Imperial College London

ProfessorSimonSchultz

Faculty of EngineeringDepartment of Bioengineering

Professor of Neurotechnology
 
 
 
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Contact

 

s.schultz Website

 
 
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Location

 

4.11Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

114 results found

Gonzalez GIV, Kgwarae PO, Schultz SR, 2023, Two-photon targeted, quad whole-cell patch-clamping robot, 11th International IEEE EMBS Conference on Neural Engineering (IEEE/EMBS NER), Publisher: IEEE, ISSN: 1948-3546

We present an automated quad-channel patch-clamp technology platform for ex vivo brain slice electrophysiology, capable of both blind and two-photon targeted robotically automated patching. The robot scales up the patch-clamp single-cell recording technique to four simultaneous channels, with seal success rates for two-photon targeted and blind modes of 54% and 68% respectively. In 50% of targeted trials (where specific cells were required), two simultaneous recordings or more were obtained. For blind mode, most trials yielded dual or triple recordings. This robot, a milestone on the path to a true in vivo targeted robotic multi-patching technology platform, will allow numerous studies into the function and connectivity patterns of both primary and secondary cell types.

Conference paper

Clarke K, Burkitt A, Lian Y, Schultz SR, Go MA, Davey Cet al., 2023, Investigating the Mechanisms Behind Experience-Dependent Place Cell Shifting, Publisher: SPRINGER, Pages: S35-S36, ISSN: 0929-5313

Conference paper

Mitchell-Heggs R, Prado S, Gava G, Go MA, Schultz Set al., 2022, Neural manifold analysis of brain circuit dynamics in health and disease, Journal of Computational Neuroscience, Vol: 51, Pages: 1-21, ISSN: 0929-5313

Recent developments in experimental neuroscience make it possible to simultaneously record the activity of thousands of neurons. However, the development of analysis approaches for such large-scale neural recordings have been slower than those applicable to single-cell experiments. One approach that has gained recent popularity is neural manifold learning. This approach takes advantage of the fact that often, even though neural datasets may be very high dimensional, the dynamics of neural activity tends to traverse a much lower-dimensional space. The topological structures formed by these low-dimensional neural subspaces are referred to as “neural manifolds”, and may potentially provide insight linking neural circuit dynamics with cognitive function and behavioral performance. In this paper we review a number of linear and non-linear approaches to neural manifold learning, including principal component analysis (PCA), multi-dimensional scaling (MDS), Isomap, locally linear embedding (LLE), Laplacian eigenmaps (LEM), t-SNE, and uniform manifold approximation and projection (UMAP). We outline these methods under a common mathematical nomenclature, and compare their advantages and disadvantages with respect to their use for neural data analysis. We apply them to a number of datasets from published literature, comparing the manifolds that result from their application to hippocampal place cells, motor cortical neurons during a reaching task, and prefrontal cortical neurons during a multi-behavior task. We find that in many circumstances linear algorithms produce similar results to non-linear methods, although in particular cases where the behavioral complexity is greater, non-linear methods tend to find lower-dimensional manifolds, at the possible expense of interpretability. We demonstrate that these methods are applicable to the study of neurological disorders through simulation of a mouse model of Alzheimer’s Disease, and speculate that neural manif

Journal article

Barkus C, Bergmann C, Branco T, Carandini M, Chadderton PT, Galiñanes GL, Gilmour G, Huber D, Huxter JR, Khan AG, King AJ, Maravall M, O'Mahony T, Ragan CI, Robinson ESJ, Schaefer AT, Schultz SR, Sengpiel F, Prescott MJet al., 2022, Refinements to rodent head fixation and fluid/food control for neuroscience, Journal of Neuroscience Methods, Vol: 381, Pages: 109705-109705, ISSN: 0165-0270

The use of head fixation in mice is increasingly common in research, its use having initially been restricted to the field of sensory neuroscience. Head restraint has often been combined with fluid control, rather than food restriction, to motivate behaviour, but this too is now in use for both restrained and non-restrained animals. Despite this, there is little guidance on how best to employ these techniques to optimise both scientific outcomes and animal welfare. This article summarises current practices and provides recommendations to improve animal wellbeing and data quality, based on a survey of the community, literature reviews, and the expert opinion and practical experience of an international working group convened by the UK's National Centre for the Replacement, Refinement and Reduction of Animals in Research (NC3Rs). Topics covered include head fixation surgery and post-operative care, habituation to restraint, and the use of fluid/food control to motivate performance. We also discuss some recent developments that may offer alternative ways to collect data from large numbers of behavioural trials without the need for restraint. The aim is to provide support for researchers at all levels, animal care staff, and ethics committees to refine procedures and practices in line with the refinement principle of the 3Rs.

