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Works citing SciDraw
Grid cells encode local head direction
Klara Gerlei, Jessica Passlack, Ian Hawes, Brianna Vandrey, Holly Stevens, Ioannis Papastathopoulos, Matthew F. Nolan
Grid and head direction codes represent cognitive spaces for navigation and memory. Pure grid cells generate grid codes that have been assumed to be independent of head direction, whereas conjunctive cells generate grid representations that are tuned to a single head direction. Here, we demonstrate that pure grid cells also encode head direction, but through distinct mechanisms. We show that individual firing fields of pure grid cells are tuned to multiple head directions, with the preferred sets of directions differing between fields. This local directionality is not predicted by previous continuous attractor or oscillatory interference models of grid firing but is accounted for by models in which pure grid cells integrate inputs from co-aligned conjunctive cells with firing rates that differ between their fields. Local directional signals from grid cells may contribute to downstream computations by decorrelating different points of view from the same location
Space, Time, and Fear: Survival Computations along Defensive Circuits
Dean Mobbs, Drew B. Headley, Weilun Ding, Peter Dayan
Naturalistic observations show that decisions to avoid or escape predators occur at different spatiotemporal scales and that they are supported by different computations and neural circuits. At their extremes, proximal threats are addressed by a limited repertoire of reflexive and myopic actions, reflecting reduced decision and state spaces and model-free (MF) architectures. Conversely, distal threats allow increased information processing supported by model-based(MB) operations, including affective prospection, replay, and planning. However, MF and MB computations are often intertwined, and under conditions of safety the foundations for future effective reactive execution can be laid through MB instruction of MF control. Together, these computations are associated with distinct population codes embedded within a distributed defensive circuitry whose goal is to determine and realize the best policy.
Adeno-Associated Viral Vectors in Neuroscience Research
David L. Haggerty, Gregory G. Grecco, Kaitlin C. Reeves, Brady Atwood
Adeno-associated viral vectors (AAVs) are increasingly useful preclinical tools in neuroscience research studies for interrogating cellular and neurocircuit functions and mapping brain connectivity. Clinically, AAVs are showing increasing promise as viable candidates for treating multiple neurological diseases. Here, we briefly review the utility of AAVs in mapping neurocircuits, manipulating neuronal function and gene expression, and activity labeling in preclinical research studies as well as AAV-based gene therapies for diseases of the nervous system. This review highlights the vast potential that AAVs have for transformative research and therapeutics in the neurosciences.
Hippocampal Remapping as Hidden State Inference
Honi Sanders, Matthew A. Wilson, Samuel J. Gershman
Cells in the hippocampus tuned to spatial location (place cells) typically change their tuning when an animal changes context, a phenomenon known as remapping. A fundamental challenge to understanding remapping is the fact that what counts as a “context change” has never been precisely defined. Furthermore, different remapping phenomena have been classified on the basis of how much the tuning changes after different types and degrees of context change, but the relationship between these variables is not clear. We address these ambiguities by formalizing remapping in terms of hidden state inference. According to this view, remapping does not directly reflect objective, observable properties of the environment, but rather subjective beliefs about the hidden state of the environment. We show how the hidden state framework can resolve a number of puzzles about the nature of remapping.
A computationally-assisted approach to extracellular neural electrophysiology with multi-electrode arrays
Alessio Paolo Buccino
With the advent of high-density multi-electrode arrays we are now able to measure the activity of hundreds of neurons simultaneously, even at the sub-cellular level. However, next-generation devices introduce novel grand challenges and the need for appropriate tools to handle the rich information that can be recorded. The work presented in this thesis has therefore focused on developing and benchmarking new tools and methods for using such devices at their full potential. Main research findings Neurons use tiny electrical signals to communicate with each other. By inserting electrodes in the brain, we can read from neurons (record electrical activity) and even write to them (induce activity by electrical stimulation). In recent years there has been a huge development in neural devices: neuroscientists can now use probes with several hundreds of very closely-spaced electrodes called Multi-Electrode Arrays. The goal of my PhD was to develop methods and tools to improve the way we read from and write to the brain tissue using these newly developed probes. In order to achieve my goal, I followed a computationally-assisted approach. The idea is to use very detailed models of single neurons (mathematical description of how the neuron behaves) to run simulations, that can be used to guide the development of new analysis methods. I used this approach to tackle several open problems of extracellular electrophysiology, including spike sorting, neuron localization, cell-type classification, and selective electrical microstimulation of neurons.
