The Role of Synaptic Tagging and Capture for Memory Dynamics in Spiking Neural Networks

Memory serves to process and store information about experiences such that this information can be used in future situations. The transfer from transient storage into long-term memory, which retains information for hours, days, and even years, is called consolidation. In brains, information is primarily stored via alteration of synapses, so-called synaptic plasticity. While these changes are at first in a transient early phase, they can be transferred to a late phase, meaning that they become stabilized over the course of several hours. This stabilization has been explained by so-called synaptic tagging and capture (STC) mechanisms. To store and recall memory representations, emergent dynamics arise from the synaptic structure of recurrent networks of neurons. This happens through so-called cell assemblies, which feature particularly strong synapses. It has been proposed that the stabilization of such cell assemblies by STC corresponds to so-called synaptic consolidation, which is observed in humans and other animals in the first hours after acquiring a new memory. The exact connection between the physiological mechanisms of STC and memory consolidation remains, however, unclear. It is equally unknown which influence STC mechanisms exert on further cognitive functions that guide behavior. On timescales of minutes to hours (that means, the timescales of STC) such functions include memory improvement, modification of memories, interference and enhancement of similar memories, and transient priming of certain memories. Thus, diverse memory dynamics may be linked to STC, which can be investigated by employing theoretical methods based on experimental data from the neuronal and the behavioral level. In this thesis, we present a theoretical model of STC-based memory consolidation in recurrent networks of spiking neurons, which are particularly suited to reproduce biologically realistic dynamics. Furthermore, we combine the STC mechanisms with calcium dynamics, which have been found to guide the major processes of early-phase synaptic plasticity in vivo. In three included research articles as well as additional sections, we develop this model and investigate how it can account for a variety of behavioral effects. We find that the model enables the robust implementation of the cognitive memory functions mentioned above. The main steps to this are: 1. demonstrating the formation, consolidation, and improvement of memories represented by cell assemblies, 2. showing that neuromodulator-dependent STC can retroactively control whether information is stored in a temporal or rate-based neural code, and 3. examining interaction of multiple cell assemblies with transient and attractor dynamics in different organizational paradigms. In summary, we demonstrate several ways by which STC controls the late-phase synaptic structure of cell assemblies. Linking these structures to functional dynamics, we show that our STC-based model implements functionality that can be related to long-term memory. Thereby, we provide a basis for the mechanistic explanation of various neuropsychological effects.2021-09-0

Enhancing memory-related sleep spindles through learning and electrical brain stimulation

Sleep has been strongly implicated in mediating memory consolidation through hippocampal-neocortical communication. Evidence suggests offline processing of encoded information in the brain during slow wave sleep (SWS), specifically during slow oscillations and spindles. In this work, we used active exploration and learning tasks to study post-experience sleep spindle density changes in rats. Experiences lead to subsequent changes in sleep spindles, but the strength and timing of the effect was task-dependent. Brain stimulation in humans and rats have been shown to enhance memory consolidation. However, the exact stimulation parameters which lead to the strongest memory enhancement have not been fully explored. We tested the efficacy of both cortical sinusoidal direct current stimulation and intracortical pulse stimulation to enhance slow oscillations and spindle density. Pulse stimulation reliably evoked state-dependent slow oscillations and spindles during SWS with increased hippocampal ripple-spindle coupling, demonstrating potential in memory enhancement

Cognitive and Neural Map Representations in Schizophrenia

An ability to build structured cognitive maps of the world may lie at the heart of understanding cognitive features of schizophrenia. In rodents, cognitive map representations are supported by sequential hippocampal place cell reactivations during rest (offline), known as replay. These events occur in the context of local high frequency ripple oscillations, and whole-brain default mode network (DMN) activation. Genetic mouse models of schizophrenia also report replay and ripple abnormalities. Here, I investigate the behavioural and neural signatures of structured internal representations in people with a diagnosis of schizophrenia (PScz, n = 29) and matched control participants (n = 28) using magnetoencephalography (MEG). Participants were asked to infer correct sequential relationships between task pictures by applying a pre-learned task template to visual experiences containing these pictures. In Chapter 3 I show that, during a post-task rest session, controls exhibited fast spontaneous neural reactivation of task state representations that replayed inferred relationships. Replay was coincident with increased ripple power in hippocampus, which may be related to NMDAR availability (Chapter 4). PScz showed both reduced replay and augmented ripple power, convergent with genetic mouse models. These abnormalities were linked to impairments in behavioural acquisition of task structure, and to its subsequent representation in visually evoked neural responses. In Chapter 5 I explore the temporal coupling between replay onsets and DMN activation. I show an impairment in this association in PScz, which related to subsequent mnemonic maintenance of learned task structure, complementing previous reports of DMN abnormalities in the condition. Finally, in Chapter 6, using a separate verbal fluency task, I show that PScz exhibit evidence of reduced use of (semantic) associative information when sampling concepts from memory. Together, my results provide support for a hypothesis that schizophrenia is associated with abnormalities in neural and behavioural correlates of cognitive map representation