Journal article

Gobbo F, Mitchell-Heggs R, Tse D, Al Omrani M, Spooner PA, Schultz SR, Morris RGMet al., 2022, Neuronal signature of spatial decision-making during navigation by freely moving rats by using calcium imaging., Proceedings of the National Academy of Sciences of USA, Vol: 119, Pages: 1-12, ISSN: 0027-8424

A challenge in spatial memory is understanding how place cell firing contributes to decision-making in navigation. A spatial recency task was created in which freely moving rats first became familiar with a spatial context over several days and thereafter were required to encode and then selectively recall one of three specific locations within it that was chosen to be rewarded that day. Calcium imaging was used to record from more than 1,000 cells in area CA1 of the hippocampus of five rats during the exploration, sample, and choice phases of the daily task. The key finding was that neural activity in the startbox rose steadily in the short period prior to entry to the arena and that this selective population cell firing was predictive of the daily changing goal on correct trials but not on trials in which the animals made errors. Single-cell and population activity measures converged on the idea that prospective coding of neural activity can be involved in navigational decision-making.

Journal article

Garcia-Font N, Mitchell-Heggs R, Saxena K, Gabbert C, Taylor G, Mastroberadino G, Spooner PA, Gobbo F, Dabrowska JK, Chattarji S, Kind PC, Schultz SR, Morris RGMet al., 2022, Ca2+ imaging of self and other in medial prefrontal cortex during social dominance interactions in a tube test., Proceedings of the National Academy of Sciences of USA, Vol: 119, Pages: 1-12, ISSN: 0027-8424

The study of social dominance interactions between animals offers a window onto the decision-making involved in establishing dominance hierarchies and an opportunity to examine changes in social behavior observed in certain neurogenetic disorders. Competitive social interactions, such as in the widely used tube test, reflect this decision-making. Previous studies have focused on the different patterns of behavior seen in the dominant and submissive animal, neural correlates of effortful behavior believed to mediate the outcome of such encounters, and interbrain correlations of neural activity. Using a rigorous mutual information criterion, we now report that neural responses recorded with endoscopic calcium imaging in the prelimbic zone of the medial prefrontal cortex show unique correlations to specific dominance-related behaviors. Interanimal analyses revealed cell/behavior correlations that are primarily with an animal's own behavior or with the other animal's behavior, or the coincident behavior of both animals (such as pushing by one and resisting by the other). The comparison of unique and coincident cells helps to disentangle cell firing that reflects an animal's own or the other's specific behavior from situations reflecting conjoint action. These correlates point to a more cognitive rather than a solely behavioral dimension of social interactions that needs to be considered in the design of neurobiological studies of social behavior. These could prove useful in studies of disorders affecting social recognition and social engagement, and the treatment of disorders of social interaction.

Journal article

Gava GP, 2021, Network analysis of the cellular circuits of memory

Intuitively, memory is conceived as a collection of static images that we accumulate as we experience the world. But actually, memories are constantly changing through our life, shaped by our ongoing experiences. Assimilating new knowledge without corrupting pre-existing memories is then a critical brain function. However, learning and memory interact: prior knowledge can proactively influence learning, and new information can retroactively modify memories of past events. The hippocampus is a brain region essential for learning and memory, but the network-level operations that underlie the continuous integration of new experiences into memory, segregating them as discrete traces while enabling their interaction, are unknown. Here I show a network mechanism by which two distinct memories interact. Hippocampal CA1 neuronal ensembles were monitored in mice as they explored a familiar environment before and after forming a new place-reward memory in a different environment. By employing a network science representation of the co-firing relationships among principal cells, I first found that new associative learning modifies the topology of the cells’ co-firing patterns representing the unrelated familiar environment. I further observed that these neuronal co-firing graphs evolved along three functional axes: the first segregated novelty; the second distinguished individual novel behavioural experiences; while the third revealed cross-memory interaction. Finally, I found that during this process, high activity principal cells rapidly formed the core representation of each memory; whereas low activity principal cells gradually joined co-activation motifs throughout individual experiences, enabling cross-memory interactions. These findings reveal an organizational principle of brain networks where high and low activity cells are differentially recruited into coactivity motifs as build- ing blocks for the flexible integration and interaction of memories.Finally, I emp

Thesis dissertation

Ness N, Schultz SR, 2021, A computational grid-to-place-cell transformation model indicates a synaptic driver of place cell impairment in early-stage Alzheimer's Disease, PLoS Computational Biology, Vol: 17, Pages: 1-27, ISSN: 1553-734X

Alzheimer’s Disease (AD) is characterized by progressive neurodegeneration and cognitive impairment. Synaptic dysfunction is an established early symptom, which correlates strongly with cognitive decline, and is hypothesised to mediate the diverse neuronal network abnormalities observed in AD. However, how synaptic dysfunction contributes to network pathology and cognitive impairment in AD remains elusive. Here, we present a grid-cell-to-place-cell transformation model of long-term CA1 place cell dynamics to interrogate the effect of synaptic loss on network function and environmental representation. Synapse loss modelled after experimental observations in the APP/PS1 mouse model was found to induce firing rate alterations and place cell abnormalities that have previously been observed in AD mouse models, including enlarged place fields and lower across-session stability of place fields. Our results support the hypothesis that synaptic dysfunction underlies cognitive deficits, and demonstrate how impaired environmental representation may arise in the early stages of AD. We further propose that dysfunction of excitatory and inhibitory inputs to CA1 pyramidal cells may cause distinct impairments in place cell function, namely reduced stability and place map resolution.