Focal epilepsy modulates vesicular positioning at cortical synapses
Eleonora Vannini, Laura Restani, Marialaura Dilillo, Liam McDonnell, Matteo Caleo, Vincenzo Marra
Neuronal networks’ hyperexcitability often results from an unbalance between excitatory and inhibitory neurotransmission; however, underlying synaptic alterations leading to this condition remains poorly understood. Here, we assess synaptic changes in the visual cortex of epileptic tetanus neurotoxin-injected mice. Using an ultrastructural measure of synaptic activity, we quantified functional differences at excitatory and inhibitory synapses. We found homeostatic changes in hyperexcitable networks, expressed as an early onset lengthening of active zones at inhibitory synapses followed by spatial reorganization of recycled vesicles at excitatory synapses. A proteomic analysis of synaptic content revealed an upregulation of Carboxypeptidase E (CPE) following Tetanus NeuroToxin (TeNT) injection. Remarkably, inhibition of CPE rapidly decreased network discharges in vivo. These analyses reveal a complex landscape of homeostatic changes affecting the epileptic synaptic release machinery, differentially at inhibitory and excitatory terminals. Our study unveil homeostatic presynaptic mechanisms which may impact release timing rather than synaptic strength.
The role of the periaqueductal gray in escape behavior
Yaara Lefler, Dario Campagner, Tiago Branco
Escape behavior is a defensive action deployed by animals in response to imminent threats. In mammalian species, a variety of different brain circuits are known to participate in this crucial survival behavior. One of these circuits is the periaqueductal gray, a midbrain structure that can command a variety of instinctive behaviors. Recent experiments using modern systems neuroscience techniques have begun to elucidate the specific role of the periaqueductal gray in controlling escape. These have shown that periaqueductal gray neurons are crucial units for gating and commanding the initiation of escape, specifically activated in situations of imminent, escapable threat. In addition, it is becoming clear that the periaqueductal gray integrates brain-wide information that can modulate escape initiation to generate flexible defensive behaviors.
Cortical circuits for integration of self-motion and visual-motion signals
Tristan A Chaplin, Troy W Margrie
The cerebral cortex contains cells which respond to movement of the head, and these cells are thought to be involved in the perception of self-motion. In particular, studies in the primary visual cortex of mice show that both running speed and passive whole-body rotation modulates neuronal activity, and modern genetically targeted viral tracing approaches have begun to identify previously unknown circuits that underlie these responses. Here we review recent experimental findings and provide a road map for future work in mice to elucidate the functional architecture and emergent properties of a cortical network potentially involved in the generation of egocentric-based visual representations for navigation.
Dynamics of Awake Hippocampal-Prefrontal Replay for Spatial Learning and Memory-Guided Decision Making
Justin D.Shin, Wenbo Tang, Shantanu P. Jadhav
Spatial learning requires remembering and choosing paths to goals. Hippocampal place cells replay spatial paths during immobility in reverse and forward order, offering a potential mechanism. However, how replay supports both goal-directed learning and memory-guided decision making is unclear. We therefore continuously tracked awake replay in the same hippocampal-prefrontal ensembles throughout learning of a spatial alternation task. We found that, during pauses between behavioral trajectories, reverse and forward hippocampal replay supports an internal cognitive search of available past and future possibilities and exhibits opposing learning gradients for prediction of past and future behavioral paths, respectively. Coordinated hippocampal-prefrontal replay distinguished correct past and future paths from alternative choices, suggesting a role in recall of past paths to guide planning of future decisions for spatial working memory. Our findings reveal a learning shift from hippocampal reverse-replay-based retrospective evaluation to forward-replay-based prospective planning, with prefrontal readout of memory-guided paths for learning and decision making.
Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future
Grace W. Lindsay
Convolutional neural networks (CNNs) were inspired by early findings in the study of biological vision. They have since become successful tools in computer vision and state-of-the-art models of both neural activity and behavior on visual tasks. This review highlights what, in the context of CNNs, it means to be a good model in computational neuroscience and the various ways models can provide insight. Specifically, it covers the origins of CNNs and the methods by which we validate them as models of biological vision. It then goes on to elaborate on what we can learn about biological vision by understanding and experimenting on CNNs and discusses emerging opportunities for the use of CNNS in vision research beyond basic object recognition.