cells of the central nervous system, have been shown to play a key role in synapse formation and elimination. Contacts of microglia with dendrites in the somatosensory cortex were found to induce filopodia and dendritic spines via Ca2+ and actin-dependent processes, while microglia-derived BDNF was shown to promote learning-dependent synapse formation. Microglia is also recognized to have a central role in the widespread elimination (or pruning) of exuberant synaptic connections during development. Clarifying the processes by which microglia control synapse homeostasis is essential to advance our current understanding of brain functions. Clear answers to these questions will have important implications for our understanding of brain diseases, as the fact that many psychiatric and neurological disorders are synaptopathies (i.e. diseases of the synapse) is now widely recognized. In the last years, my group has identified TREM2, an innate immune receptor with phagocytic and antiinflammatory properties expressed in brain exclusively by microglia, as essential for microglia-mediated synaptic refinement during the early stages of brain development. The talk will describe the role of TREM2 in synapse elimination and introduce the molecular actors involved. I will also describe additional pathways by which the immune system may affect the formation and homeostasis of synaptic contacts.

During learning, populations of neurons alter their connectivity and activity patterns, enabling the brain to construct a model of the external world. Conventional wisdom holds that the durability of a such a model is reflected in the stability of neural responses and the stability of synaptic connections that form memory engrams. However, recent experimental findings have challenged this idea, revealing that neural population activity in circuits involved in sensory perception, motor planning and spatial memory continually change over time during familiar behavioural tasks. This continual change suggests significant redundancy in neural representations, with many circuit configurations providing equivalent function. I will describe recent work that explores the consequences of such redundancy for learning and for task representation. Despite large changes in neural activity, we find cortical responses in sensorimotor tasks admit a relatively stable readout at the population level. Furthermore, we find that redundancy in circuit connectivity can make a task easier to learn and compensate for deficiencies in biological learning rules. Finally, if neuronal connections are subject to an unavoidable level of turnover, the level of plasticity required to optimally maintain a memory is generally lower than the total change due to turnover itself, predicting continual reconfiguration of an engram.

The structural connectivity matrix of synaptic weights between neurons is a critical determinant of overall network function. However, quantitative links between neural network structure and function are complex and subtle. For example, many networks can give rise to similar functional responses, and the same network can function differently depending on context. Whether certain patterns of synaptic connectivity are required to generate specific network-level computations is largely unknown. Here we introduce a geometric framework for identifying synaptic connections required by steady-state responses in recurrent networks of rectified-linear neurons. Assuming that the number of specified response patterns does not exceed the number of input synapses, we analytically calculate all feedforward and recurrent connectivity matrices that can generate the specified responses from the network inputs. We then use this analytical characterization to rigorously analyze the solution space geometry and derive certainty conditions guaranteeing a non-zero synapse between neurons.

The establishment of synaptic connections is essential for normal brain function, yet the molecular mechanisms responsible for the precise connectivity of specific neural circuits remain largely unknown. Previous work has shown that the assembly of cortical circuits requires specific functions of molecular signalling complexes at different classes of synapses. In this talk, I will describe the molecular logic through which specific pyramidal cell-interneuron circuits are established in the cerebral cortex of the mouse, and how alterations in some of these connectivity motifs might be liked to disease.

The long-term goal of my research is to study the mammalian retina as a model for the central nervous system (CNS) -- to understand how it functions in physiological conditions, how it is formed, how it breaks down in pathological conditions, and how it can be repaired. I have focused on two research themes: 1) Photoreceptor structure, synapse, circuits, and development, 2) Hibernation and metabolic adaptations in the retina and beyond. As the first neuron of the visual system, photoreceptors are vital for photoreception and transmission of visual signals. I am particularly interested in cone photoreceptors, as they mediate our daylight vision with high resolution color information. Diseases affecting cone photoreceptors compromise visual functions in the central macular area of the human retina and are thus most detrimental to our vision. However, because cones are much less abundant compared to rods in most mammals, they are less well studied. We have used the ground squirrel (GS) as a model system to study cone vision, taking advantage of their unique cone-dominant retina. In particular, we have focused on short-wavelength sensitive cones (S-cones), which are not only essential for color vision, but are also an important origin of signals for biological rhythm, mood and cognitive functions, and the growth of the eye during development. We are studying critical cone synaptic structures – synaptic ribbons, the synaptic connections of S-cones, and the development of S-cones with regard to their specific connections. These works will provide knowledge of normal retinal development and function, which can also be extended to the rest of CNS; for example, the mechanisms of synaptic targeting during development. In addition, such knowledge will benefit the development of optimal therapeutic strategies for regeneration and repair in cases of retinal degenerative disease. Many neurodegenerative diseases, including retinal diseases, are rooted in metabolic stress in neurons and/or glial cells. Using the same GS model, we aim to learn from this hibernating mammal, which possesses an amazing capability to adapt to the extreme metabolic conditions during hibernation. By exploring the mechanisms of such adaptation, we hope to discover novel therapeutic tactics for neurodegenerative diseases.

Starting with the work of Hodgkin and Huxley in the 1950s, we now have a fairly good understanding of how the spiking activity of neurons can be modelled mathematically. For cortical circuits the understanding is much more limited. Most network studies have considered stylized models with a single or a handful of neuronal populations consisting of identical neurons with statistically identical connection properties. However, real cortical networks have heterogeneous neural populations and much more structured synaptic connections. Unlike typical simplified cortical network models, real networks are also “multipurpose” in that they perform multiple functions. Historically the lack of computational resources has hampered the mathematical exploration of cortical networks. With the advent of modern supercomputers, however, simulations of networks comprising hundreds of thousands biologically detailed neurons are becoming feasible (Einevoll et al, Neuron, 2019). Further, a large-scale biologically network model of the mouse primary visual cortex comprising 230.000 neurons has recently been developed at the Allen Institute for Brain Science (Billeh et al, Neuron, 2020). Using this model as a starting point, I will discuss how we can move towards multipurpose models that incorporate the true biological complexity of cortical circuits and faithfully reproduce multiple experimental observables such as spiking activity, local field potentials or two-photon calcium imaging signals. Further, I will discuss how such validated comprehensive network models can be used to gain insights into the functioning of cortical circuits.

The concept of a dynamic excitation / inhibition ratio, that can shape information flow in cortical circuits during complex behavioural tasks due to circuit disinhibition, has recently arisen as an important and conserved processing motif. It has been also recognized that, in cortical circuits, a subpopulation of GABAergic cells that express vasoactive intestinal polypeptide (VIP) innervates selectively inhibitory interneurons, providing for circuit disinhibition as a possible outcome, depending on the network state and behavioural context. In this talk, I will highlight the latest discoveries on the dynamic organization of hippocampal disinhibitory circuits with a focus on VIP-expressing interneurons. I will discuss the neuron types that can be involved in disinhibition and their local circuit and long-range synaptic connections. I will also discuss some recent findings on how hippocampal VIP circuits may coordinate spatial learning.