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Lukas Solanka

Bio: Lukas Solanka is an academic researcher from University of Edinburgh. The author has contributed to research in topics: Attractor network & Attractor. The author has an hindex of 3, co-authored 5 publications receiving 308 citations.

Papers
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Journal ArticleDOI
09 Jan 2013-Neuron
TL;DR: This work establishes with optogenetic activation of layer II of the medial entorhinal cortex that theta frequency drive to this circuit is sufficient to generate nested gamma frequency oscillations in synaptic activity and indicates that grid cells communicate primarily via inhibitory interneurons.

266 citations

Journal ArticleDOI
TL;DR: In this paper, the authors discuss evidence for continuous attractor network models that account for grid firing by synaptic interactions between excitatory and inhibitory cells, and demonstrate that only a subset of neurons in a network need to have grid-like firing fields.
Abstract: 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.

54 citations

Journal ArticleDOI
06 Jul 2015-eLife
TL;DR: It is shown that moderate intrinsic noise massively increases the range of synaptic strengths supporting gamma oscillations and grid computation, and promotes independent control of multiplexed firing rate- and gamma-based computational mechanisms.
Abstract: 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.

25 citations

Journal ArticleDOI
TL;DR: The results show that the presence of noise may have profound implications on the stability of theta-nested gamma oscillations in attractor neural circuits with feedback inhibition.
Abstract: Grid cells in the medial entorhinal cortex (MEC) encode location through firing fields that form grid-like maps of the environment. At the same time network activity in the MEC is dominated by oscillations in the theta (4-12 Hz) and gamma (30-100 Hz) bands. Our recent experimental data established that feedback inhibition between excitatory stellate cells and inhibitory fast spiking interneurons dominates the synaptic connectivity in layer II of the MEC, and that continuous attractor models derived from these properties are sufficient to explain both the network oscillations and grid firing fields [1]. Here we use a spiking continuous attractor network model of layer II stellate cells and fast spiking interneurons to examine the effect of intrinsic noise on the emergence of theta nested-gamma oscillations. In this model, stellate cells connect exclusively to interneurons, while interneurons contact only stellate cells. When driven with a theta (8 Hz) modulated external input, we observed network synchronization in the gamma range during the trough of the theta signal, which depended on the amount of intrinsic Gaussian noise generated independently at each neuron. Without noise, the network exhibited aberrant hyper-synchronous firing of a subpopulation of stellate cells at the beginning of each theta cycle. The stellate cell firing resulted in a strong feedback inhibitory postsynaptic currents from fast spiking interneurons that had an additional synchronizing effect. The strong inhibitory feedback abolished nested gamma oscillations. With increasing noise, the network entered an intermediate state in which we observed theta nested-gamma oscillations [1]. When noise level was set even higher, the nested gamma oscillations disappeared without increasing the amount of inhibitory coupling. We also present the impact of noise on bump attractor formation and gridness score of the grid-like receptive fields. Together, these results show that the presence of noise may have profound implications on the stability of theta-nested gamma oscillations in attractor neural circuits with feedback inhibition.

Cited by
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Journal ArticleDOI
TL;DR: This Historical Commentary reflects on the scientific landscape of this decade-long transition between microbial opsin engineering and modular genetic methods for cell-type targeting, with the publication of thousands of discoveries and insights into the function of nervous systems and beyond.
Abstract: Over the past 10 years, the development and convergence of microbial opsin engineering, modular genetic methods for cell-type targeting and optical strategies for guiding light through tissue have enabled versatile optical control of defined cells in living systems, defining modern optogenetics. Despite widespread recognition of the importance of spatiotemporally precise causal control over cellular signaling, for nearly the first half (2005-2009) of this 10-year period, as optogenetics was being created, there were difficulties in implementation, few publications and limited biological findings. In contrast, the ensuing years have witnessed a substantial acceleration in the application domain, with the publication of thousands of discoveries and insights into the function of nervous systems and beyond. This Historical Commentary reflects on the scientific landscape of this decade-long transition.