Journal article

Gava GP, McHugh SB, Lefèvre L, Lopes-Dos-Santos V, Trouche S, El-Gaby M, Schultz SR, Dupret Det al., 2021, Integrating new memories into the hippocampal network activity space, Nature Neuroscience, Vol: 24, Pages: 326-330, ISSN: 1097-6256

By investigating the topology of neuronal co-activity, we found that mnemonic information spans multiple operational axes in the mouse hippocampus network. High-activity principal cells form the core of each memory along a first axis, segregating spatial contexts and novelty. Low-activity cells join co-activity motifs across behavioral events and enable their crosstalk along two other axes. This reveals an organizational principle for continuous integration and interaction of hippocampal memories.

Journal article

Go MA, Rogers J, Gava G, Davey C, Prado S, Liu Y, Schultz Set al., 2021, Place cells in head-fixed mice navigating a floating real-world environment, Frontiers in Cellular Neuroscience, Vol: 15, ISSN: 1662-5102

The hippocampal place cell system in rodents has provided a major paradigm for the scientific investigation of memory function and dysfunction. Place cells have been observed in area CA1 of the hippocampus of both freely moving animals, and of head-fixed animals navigating in virtual reality environments. However, spatial coding in virtual reality preparations has been observed to be impaired. Here we show that the use of a real-world environment system for head-fixed mice, consisting of an air-floating track with proximal cues, provides some advantages over virtual reality systems for the study of spatial memory. We imaged the hippocampus of head-fixed mice injected with the genetically encoded calcium indicator GCaMP6s while they navigated circularly constrained or open environments on the floating platform. We observed consistent place tuning in a substantial fraction of cells despite the absence of distal visual cues. Place fields remapped when animals entered a different environment. When animals re-entered the same environment, place fields typically remapped over a time period of multiple days, faster than in freely moving preparations, but comparable with virtual reality. Spatial information rates were within the range observed in freely moving mice. Manifold analysis indicated that spatial information could be extracted from a low-dimensional subspace of the neural population dynamics. This is the first demonstration of place cells in head-fixed mice navigating on an air-lifted real-world platform, validating its use for the study of brain circuits involved in memory and affected by neurodegenerative disorders.

Journal article

Jager P, Moore G, Calpin P, Durmishi X, Salgarella I, Menage L, Kita Y, Wang Y, Kim DW, Blackshaw S, Schultz SR, Brickley S, Shimogori T, Delogu Aet al., 2021, Dual midbrain and forebrain origins of thalamic inhibitory interneurons, eLife, Vol: 10, Pages: 1-29, ISSN: 2050-084X

The ubiquitous presence of inhibitory interneurons in the thalamus of primates contrasts with the sparsity of interneurons reported in mice. Here, we identify a larger than expected complexity and distribution of interneurons across the mouse thalamus, where all thalamic interneurons can be traced back to two developmental programmes: one specified in the midbrain and the other in the forebrain. Interneurons migrate to functionally distinct thalamocortical nuclei depending on their origin: the abundant, midbrain-derived class populates the first and higher order sensory thalamus while the rarer, forebrain-generated class is restricted to some higher order associative regions. We also observe that markers for the midbrain-born class are abundantly expressed throughout the thalamus of the New World monkey marmoset. These data therefore reveal that, despite the broad variability in interneuron density across mammalian species, the blueprint of the ontogenetic organisation of thalamic interneurons of larger-brained mammals exists and can be studied in mice.

Journal article

Mu Z, Nikolic K, Schultz SR, 2021, Quadratic Mutual Information estimation of mouse dLGN receptive fields reveals asymmetry between ON and OFF visual pathways., 10th International IEEE-EMBS Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 1134-1139, ISSN: 1948-3546

Conference paper

Mu Z, Nikolic K, Schultz SR, 2020, Quadratic Mutual Information estimation of mouse dLGN receptive fields reveals asymmetry between ON and OFF visual pathways