Neural replay in representation, learning and planning

Spontaneous neural activity is rarely the subject of investigation in cognitive neuroscience. This may be due to a dominant metaphor of cognition as the information processing unit, whereas internally generated thoughts are often considered as noise. Adopting a reinforcement learning (RL) framework, I consider cognition in terms of an agent trying to attain its internal goals. This framework motivated me to address in my thesis the role of spontaneous neural activity in human cognition. First, I developed a general method, called temporal delayed linear modelling (TDLM), to enable me to analyse this spontaneous activity. TDLM can be thought of as a domain general sequence detection method. It combines nonlinear classification and linear temporal modelling. This enables testing for statistical regularities in sequences of neural representations of a decoded state space. Although developed for use with human non- invasive neuroimaging data, the method can be extended to analyse rodent electrophysiological recordings. Next, I applied TDLM to study spontaneous neural activity during rest in humans. As in rodents, I found that spontaneously generated neural events tended to occur in structured sequences. These sequences are accelerated in time compared to those that related to actual experience (30 -50 ms state-to-state time lag). These sequences, termed replay, reverse their direction after reward receipt. Notably, this human replay is not a recapitulation of prior experience, but follows sequence implied by a learnt abstract structural knowledge, suggesting a factorized representation of structure and sensory information. Finally, I test the role of neural replay in model-based learning and planning in humans. Following reward receipt, I found significant backward replay of non-local experience with a 160 ms lag. This replay prioritises and facilitates the learning of action values. In a separate sequential planning task, I show these neural sequences go forward in direction, depicting the trajectory subjects about to take. The research presented in this thesis reveals a rich role of spontaneous neural activity in supporting internal computations that underpin planning and inference in human cognition

Long-term stability of the hippocampal neural code as a substrate for episodic memory

The hippocampus supports the initial formation and recall of episodic memories, as well as the consolidation of short-term into long-term memories. The ability of hippocampal neurons to rapidly change their connection strengths during learning and maintain these changes over long time-scales may provide a mechanism supporting memory. However, little evidence currently exists concerning the long-term stability of information contained in hippocampal neuronal activity, likely due to limitations in recording extracellular activity in vivo from the same neurons across days. In this thesis I employ calcium imaging in freely moving mice to longitudinally track the activity of large ensembles of hippocampal neurons. Using this technology, I explore the proposal that long-term stability of hippocampal information provides a substrate for episodic memory in three different ways. First, I tested the hypothesis that hippocampal activity should remain stable across days in the absence of learning. I found that place cells – hippocampal neurons containing information about a mouse’s position – maintain a coherent map relative to each other across long time-scales but exhibit instability in how they anchor to the external world. Furthermore, I found that coherent maps were frequently used to represent a different environment and incorporated learning via changes in a subset of neurons. Next, I examined how learning a spatial alternation task impacts neuron stability. I found that splitter neurons whose activity patterns reflected an animal’s future or past trajectory emerged relatively slowly when compared to place cells. However, splitter neurons remained more consistently active and relayed more consistent spatial information across days than did place cells, suggesting that the utility of information provided by a neuron influences its long term stability. Last, I investigated how protein synthesis, known to be necessary for long-term maintenance of changes in hippocampal neuron connection strengths and for proper memory consolidation, influences their activity patterns across days. I found that along with blocking memory consolidation, inhibiting protein synthesis induced a profound, long-lasting decrease in neuronal activity up to two days later. These results combined demonstrate the importance of rapid, lasting changes in the hippocampal neuronal code to supporting long-term memory

The malleable brain: plasticity of neural circuits and behavior: A review from students to students

