Inter- and intra-animal variation of integrative properties of stellate cells in the medial entorhinal cortex

Funding Information: We thank Vanessa Stempel for comments on the manuscript, Tor Stensola and Edvard Moser for sharing published data, and Lukas Solanka and Lukas Fischer for help with building the large cage. This work was supported by grants to MN from the Wellcome Trust (200855/Z/16/Z) and the BBSRC (BB/L010496/1, BB/1022147/1 and BB/H020284/1). Publisher Copyright: © 2020, eLife Sciences Publications Ltd. All rights reserved.Peer reviewedPublisher PD

Micro-, Meso- and Macro-Dynamics of the Brain

Neurosciences, Neurology, Psychiatr

Models of spatial representation in the medial entorhinal cortex

Komplexe kognitive Funktionen wie Gedächtnisbildung, Navigation und Entscheidungsprozesse hängen von der Kommunikation zwischen Hippocampus und Neokortex ab. An der Schnittstelle dieser beiden Gehirnregionen liegt der entorhinale Kortex - ein Areal, das Neurone mit bemerkenswerten räumlichen Repräsentationen enthält: Gitterzellen. Gitterzellen sind Neurone, die abhängig von der Position eines Tieres in seiner Umgebung feuern und deren Feuerfelder ein dreieckiges Muster bilden. Man vermutet, dass Gitterzellen Navigation und räumliches Gedächtnis unterstützen, aber die Mechanismen, die diese Muster erzeugen, sind noch immer unbekannt. In dieser Dissertation untersuche ich mathematische Modelle neuronaler Schaltkreise, um die Entstehung, Weitervererbung und Verstärkung von Gitterzellaktivität zu erklären. Zuerst konzentriere ich mich auf die Entstehung von Gittermustern. Ich folge der Idee, dass periodische Repräsentationen des Raumes durch Konkurrenz zwischen dauerhaft aktiven, räumlichen Inputs und der Tendenz eines Neurons, durchgängiges Feuern zu vermeiden, entstehen könnten. Aufbauend auf vorangegangenen theoretischen Arbeiten stelle ich ein Einzelzell-Modell vor, das gitterartige Aktivität allein durch räumlich-irreguläre Inputs, Feuerratenadaptation und Hebbsche synaptische Plastizität erzeugt. Im zweiten Teil der Dissertation untersuche ich den Einfluss von Netzwerkdynamik auf das Gitter-Tuning. Ich zeige, dass Gittermuster zwischen neuronalen Populationen weitervererbt werden können und dass sowohl vorwärts gerichtete als auch rekurrente Verbindungen die Regelmäßigkeit von räumlichen Feuermustern verbessern können. Schließlich zeige ich, dass eine entsprechende Konnektivität, die diese Funktionen unterstützt, auf unüberwachte Weise entstehen könnte. Insgesamt trägt diese Arbeit zu einem besseren Verständnis der Prinzipien der neuronalen Repräsentation des Raumes im medialen entorhinalen Kortex bei.High-level cognitive abilities such as memory, navigation, and decision making rely on the communication between the hippocampal formation and the neocortex. At the interface between these two brain regions is the entorhinal cortex, a multimodal association area where neurons with remarkable representations of self-location have been discovered: the grid cells. Grid cells are neurons that fire according to the position of an animal in its environment and whose firing fields form a periodic triangular pattern. Grid cells are thought to support animal's navigation and spatial memory, but the cellular mechanisms that generate their tuning are still unknown. In this thesis, I study computational models of neural circuits to explain the emergence, inheritance, and amplification of grid-cell activity. In the first part of the thesis, I focus on the initial formation of grid-cell tuning. I embrace the idea that periodic representations of space could emerge via a competition between persistently-active spatial inputs and the reluctance of a neuron to fire for long stretches of time. Building upon previous theoretical work, I propose a single-cell model that generates grid-like activity solely form spatially-irregular inputs, spike-rate adaptation, and Hebbian synaptic plasticity. In the second part of the thesis, I study the inheritance and amplification of grid-cell activity. Motivated by the architecture of entorhinal microcircuits, I investigate how feed-forward and recurrent connections affect grid-cell tuning. I show that grids can be inherited across neuronal populations, and that both feed-forward and recurrent connections can improve the regularity of spatial firing. Finally, I show that a connectivity supporting these functions could self-organize in an unsupervised manner. Altogether, this thesis contributes to a better understanding of the principles governing the neuronal representation of space in the medial entorhinal cortex

Learning place cells, grid cells and invariances with excitatory and inhibitory plasticity

Neurons in the hippocampus and adjacent brain areas show a large diversity in their tuning to location and head direction, and the underlying circuit mechanisms are not yet resolved. In particular, it is unclear why certain cell types are selective to one spatial variable, but invariant to another. For example, place cells are typically invariant to head direction. We propose that all observed spatial tuning patterns – in both their selectivity and their invariance – arise from the same mechanism: Excitatory and inhibitory synaptic plasticity driven by the spatial tuning statistics of synaptic inputs. Using simulations and a mathematical analysis, we show that combined excitatory and inhibitory plasticity can lead to localized, grid-like or invariant activity. Combinations of different input statistics along different spatial dimensions reproduce all major spatial tuning patterns observed in rodents. Our proposed model is robust to changes in parameters, develops patterns on behavioral timescales and makes distinctive experimental predictions.BMBF, 01GQ1201, Lernen und Gedächtnis in balancierten Systeme