<jats:title>Abstract</jats:title><jats:p>The longstanding theory of “parallel processing” predicts that, except for a sign reversal, ON and OFF cells are driven by a similar pre-synaptic circuit and have similar visual field coverage, direction/orientation selectivity, visual acuity and other functional properties. However, recent experimental data challenges this view. Here we present an information theory based receptive field (RF) estimation method - quadratic mutual information (QMI) - applied to multi-electrode array electrophysiological recordings from the mouse dorsal lateral geniculate nucleus (dLGN). This estimation method provides more accurate RF estimates than the commonly used Spike-Triggered Average (STA) method, particularly in the presence of spatially correlated inputs. This improved efficiency allowed a larger number of RFs (285 vs 189 cells) to be extracted from a previously published dataset. Fitting a spatial-temporal Difference-of-Gaussians (ST-DoG) model to the RFs revealed that while the structural RF properties of ON and OFF cells are largely symmetric, there were some asymmetries apparent in the functional properties of ON and OFF visual processing streams - with OFF cells preferring higher spatial and temporal frequencies on average, and showing a greater degree of orientation selectivity.</jats:p>

Journal article

Márton C, Schultz S, Averbeck B, 2020, Learning to select actions shapes recurrent dynamics in the corticostriatal system, Neural Networks, Vol: 132, Pages: 375-393, ISSN: 0893-6080

Learning to select appropriate actions based on their values is fundamental to adaptive behavior. This form of learning is supported by fronto-striatal systems. The dorsal-lateral prefrontal cortex (dlPFC) and the dorsal striatum (dSTR), which are strongly interconnected, are key nodes in this circuitry. Substantial experimental evidence, including neurophysiological recordings, have shown that neurons in these structures represent key aspects of learning. The computational mechanisms that shape the neurophysiological responses, however, are not clear. To examine this, we developed a recurrent neural network (RNN) model of the dlPFC-dSTR circuit and trained it on an oculomotor sequence learning task. We compared the activity generated by the model to activity recorded from monkey dlPFC and dSTR in the same task. This network consisted of a striatal component which encoded action values, and a prefrontal component which selected appropriate actions. After training, this system was able to autonomously represent and update action values and select actions, thus being able to closely approximate the representational structure in corticostriatal recordings. We found that learning to select the correct actions drove action-sequence representations further apart in activity space, both in the model and in the neural data. The model revealed that learning proceeds by increasing the distance between sequence-specific representations. This makes it more likely that the model will select the appropriate action sequence as learning develops. Our model thus supports the hypothesis that learning in networks drives the neural representations of actions further apart, increasing the probability that the network generates correct actions as learning proceeds. Altogether, this study advances our understanding of how neural circuit dynamics are involved in neural computation, revealing how dynamics in the corticostriatal system support task learning.

Journal article

Lubba CH, Ouyang A, Jones N, Bruns T, Schultz Set al., 2020, Bladder pressure encoding by sacral dorsal root ganglion fibres: implications for decoding, Journal of Neural Engineering, Vol: 18, Pages: 1-19, ISSN: 1741-2552

Objective: We aim at characterising the encoding of bladder pressure (intravesical pressure) by a population of sensory fibres. This research is motivated by the possibility to restore bladder function in elderly patients or after spinal cord injury using implanted devices, so called bioelectronic medicines. For these devices, nerve-based estimation of intravesical pressure can enable a personalized and on-demand stimulation paradigm, which has promise of being more effective and efficient. In this context, a better understanding of the encoding strategies employed by the body might in the future be exploited by informed decoding algorithms that enable a precise and robust bladder-pressure estimation. Approach: To this end, we apply information theory to microelectrode-array recordings from the cat sacral dorsal root ganglion while filling the bladder, conduct surrogate data studies to augment the data we have, and finally decode pressure in a simple informed approach. Main results: We find an encoding scheme by different main bladder neuron types that we divide into three response types (slow tonic, phasic, and derivative fibres). We show that an encoding by different bladder neuron types, each represented by multiple cells, offers reliability through within-type redundancy and high information rates through semi-independence of different types. Our subsequent decoding study shows a more robust decoding from mean responses of homogeneous cell pools. Significance: We have here, for the first time, established a link between an information theoretic analysis of the encoding of intravesical pressure by a population of sensory neurons to an informed decoding paradigm. We show that even a simple adapted decoder can exploit the redundancy in the population to be more robust against cell loss. This work thus paves the way towards principled encoding studies in the periphery and towards a new generation of informed peripheral nerve decoders for bioelectronic medicines.