One of the most intriguing features of the brain is its ability to be malleable, allowing it to adapt continually to changes in the environment. Specific neuronal activity patterns drive long-lasting increases or decreases in the strength of synaptic connections, referred to as long-term potentiation (LTP) and long-term depression (LTD) respectively. Such phenomena have been described in a variety of model organisms, which are used to study molecular, structural, and functional aspects of synaptic plasticity. This review originated from the first International Society for Neurochemistry (ISN) and Journal of Neurochemistry (JNC) Flagship School held in Alpbach, Austria (Sep 2016), and will use its curriculum and discussions as a framework to review some of the current knowledge in the field of synaptic plasticity. First, we describe the role of plasticity during development and the persistent changes of neural circuitry occurring when sensory input is altered during critical developmental stages. We then outline the signaling cascades resulting in the synthesis of new plasticity-related proteins, which ultimately enable sustained changes in synaptic strength. Going beyond the traditional understanding of synaptic plasticity conceptualized by LTP and LTD, we discuss system-wide modifications and recently unveiled homeostatic mechanisms, such as synaptic scaling. Finally, we describe the neural circuits and synaptic plasticity mechanisms driving associative memory and motor learning. Evidence summarized in this review provides a current view of synaptic plasticity in its various forms, offers new insights into the underlying mechanisms and behavioral relevance, and provides directions for future research in the field of synaptic plasticity.Fil: Schaefer, Natascha. University of Wuerzburg; AlemaniaFil: Rotermund, Carola. University of Tuebingen; AlemaniaFil: Blumrich, Eva Maria. Universitat Bremen; AlemaniaFil: Lourenco, Mychael V.. Universidade Federal do Rio de Janeiro; BrasilFil: Joshi, Pooja. Robert Debre Hospital; FranciaFil: Hegemann, Regina U.. University of Otago; Nueva ZelandaFil: Jamwal, Sumit. ISF College of Pharmacy; IndiaFil: Ali, Nilufar. Augusta University; Estados UnidosFil: García Romero, Ezra Michelet. Universidad Veracruzana; MéxicoFil: Sharma, Sorabh. Birla Institute of Technology and Science; IndiaFil: Ghosh, Shampa. Indian Council of Medical Research; IndiaFil: Sinha, Jitendra K.. Indian Council of Medical Research; IndiaFil: Loke, Hannah. Hudson Institute of Medical Research; AustraliaFil: Jain, Vishal. Defence Institute of Physiology and Allied Sciences; IndiaFil: Lepeta, Katarzyna. Polish Academy of Sciences; ArgentinaFil: Salamian, Ahmad. Polish Academy of Sciences; ArgentinaFil: Sharma, Mahima. Polish Academy of Sciences; ArgentinaFil: Golpich, Mojtaba. University Kebangsaan Malaysia Medical Centre; MalasiaFil: Nawrotek, Katarzyna. University Of Lodz; ArgentinaFil: Paid, Ramesh K.. Indian Institute of Chemical Biology; IndiaFil: Shahidzadeh, Sheila M.. Syracuse University; Estados UnidosFil: Piermartiri, Tetsade. Universidade Federal de Santa Catarina; BrasilFil: Amini, Elham. University Kebangsaan Malaysia Medical Centre; MalasiaFil: Pastor, Verónica. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia ; ArgentinaFil: Wilson, Yvette. University of Melbourne; AustraliaFil: Adeniyi, Philip A.. Afe Babalola University; NigeriaFil: Datusalia, Ashok K.. National Brain Research Centre; IndiaFil: Vafadari, Benham. Polish Academy of Sciences; ArgentinaFil: Saini, Vedangana. University of Nebraska; Estados UnidosFil: Suárez Pozos, Edna. Instituto Politécnico Nacional; MéxicoFil: Kushwah, Neetu. Defence Institute of Physiology and Allied Sciences; IndiaFil: Fontanet, Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia ; ArgentinaFil: Turner, Anthony J.. University of Leeds; Reino Unid

Extinction of cue-evoked food seeking recruits a GABAergic interneuron ensemble in the dorsal medial prefrontal cortex of mice

Animals must quickly adapt food-seeking strategies to locate nutrient sources in dynamically changing environments. Learned associations between food and environmental cues that predict its availability promote food-seeking behaviors. However, when such cues cease to predict food availability, animals undergo 'extinction' learning, resulting in the inhibition of food-seeking responses. Repeatedly activated sets of neurons, or 'neuronal ensembles', in the dorsal medial prefrontal cortex (dmPFC) are recruited following appetitive conditioning and undergo physiological adaptations thought to encode cue-reward associations. However, little is known about how the recruitment and intrinsic excitability of such dmPFC ensembles are modulated by extinction learning. Here, we used in vivo 2-Photon imaging in male Fos-GFP mice that express green fluorescent protein (GFP) in recently behaviorally-activated neurons to determine the recruitment of activated pyramidal and GABAergic interneuron mPFC ensembles during extinction. During extinction, we revealed a persistent activation of a subset of interneurons which emerged from a wider population of interneurons activated during the initial extinction session. This activation pattern was not observed in pyramidal cells, and extinction learning did not modulate the excitability properties of activated neurons. Moreover, extinction learning reduced the likelihood of reactivation of pyramidal cells activated during the initial extinction session. Our findings illuminate novel neuronal activation patterns in the dmPFC underlying extinction of food-seeking, and in particular, highlight an important role for interneuron ensembles in this inhibitory form of learning

Investigating the Role of Experience-Activated Neurons in Sleep-Dependent Memory Consolidation