Hippocampal Spatial Representation: Integrating Environmental and Self-motion Signals

Electrophysiological recording in freely-moving rodents has established that place cells fire when the animal occupies a specific location and grid cells fire when at several locations, arranged on a regular triangular grid. Experiments and theories suggest that place cells and grid cells 1) receive inputs reflecting both environmental and self-motion information, and 2) are functionally connected to each other. Yet it remains elusive how the environmental and self-motion inputs dictate either place cell or grid cell firing. In a series of experiments, I address this question by manipulating the inputs independently while simultaneously recording place and grid cells activity. Firstly, I introduce our visual 2-d virtual reality system, in which mice run on an air-supported Styrofoam ball with their head held but allowed to rotate in the horizontal plane. The virtual arena is projected on surrounding screens and on the floor at a viewpoint that shifts with the rotation of the ball. With sufficient training, mice can navigate freely in the virtual environment and successfully retrieve rewards from an unmarked location. Electrophysiological data confirms that place, grid, and head-direction cells show characteristic spatial tuning in VR. In a second experiment, the gain factor that maps mice’s running speed to the visual translation of the virtual environment is manipulated. Results show that place cell firings are more driven by vision while grid cells incorporate self-motion inputs better. The last experiment had mice navigate in darkness. Without visual input co-recorded place cells and grid cells both suffer disruption in spatial tuning, albeit tuning is better preserved near to environmental boundaries. These results demonstrated that environmental and self-motion signals contribute to place and grid cells’ spatial representation of different significance, and constrain models with presumptions about how the place cells and grid cells integrate inputs and interact with each other

Acetylcholine neuromodulation in normal and abnormal learning and memory: vigilance control in waking, sleep, autism, amnesia, and Alzheimer's disease

This article provides a unified mechanistic neural explanation of how learning, recognition, and cognition break down during Alzheimer's disease, medial temporal amnesia, and autism. It also clarifies whey there are often sleep disturbances during these disorders. A key mechanism is how acetylcholine modules vigilance control in cortical layer

The representation of space in mammals

Animals require cognitive maps for efficiently navigating in their natural habitat. Cognitive maps are a neuronal representation of their outside world. In mammals, place cells and grid cells have been implicated to form the basis of these neuronal representations. Place cells are active at one particular location in an environment and grid cells at multiple locations of the external world that are arranged in a hexagonal lattice.As such, these cell types encode space in qualitatively different ways. Whereas the firing of one place cell is indicative of the animal's current location, the firing of one grid cell suggests that the animal is at any of the lattice's nodes. Thus, a population of place cells with varying parameters (place code) is required to exhaustively and uniquely represent an environment. Similarly, for grid cells a population with diverse encoding parameters (grid code) is needed. Place cells indeed have varying parameters: different cells are active at different locations, and the active locations have different sizes. Also, the hexagonal lattices of grid cells differ: they are spatially shifted, have different distances between the nodes and the sizes of the nodes vary in their magnitude. Hence, grid codes and place codes depend on multiple parameters, but what is the effect of these on the representation of space that they provide?In this thesis, we study, which parameters are key for an accurate representation of space by place and grid codes, respectively. Furthermore, we investigate whether place and grid codes provide a qualitatively different spatial resolution

A neural network model of normal and abnormal learning and memory consolidation

The amygdala and hippocampus interact with thalamocortical systems to regulate cognitive-emotional learning, and lesions of amygdala, hippocampus, thalamus, and cortex have different effects depending on the phase of learning when they occur. In examining eyeblink conditioning data, several questions arise: Why is the hippocampus needed for trace conditioning where there is a temporal gap between the conditioned stimulus offset and the onset of the unconditioned stimulus, but not needed for delay conditioning where stimuli temporally overlap and co-terminate? Why do amygdala lesions made before or immediately after training decelerate conditioning while those made later have no impact on conditioned behavior? Why do thalamic lesions degrade trace conditioning more than delay conditioning? Why do hippocampal lesions degrade recent learning but not temporally remote learning? Why do cortical lesions degrade temporally remote learning, and cause amnesia, but not recent or post-lesion learning? How is temporally graded amnesia caused by ablation of medial prefrontal cortex? How are mechanisms of motivated attention and the emergent state of consciousness linked during conditioning? How do neurotrophins, notably Brain Derived Neurotrophic Factor (BDNF), influence memory formation and consolidation? A neural model, called neurotrophic START, or nSTART, proposes answers to these questions. The nSTART model synthesizes and extends key principles, mechanisms, and properties of three previously published brain models of normal behavior. These three models describe aspects of how the brain can learn to categorize objects and events in the world; how the brain can learn the emotional meanings of such events, notably rewarding and punishing events, through cognitive-emotional interactions; and how the brain can learn to adaptively time attention paid to motivationally important events, and when to respond to these events, in a context-appropriate manner. The model clarifies how hippocampal adaptive timing mechanisms and BDNF may bridge the gap between stimuli during trace conditioning and thereby allow thalamocortical and corticocortical learning to take place and be consolidated. The simulated data arise as emergent properties of several brain regions interacting together. The model overcomes problems of alternative memory models, notably models wherein memories that are initially stored in hippocampus move to the neocortex during consolidation