Journal article

Quicke P, Howe CL, Song P, Jadan HV, Song C, Knöpfel T, Neil M, Dragotti PL, Schultz SR, Foust AJet al., 2020, Subcellular resolution three-dimensional light-field imaging with genetically encoded voltage indicators, Neurophotonics, Vol: 7, ISSN: 2329-4248

Significance: Light-field microscopy (LFM) enables high signal-to-noise ratio (SNR) and light efficient volume imaging at fast frame rates. Voltage imaging with genetically encoded voltage indicators (GEVIs) stands to particularly benefit from LFM's volumetric imaging capability due to high required sampling rates and limited probe brightness and functional sensitivity. Aim: We demonstrate subcellular resolution GEVI light-field imaging in acute mouse brain slices resolving dendritic voltage signals in three spatial dimensions. Approach: We imaged action potential-induced fluorescence transients in mouse brain slices sparsely expressing the GEVI VSFP-Butterfly 1.2 in wide-field microscopy (WFM) and LFM modes. We compared functional signal SNR and localization between different LFM reconstruction approaches and between LFM and WFM. Results: LFM enabled three-dimensional (3-D) localization of action potential-induced fluorescence transients in neuronal somata and dendrites. Nonregularized deconvolution decreased SNR with increased iteration number compared to synthetic refocusing but increased axial and lateral signal localization. SNR was unaffected for LFM compared to WFM. Conclusions: LFM enables 3-D localization of fluorescence transients, therefore eliminating the need for structures to lie in a single focal plane. These results demonstrate LFM's potential for studying dendritic integration and action potential propagation in three spatial dimensions.

Journal article

Quicke P, Howe CL, Song P, Jadan HV, Song C, Knöpfel T, Neil M, Dragotti PL, Schultz SR, Foust AJet al., 2020, Subcellular resolution 3D light field imaging with genetically encoded voltage indicators, Neurophotonics, Vol: 7, ISSN: 2329-4248

Significance: Light-field microscopy (LFM) enables high signal-to-noise ratio (SNR) and light efficient volume imaging at fast frame rates. Voltage imaging with genetically encoded voltage indicators (GEVIs) stands to particularly benefit from LFM’s volumetric imaging capability due to high required sampling rates and limited probe brightness and functional sensitivity.Aim: We demonstrate subcellular resolution GEVI light-field imaging in acute mouse brain slices resolving dendritic voltage signals in three spatial dimensions.Approach: We imaged action potential-induced fluorescence transients in mouse brain slices sparsely expressing the GEVI VSFP-Butterfly 1.2 in wide-field microscopy (WFM) and LFM modes. We compared functional signal SNR and localization between different LFM reconstruction approaches and between LFM and WFM.Results: LFM enabled three-dimensional (3-D) localization of action potential-induced fluorescence transients in neuronal somata and dendrites. Nonregularized deconvolution decreased SNR with increased iteration number compared to synthetic refocusing but increased axial and lateral signal localization. SNR was unaffected for LFM compared to WFM.Conclusions: LFM enables 3-D localization of fluorescence transients, therefore eliminating the need for structures to lie in a single focal plane. These results demonstrate LFM’s potential for studying dendritic integration and action potential propagation in three spatial dimensions.

Journal article

Schultz S, Gava G, 2019, Neural codes – necessary but not sufficient for understanding brain function, Behavioral and Brain Sciences, Vol: 42, ISSN: 0140-525X

Brains are information processing systems, whose operational principles ultimately cannot be understood without resource to information theory. We suggest that understanding how external signals are represented in the brain is a necessary step towards employing further engineering tools (such as control theory) to understand the information processing performed by brain circuits during behaviour.

Journal article

Lubba CH, Sethi SS, Knaute P, Schultz SR, Fulcher BD, Jones NSet al., 2019, catch22: CAnonical time-series CHaracteristics, Data Mining and Knowledge Discovery, Vol: 33, Pages: 1821-1852, ISSN: 1384-5810

Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through systematic comparison across a comprehensive time-series feature library, such as those in the hctsa toolbox. However, this approach is computationally expensive and involves evaluating many similar features, limiting the widespread adoption of feature-based representations of time series for real-world applications. In this work, we introduce a method to infer small sets of time-series features that (i) exhibit strong classification performance across a given collection of time-series problems, and (ii) are minimally redundant. Applying our method to a set of 93 time-series classification datasets (containing over 147,000 time series) and using a filtered version of the hctsa feature library (4791 features), we introduce a set of 22 CAnonical Time-series CHaracteristics, catch22, tailored to the dynamics typically encountered in time-series data-mining tasks. This dimensionality reduction, from 4791 to 22, is associated with an approximately 1000-fold reduction in computation time and near linear scaling with time-series length, despite an average reduction in classification accuracy of just 7%. catch22 captures a diverse and interpretable signature of time series in terms of their properties, including linear and non-linear autocorrelation, successive differences, value distributions and outliers, and fluctuation scaling properties. We provide an efficient implementation of catch22, accessible from many programming environments, that facilitates feature-based time-series analysis for scientific, industrial, financial and medical applications using a common language of interpretable time-series properties.

