The concept of educatedness : an analysis of the current perspectives of a population of university teachers on the personal qualities indicative of educatedness, and its implications for university teaching

The author collected from a variety of sources descriptions of more than six hundred personal qualities held to be indicative of educational development. A survey was performed to determine which of these qualities are most widely agreed to be indicators of advanced educational development (that is, qualities which would indicate that a person had developed his or her potential as a functioning human being to an advanced extent). The respondents were 42 volunteer academics from many different university disciplines, with an avowed interest in the educational development of people. This survey made use of a card-sort method which enabled each respondent to assess each of the more than 600 collected qualities as potential indicators of educational development. Subsequent interviews gathered information on the respondents' insights into the essence of personal educational development and on the processes which they felt assisted in fostering the qualities they valued. A remarkable degree of consistency was found in the way the respondents (independently) prioritised the qualities. An analysis of the responses led to the deduction of the following eleven broad themes commonly held to characterise advanced educational development: • A sense of self-worth • A positive orientation to existence • A developed power of will • Creativeness • Individuality • A disposition to search for meaning • Being properly equipped to search for meaning • Movement towards self-understanding • Evidence of integrative understandings • A life-enhancing disposition and • The ability to make meaningful contact with others. The extent of alignment shown with these themes by respondents who exhibited a broad diversity of cultural and life-experiences makes it possible to propose that these themes might conceivably represent a substantial core of a universally valid interpretation of advanced educational development

Intrinsic electrophysiological properties of entorhinal cortex stellate cells and their contribution to grid cell firing fields

The medial entorhinal cortex (MEC) is an increasingly important focus for investigation of mechanisms for spatial representation. Grid cells found in layer II of the MEC are likely to be stellate cells, which form a major projection to the dentate gyrus. Entorhinal stellate cells are distinguished by distinct intrinsic electrophysiological properties, but how these properties contribute to representation of space is not yet clear. Here, we review the ionic conductances, synaptic, and excitable properties of stellate cells, and examine their implications for models of grid firing fields. We discuss why existing data are inconsistent with models of grid fields that require stellate cells to generate periodic oscillations. An alternative possibility is that the intrinsic electrophysiological properties of stellate cells are tuned specifically to control integration of synaptic input. We highlight recent evidence that the dorsal-ventral organization of synaptic integration by stellate cells, through differences in currents mediated by HCN and leak potassium channels, influences the corresponding organization of grid fields. Because accurate cellular data will be important for distinguishing mechanisms for generation of grid fields, we introduce new data comparing properties measured with whole-cell and perforated patch-clamp recordings. We find that clustered patterns of action potential firing and the action potential after-hyperpolarization (AHP) are particularly sensitive to recording condition. Nevertheless, with both methods, these properties, resting membrane properties and resonance follow a dorsal-ventral organization. Further investigation of the molecular basis for synaptic integration by stellate cells will be important for understanding mechanisms for generation of grid fields

Functional dissection of a cortical microcircuit for spatial computation

In mammals, spatial learning and memory depend on neural processing carried out in the hippocampal formation. Interestingly, extracellular recordings from behaving animals have shown that cells in this region exhibit spatially modulated activity patterns, thus providing insights into the neural activity underlying spatial behaviour. One area within the hippocampal formation, layer II of the medial entorhinal cortex, houses cells that encode a grid-like map of space using a firing rate code. At the same time, oscillatory signals at distinct theta (4–12 Hz) and gamma (30–120 Hz) frequencies are also present in layer II, providing a substrate for a timing code. To understand how layer II of the medial entorhinal cortex produces these outputs I sought to characterise the electrical properties and functional computational architecture of its microcircuitry. The functionality of any neural circuit depends on the electrical properties of its constituent cells. Because the grid cells in layer II are likely to be stellate cells, I used the perforated patch-clamp technique to accurately assess the intrinsic excitable properties of this cell type. Compared to whole-cell recordings, these recordings indicate that some intrinsic properties of stellate cells, such as spike clustering, which is revealed to be robust, are more likely to play a functional role in circuit computation. Conversely, other intrinsic properties, such as spontaneous membrane potential fluctuations, which are confirmed to be insufficiently stable to support reliable interference patterns, are revealed to be less likely than other, more robust electrical properties to play a direct role in circuit function. The characteristic connectivity profiles of different cell types are also critical for circuit function. To investigate cell type-specific connectivity in layer II I used optogenetic stimulation in combination with in vitro electrophysiology to record synaptic activity in different cell types while selectively activating distinct subpopulations of cells with light. Using this method I found that connections between stellate cells are absent or very rare and that communication between stellate cells is instead mediated by strong feedback inhibition from fast-spiking interneurons. Dissecting oscillatory activity in neural circuits may be important for establishing functionally relevant circuit architecture and dynamics but is difficult in vivo. I accomplished this in vitro by recapitulating the interacting theta and gamma rhythms that are observed in vivo with an optogenetic method. I found that locally driving a subset of neurons in the layer II microcircuit at theta frequency with a light stimiulus produced a nested field rhythm at gamma frequency that was also evident as rhythmic inhibition onto stellate cells. Critically, these interacting rhythms closely resembled those recorded from behaving animals. In addition, I found that this thetanested gamma is sufficiently regular to act as a clock-like reference signal, indicating its potential role in implementing a timing code. To functionally dissect the circuit I performed multiple simultaneous whole-cell patch-clamp recordings during circuit activation. These recordings revealed how feedback interactions between stellate cells and fast-spiking interneurons underpin the theta-nested gamma rhythm. Together, these results suggest that feedback inhibition in layer II acts as a common substrate for theta-nested gamma oscillations and possibly also grid firing fields, thereby providing a framework for understanding how computations are carried out in layer II of the medial entorhinal cortex