Sleep is critical for memory consolidation, yet the mechanisms which underlie this process are not well understood. There are two main hypotheses on how sleep promotes memory consolidation: the Sleep Homeostasis Hypothesis (SHY) and Active System Consolidation (ASC). SHY posits that during waking experience, the brain forms new memories—creating and strengthening synapses. Unregulated, this process could reach a point of saturation, which would be metabolically expensive and occlude new memory formation. SHY hypothesizes that during sleep, synapses are uniformly scaled, eliminating weak connections while stronger synapses (important memories) persist. In contrast, ASC postulates that sleep synchronizes neural firing, selectively activating (and strengthening) synaptic connections for specific memories—promoting consolidation. Thus, according to the two hypotheses, different synaptic changes are expected across sleep. Unresolved discrepancies between ACS and SHY may be due to technical limitations. Until recently, techniques have been unavailable to characterize and manipulate the neurons involved in a specific memory. Experimental outcomes have historically relied on data averaged across the neurons in a given brain structure. This lack of resolution has been a major barrier to understanding how sleep promotes memory consolidation. To move beyond these limitations, this thesis employs both in vivo recording of neurons (allowing tracking of memory encoding neurons across behavioral states) and recently developed engram, or memory trace, tools (allowing us to manipulate the activity of neurons encoding a specific memory). These experimental strategies aim to clarify whether SHY or ASC (or both) occur in primary visual cortex (V1) during post learning sleep, and whether this consolidation is dependent on sleep-specific memory reactivation. Using neuronal firing rates as a measure of plasticity, we examined the activity of V1 neurons across sleep, sleep deprivation, and post-learning sleep. The learning paradigm used is orientation-specific response potentiation (OSRP) which manifests as selective increases in V1 neuronal responses to a specific orientated grating. All sleep conditions showed an upregulation in the activity of low firing rate neurons and a downregulation of the activity of high firing rate neurons. These low firing rate neurons convey more visual information and selectively express OSRP. This suggests that sleep selectively upregulates the activity of neurons involved in sensory experience while simultaneously downregulating the activity those that are not. To evaluate the necessity of memory reactivation during sleep for consolidation, we used engram technology to selectively manipulate neurons activated by a specific visual stimulus. We combined visually-cued conditioning to oriented gratings with engram labelling to create a tractable system for manipulating a specific memory during sleep. We show that the TRAP (targeted recombination in active populations) engram mouse line can be used to drive transgene expression in a specific oriented grating ensemble in primary visual cortex. We then inhibit this ensemble during post-conditioning sleep causing impaired consolidation. This was done in a content specific manner without altering sleep architecture or oscillations - indicating that reactivation specifically is necessary for sleep dependent memory consolidation. This work unites two long standing hypotheses regarding sleep function for brain circuitry - SHY and ASC. The data support a comprehensive model in which sleep selectively reactivates neurons encoding relevant information. This upregulates their activity, while simultaneously decreasing activity in neurons whose information content is not salient. Future work will be needed to understand the molecular, cellular, and network mechanisms which drive these changes in specific cell populations.PHDMolecular, Cellular, and Developmental BiologyUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155072/1/clabri_1.pd

Theoretical and Experimental Studies of Neuronal Network Dynamics: Relating Topology to Function.

Within the brain, the interplay between connectivity patterns of neurons and their spatiotemporal dynamics is believed to be intricately linked to the bases of cognition, learning, and memory. In order to understand these processes, which are widely believed to be due to large-scale dynamical interactions, I investigate neuronal systems at the network level through computational simulation and reduced experimental preparations in conjunction with network analysis techniques. In a network of temporally evolving elements, it is possible to define a functional connectivity dependent on the spatiotemporal patterning of activity as well as an underlying anatomical connectivity. How functional integration or segregation of neuronal units arises from underlying anatomical structure could prove key to understanding the neural correlates of cognition and is dependent on various modulatory factors which define different brain states and functional modes. In addition, these dynamics are able to affect anatomical structure through plasticity and learning, completing a feedback loop of information processing and interaction with external environments. In the first part of my work, I model the rapid formation of novel associative memories in the hippocampus and consolidation to long term storage sites in the neocortex. I examine how global modulation of network excitability can give rise to functional structure reflecting underlying heterogeneous connectivity associated with stored memory. This mechanism, coupled with a neocortical inhibitory feedback and two different timescales of plasticity, can mediate information transfer and memory consolidation. These dynamics are matched with experimental data observed during behavioral learning. I pair these theoretical studies with an experimental investigation of a reduced hippocampal culture preparation. Within these cultures, cells are able to grow processes and connect together to form networks which can be easily visualized and recorded. I relate the anatomical neuronal network structure of these cells as well as the modulatory effects of a confluent glial network to changes in spiking activity, and find that, as the cultures mature and develop extended processes, bursting dynamics grow more coherent and global. Different glial network conditions modulate functional groupings, with more extensive glial morphology associated with global neuronal signaling and higher synchronization in firing dynamics.Ph.D.Applied PhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/78901/1/janexw_1.pd