Journal article

Tolkiehn M, Schultz SR, 2019, Temporo-nasally biased moving grating selectivity in mouse primary visual cortex

<jats:title>Abstract</jats:title><jats:p>Orientation tuning in mouse primary visual cortex (V1) has long been reported to have a random or “salt-and-pepper” organisation, lacking the structure found in cats and primates. Laminar in-vivo multi-electrode array recordings here reveal previously elusive structure in the representation of visual patterns in the mouse visual cortex, with temporo-nasally drifting gratings eliciting consistently highest neuronal responses across cortical layers and columns, whilst upward moving gratings reliably evoked the lowest activities. We suggest this bias in direction selectivity to be behaviourally relevant as objects moving into the visual field from the side or behind may pose a predatory threat to the mouse whereas upward moving objects do not. We found furthermore that direction preference and selectivity was affected by stimulus spatial frequency, and that spatial and directional tuning curves showed high signal correlations decreasing with distance between recording sites. In addition, we show that despite this bias in direction selectivity, it is possible to decode stimulus identity and that spatiotemporal features achieve higher accuracy in the decoding task whereas spike count or population counts are sufficient to decode spatial frequencies implying different encoding strategies.</jats:p><jats:sec><jats:title>Significance statement</jats:title><jats:p>We show that temporo-nasally drifting gratings (i.e. opposite the normal visual flow during forward movement) reliably elicit the highest neural activity in mouse primary visual cortex, whereas upward moving gratings reliably evoke the lowest responses. This encoding may be highly behaviourally relevant, as objects approaching from the periphery may pose a threat (e.g. predators), whereas upward moving objects do not. This is a result at odds with the belief that mouse primary visual cortex is randomly organised. Fur

Journal article

Tolkiehn M, Schultz SR, 2019, Neural ensemble activity depends on stimulus type in mouse primary visual cortex

<jats:title>ABSTRACT</jats:title><jats:p>Early cortical processing of visual information has long been investigated by describing the response properties such as receptive fields or orientation selectivity of individual neurons to moving gratings. However, thanks to recent technological advances, it has been become easier to record from larger neuronal populations which allow us to analyse the population responses to probe visual information processing at the population level. In the end, it is unlikely that sensory processing is a single-neuron effort but that of an entire population. Here we show how different stimulus types evoke distinct binary activity patterns (words) of simultaneous events on different sites in the anaesthetised mouse. Spontaneous activity and natural scenes indicated lower word distribution divergences than each to drifting gratings. Accounting for firing rate differences, spontaneous activity was linked to more unique patterns than stimulus-driven responses. Multidimensional scaling conveyed that pattern probability distributions clustered for spatial frequencies but not for directions. Further, drifting gratings modulated the Shannon entropy estimated on spatial patterns in a similar fashion as classical directional and spatial frequency tuning functions of neurons. This was supported by a distinct sublinear relationship between Shannon entropy and mean population firing rate.</jats:p>

Journal article

Márton CD, Schultz SR, Averbeck BB, 2019, Learning to select actions shapes recurrent dynamics in the corticostriatal system

<jats:title>Abstract</jats:title><jats:p>Learning to select appropriate actions based on their values is fundamental to adaptive behavior. This form of learning is supported by fronto-striatal systems. The dorsal-lateral prefrontal cortex (dlPFC) and the dorsal striatum (dSTR), which are strongly interconnected, are key nodes in this circuitry. Substantial experimental evidence, including neurophysiological recordings, have shown that neurons in these structures represent key aspects of learning. The computational mechanisms that shape the neurophysiological responses, however, are not clear. To examine this, we developed a recurrent neural network (RNN) model of the dlPFC-dSTR circuit and trained it on an oculomotor sequence learning task. We compared the activity generated by the model to activity recorded from monkey dlPFC and dSTR in the same task. This network consisted of a striatal component which encoded action values, and a prefrontal component which selected appropriate actions. After training, this system was able to autonomously represent and update action values and select actions, thus being able to closely approximate the representational structure in corticostriatal recordings. We found that learning to select the correct actions drove action-sequence representations further apart in activity space, both in the model and in the neural data. The model revealed that learning proceeds by increasing the distance between sequence-specific representations. This makes it more likely that the model will select the appropriate action sequence as learning develops. Our model thus supports the hypothesis that learning in networks drives the neural representations of actions further apart, increasing the probability that the network generates correct actions as learning proceeds. Altogether, this study advances our understanding of how neural circuit dynamics are involved in neural computation, showing how dynamics in the corticostriatal system

Working paper

Garasto S, Nicola W, Bharath A, Schultz Set al., 2019, Neural sampling strategies for visual stimulus reconstruction from two-photon imaging of mouse primary visual cortex, 2019 9th International IEEE/EMBS Conference on Neural Engineering(NER), Publisher: IEEE

Interpreting the neural code involves decoding the firing pattern of sensory neurons from the perspective of a downstream population. Performing such a read-out is an essential step for the understanding of sensory information processing in the brain and has implications for Brain-Machine Interfaces. While previous work has focused on classification algorithms to categorize stimuli using a predefined set of labels, less attention has been given to full-stimulus reconstruction, especially from calcium imaging recordings. Here, we attempt a pixel-by-pixel reconstruction of complex natural stimuli from two-photon calcium imaging of 103 neurons in layer 2/3 of mouse primary visual cortex. Using an optimal linear estimator, we investigated which factors drive the reconstruction performance at the pixel level. We find the density of receptive fields to be the most influential feature. Finally, we use the receptive field data and simulations from a linear-nonlinear Poisson model to extrapolate decoding accuracy as a function of network size. Based on our analysis on a public dataset, reconstruction performance using two-photon protocols might be considerably improved if the receptive fields are sampled more uniformly in the full visual field. These results provide practical experimental guidelines to boost the accuracy of full-stimulus reconstruction.