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

Continuous attractor network models of grid cell firing based on excitatory-inhibitory interactions

Neurons in the medial entorhinal cortex encode location through spatial firing fields that have a grid‐like organisation. The challenge of identifying mechanisms for grid firing has been addressed through experimental and theoretical investigations of medial entorhinal circuits. Here, we discuss evidence for continuous attractor network models that account for grid firing by synaptic interactions between excitatory and inhibitory cells. These models assume that grid‐like firing patterns are the result of computation of location from velocity inputs, with additional spatial input required to oppose drift in the attractor state. We focus on properties of continuous attractor networks that are revealed by explicitly considering excitatory and inhibitory neurons, their connectivity and their membrane potential dynamics. Models at this level of detail can account for theta‐nested gamma oscillations as well as grid firing, predict spatial firing of interneurons as well as excitatory cells, show how gamma oscillations can be modulated independently from spatial computations, reveal critical roles for neuronal noise, and demonstrate that only a subset of excitatory cells in a network need have grid‐like firing fields. Evaluating experimental data against predictions from detailed network models will be important for establishing the mechanisms mediating grid firing. [Image: see text

GABAergic Projections from the Medial Septum Selectively Inhibit Interneurons in the Medial Entorhinal Cortex

The medial septum (MS) is required for theta rhythmic oscillations and grid cell firing in the medial entorhinal cortex (MEC). While GABAergic, glutamatergic, and cholinergic neurons project from the MS to the MEC, their synaptic targets are unknown. To investigate whether MS neurons innervate specific layers and cell types in the MEC, we expressed channelrhodopsin-2 in mouse MS neurons and used patch-clamp recording in brain slices to determine the response to light activation of identified cells in the MEC. Following activation of MS axons, we observed fast monosynaptic GABAergic IPSPs in the majority (>60%) of fast-spiking (FS) and low-threshold-spiking (LTS) interneurons in all layers of the MEC, but in only 1.5% of nonstellate principal cells (NSPCs) and in no stellate cells. We also observed fast glutamatergic responses to MS activation in a minority (<5%) of NSPCs, FS, and LTS interneurons. During stimulation of MS inputs at theta frequency (10 Hz), the amplitude of GABAergic IPSPs was maintained, and spike output from LTS and FS interneurons was entrained at low (25–60 Hz) and high (60–180 Hz) gamma frequencies, respectively. By demonstrating cell type-specific targeting of the GABAergic projection from the MS to the MEC, our results support the idea that the MS controls theta frequency activity in the MEC through coordination of inhibitory circuits

Noise promotes independent control of gamma oscillations and grid firing within recurrent attractor networks

Neural computations underlying cognitive functions require calibration of the strength of excitatory and inhibitory synaptic connections and are associated with modulation of gamma frequency oscillations in network activity. However, principles relating gamma oscillations, synaptic strength and circuit computations are unclear. We address this in attractor network models that account for grid firing and theta-nested gamma oscillations in the medial entorhinal cortex. We show that moderate intrinsic noise massively increases the range of synaptic strengths supporting gamma oscillations and grid computation. With moderate noise, variation in excitatory or inhibitory synaptic strength tunes the amplitude and frequency of gamma activity without disrupting grid firing. This beneficial role for noise results from disruption of epileptic-like network states. Thus, moderate noise promotes independent control of multiplexed firing rate- and gamma-based computational mechanisms. Our results have implications for tuning of normal circuit function and for disorders associated with changes in gamma oscillations and synaptic strength

Laminar and Dorsoventral Molecular Organization of the Medial Entorhinal Cortex Revealed by Large-scale Anatomical Analysis of Gene Expression

Neural circuits in the medial entorhinal cortex (MEC) encode an animal's position and orientation in space. Within the MEC spatial representations, including grid and directional firing fields, have a laminar and dorsoventral organization that corresponds to a similar topography of neuronal connectivity and cellular properties. Yet, in part due to the challenges of integrating anatomical data at the resolution of cortical layers and borders, we know little about the molecular components underlying this organization. To address this we develop a new computational pipeline for high-throughput analysis and comparison of in situ hybridization (ISH) images at laminar resolution. We apply this pipeline to ISH data for over 16,000 genes in the Allen Brain Atlas and validate our analysis with RNA sequencing of MEC tissue from adult mice. We find that differential gene expression delineates the borders of the MEC with neighboring brain structures and reveals its laminar and dorsoventral organization. We propose a new molecular basis for distinguishing the deep layers of the MEC and show that their similarity to corresponding layers of neocortex is greater than that of superficial layers. Our analysis identifies ion channel-, cell adhesion- and synapse-related genes as candidates for functional differentiation of MEC layers and for encoding of spatial information at different scales along the dorsoventral axis of the MEC. We also reveal laminar organization of genes related to disease pathology and suggest that a high metabolic demand predisposes layer II to neurodegenerative pathology. In principle, our computational pipeline can be applied to high-throughput analysis of many forms of neuroanatomical data. Our results support the hypothesis that differences in gene expression contribute to functional specialization of superficial layers of the MEC and dorsoventral organization of the scale of spatial representations