956 citations

Journal ArticleDOI
TL;DR: It is shown that removal of perineuronal nets leads to lower inhibitory spiking activity, and reduces grid cells’ ability to create stable representations of a novel environment, and that PNN removal in entorhinal cortex distorted spatial representations in downstream hippocampal neurons.
Abstract: Grid cells are part of a widespread network which supports navigation and spatial memory. Stable grid patterns appear late in development, in concert with extracellular matrix aggregates termed perineuronal nets (PNNs) that condense around inhibitory neurons. It has been suggested that PNNs stabilize synaptic connections and long-term memories, but their role in the grid cell network remains elusive. We show that removal of PNNs leads to lower inhibitory spiking activity, and reduces grid cells' ability to create stable representations of a novel environment. Furthermore, in animals with disrupted PNNs, exposure to a novel arena corrupted the spatiotemporal relationships within grid cell modules, and the stored representations of a familiar arena. Finally, we show that PNN removal in entorhinal cortex distorted spatial representations in downstream hippocampal neurons. Together this work suggests that PNNs provide a key stabilizing element for the grid cell network.

935 citations

Journal ArticleDOI
01 Aug 2014-Science
TL;DR: Not only are PV+ interneurons involved in basic microcircuit functions, but they also play a role in complex network operations, including expansion of dynamic activity range, pattern separation, modulation of place and grid field shapes, phase precession, and gain modulation of sensory responses.
Abstract: The success story of fast-spiking, parvalbumin-positive (PV(+)) GABAergic interneurons (GABA, γ-aminobutyric acid) in the mammalian central nervous system is noteworthy. In 1995, the properties of these interneurons were completely unknown. Twenty years later, thanks to the massive use of subcellular patch-clamp techniques, simultaneous multiple-cell recording, optogenetics, in vivo measurements, and computational approaches, our knowledge about PV(+) interneurons became more extensive than for several types of pyramidal neurons. These findings have implications beyond the "small world" of basic research on GABAergic cells. For example, the results provide a first proof of principle that neuroscientists might be able to close the gaps between the molecular, cellular, network, and behavioral levels, representing one of the main challenges at the present time. Furthermore, the results may form the basis for PV(+) interneurons as therapeutic targets for brain disease in the future. However, much needs to be learned about the basic function of these interneurons before clinical neuroscientists will be able to use PV(+) interneurons for therapeutic purposes.

899 citations

Journal ArticleDOI
TL;DR: It is argued that the phenomenology of effort can be understood as the felt output of these cost/benefit computations of the costs and benefits associated with task performance and motivates reduced deployment of these computational mechanisms in the service of the present task.
Abstract: Why does performing certain tasks cause the aversive experience of mental effort and concomitant deterioration in task performance? One explanation posits a physical resource that is depleted over time. We propose an alternative explanation that centers on mental representations of the costs and benefits associated with task performance. Specifically, certain computational mechanisms, especially those associated with executive function, can be deployed for only a limited number of simultaneous tasks at any given moment. Consequently, the deployment of these computational mechanisms carries an opportunity cost-that is, the next-best use to which these systems might be put. We argue that the phenomenology of effort can be understood as the felt output of these cost/benefit computations. In turn, the subjective experience of effort motivates reduced deployment of these computational mechanisms in the service of the present task. These opportunity cost representations, then, together with other cost/benefit calculations, determine effort expended and, everything else equal, result in performance reductions. In making our case for this position, we review alternative explanations for both the phenomenology of effort associated with these tasks and for performance reductions over time. Likewise, we review the broad range of relevant empirical results from across sub-disciplines, especially psychology and neuroscience. We hope that our proposal will help to build links among the diverse fields that have been addressing similar questions from different perspectives, and we emphasize ways in which alternative models might be empirically distinguished.

895 citations

Journal ArticleDOI
TL;DR: The current understanding of the origins and the mnemonic functions of hippocampal theta, sharp wave–ripples and gamma rhythms is discussed on the basis of findings from rodent studies and an updated synthesis of their roles and interactions within the hippocampal network is presented.
Abstract: The hippocampal local field potential (LFP) shows three major types of rhythms: theta, sharp wave-ripples and gamma. These rhythms are defined by their frequencies, they have behavioural correlates in several species including rats and humans, and they have been proposed to carry out distinct functions in hippocampal memory processing. However, recent findings have challenged traditional views on these behavioural functions. In this Review, I discuss our current understanding of the origins and the mnemonic functions of hippocampal theta, sharp wave-ripples and gamma rhythms on the basis of findings from rodent studies. In addition, I present an updated synthesis of their roles and interactions within the hippocampal network.

490 citations