Conference paper

Lubba CH, Fulcher BD, Schultz SR, Jones NSet al., 2019, Efficient peripheral nerve firing characterisation through massive feature extraction, 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 179-182, ISSN: 1948-3546

Peripheral nerve decoding algorithms form an important component of closed-loop bioelectronic medicines devices. For any decoding method, meaningful properties need to be extracted from the peripheral nerve signal as the first step. Simple measures such as signal amplitude and features of the Fourier power spectrum are most typically used, leaving open whether important information is encoded in more subtle properties of the dynamics. We here propose a feature-based analysis method that identifies changes in firing characteristics across recording sections by unsupervised dimensionality reduction in a high-dimensional feature-space and selects single efficiently implementable estimators for each characteristic to be used as the basis for a better decoding in future bioelectronic medicines devices.

Conference paper

Quicke P, Song C, McKimm EJ, Milosevic MM, Howe CL, Neil M, Schultz SR, Antic SD, Foust AJ, Knopfel Tet al., 2019, Corrigendum: Single-neuron level one-photon voltage imaging with sparsely targeted genetically encoded voltage indicators, Frontiers in Cellular Neuroscience, Vol: 13, ISSN: 1662-5102

Voltage imaging of many neurons simultaneously at single-cell resolution is hampered bythe difficulty of detecting small voltage signals from overlapping neuronal processes inneural tissue. Recent advances in genetically encoded voltage indicator (GEVI) imaginghave shown single-cell resolution optical voltage recordings in intact tissue throughimaging naturally sparse cell classes, sparse viral expression, soma restricted expression,advanced optical systems, or a combination of these. Widespread sparse and strongtransgenic GEVI expression would enable straightforward optical access to a denselyoccurring cell type, such as cortical pyramidal cells. Here we demonstrate that a recentlydescribed sparse transgenic expression strategy can enable single-cell resolution voltageimaging of cortical pyramidal cells in intact brain tissue without restricting expression tothe soma. We also quantify the functional crosstalk in brain tissue and discuss optimalimaging rates to inform future GEVI experimental design.

Journal article

Lubba CH, Le Guen Y, Jarvis S, Jones NS, Cork SC, Eftekhar A, Schultz SRet al., 2019, Correction to: PyPNS: Multiscale Simulation of a Peripheral Nerve in Python., Neuroinformatics

The original version of this article unfortunately contained a mistake. The following text: "This project has received funding from European Research Council (ERC) Synergy Grant no. 319818." is missing in the Acknowledgments.

Journal article

Marton C, Fukushima M, Camalier C, Schultz S, Averbeck Bet al., 2019, Signature patterns for top-down and bottom-up information processing via cross-frequency coupling in macaque auditory cortex, eNeuro, Vol: 6, Pages: 1-14, ISSN: 2373-2822

Predictive coding is a theoretical framework that provides a functional interpretation of top-down and bottom-up interactions in sensory processing. The theory suggests there are differences in message passing up versus down the cortical hierarchy. These differences result from the linear feedforward of prediction errors, and the nonlinear feedback of predictions. This implies that cross-frequency interactions should predominate top-down. But it remains unknown whether these differences are expressed in cross-frequency interactions in the brain. Here we examined bidirectional cross-frequency coupling across four sectors of the auditory hierarchy in the macaque. We computed two measures of cross-frequency coupling, phase–amplitude coupling (PAC) and amplitude–amplitude coupling (AAC). Our findings revealed distinct patterns for bottom-up and top-down information processing among cross-frequency interactions. Both top-down and bottom-up interactions made prominent use of low frequencies: low-to-low-frequency (theta, alpha, beta) and low-frequency-to-high- gamma couplings were predominant top-down, while low-frequency-to-low-gamma couplings were predominant bottom-up. These patterns were largely preserved across coupling types (PAC and AAC) and across stimulus types (natural and synthetic auditory stimuli), suggesting that they are a general feature of information processing in auditory cortex. Our findings suggest the modulatory effect of low frequencies on gamma-rhythms in distant regions is important for bidirectional information transfer. The finding of low-frequency-to-low-gamma interactions in the bottom-up direction suggest that nonlinearities may also play a role in feedforward message passing. Altogether, the patterns of cross-frequency interaction we observed across the auditory hierarchy are largely consistent with the predictive coding framework.

Journal article

Soor N, Quicke P, Howe C, Pang KT, Neil M, Schultz S, Foust Aet al., 2019, All-optical crosstalk-free manipulation and readout of Chronos-expressing neurons, Journal of Physics D: Applied Physics, Vol: 52, Pages: 1-10, ISSN: 0022-3727

All optical neurophysiology allows manipulation and readout of neural network activity with single-cell spatial resolution and millisecond temporal resolution. Neurons can be made to express proteins that actuate transmembrane currents upon light absorption, enabling optical control of membrane potential and action potential signalling. In addition, neurons can be genetically or synthetically labelled with fluorescent reporters of changes in intracellular calcium concentration or membrane potential. Thus, to optically manipulate and readout neural activity in parallel, two spectra are involved: the action spectrum of the actuator, and the absorption spectrum of the fluorescent reporter. Due to overlap in these spectra, previous all-optical neurophysiology paradigms have been hindered by spurious activation of neuronal activity caused by the readout light. Here, we pair the blue-green absorbing optogenetic actuator, Chronos, with a deep red-emitting fluorescent calcium reporter CaSiR-1. We show that cultured Chinese hamster ovary cells transfected with Chronos do not exhibit transmembrane currents when illuminated with wavelengths and intensities suitable for exciting one-photon CaSiR-1 fluorescence. We then demonstrate crosstalk-free, high signal-to-noise ratio CaSiR-1 red fluorescence imaging at 100 frames s−1 of Chronos-mediated calcium transients evoked in neurons with blue light pulses at rates up to 20 Hz. These results indicate that the spectral separation between red light excited fluorophores, excited efficiently at or above 640 nm, with blue-green absorbing opsins such as Chronos, is sufficient to avoid spurious opsin actuation by the imaging wavelengths and therefore enable crosstalk-free all-optical neuronal manipulation and readout.

Journal article

Quicke P, Song C, McKimm EJ, Milosevic MM, Howe CL, Neil M, Schultz SR, Antic SD, Foust AJ, Knopfel Tet al., 2019, Single-neuron level one-photon voltage imaging with sparsely targeted genetically encoded voltage indicators, Frontiers in Cellular Neuroscience, Vol: 13, ISSN: 1662-5102

Voltage imaging of many neurons simultaneously at single-cell resolution is hampered by the difficulty of detecting small voltage signals from overlapping neuronal processes in neural tissue. Recent advances in genetically encoded voltage indicator (GEVI) imaging have shown single-cell resolution optical voltage recordings in intact tissue through imaging naturally sparse cell classes, sparse viral expression, soma restricted expression, advanced optical systems, or a combination of these. Widespread sparse and strong transgenic GEVI expression would enable straightforward optical access to a densely occurring cell type, such as cortical pyramidal cells. Here we demonstrate that a recently described sparse transgenic expression strategy can enable single-cell resolution voltage imaging of cortical pyramidal cells in intact brain tissue without restricting expression to the soma. We also quantify the functional crosstalk in brain tissue and discuss optimal imaging rates to inform future GEVI experimental design.

Journal article

Lubba CH, Sethi SS, Knaute P, Schultz SR, Fulcher BD, Jones NSet al., 2019, <i>catch22</i>: CAnonical Time-series CHaracteristics <i>selected through</i> highly comparative time-series analysis

<jats:title>Abstract</jats:title><jats:p>Capturing the dynamical properties of time series concisely as interpretable feature vectors can enable efficient clustering and classification for time-series applications across science and industry. Selecting an appropriate feature-based representation of time series for a given application can be achieved through systematic comparison across a comprehensive time-series feature library, such as those in the <jats:italic>hctsa</jats:italic> toolbox. However, this approach is computationally expensive and involves evaluating many similar features, limiting the widespread adoption of feature-based representations of time series for real-world applications. In this work, we introduce a method to infer small sets of time-series features that (i) exhibit strong classification performance across a given collection of time-series problems, and (ii) are minimally redundant. Applying our method to a set of 93 time-series classification datasets (containing over 147 000 time series, including biomedical datasets) and using a filtered version of the <jats:italic>hctsa</jats:italic> feature library (4791 features), we introduce a generically useful set of 22 CAnonical Time-series CHaracteristics, <jats:italic>catch22</jats:italic>. This dimensionality reduction, from 4791 to 22, is associated with an approximately 1000-fold reduction in computation time and near linear scaling with time-series length, despite an average reduction in classification accuracy of just 7%. <jats:italic>catch22</jats:italic> captures a diverse and interpretable signature of time series in terms of their properties, including linear and non-linear autocorrelation, successive differences, value distributions and outliers, and fluctuation scaling properties. We provide an efficient implementation of <jats:italic>catch22</jats:italic>, accessible from many programming environments, tha

Working paper

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