scholarly journals Interpreting Wide-Band Neural Activity Using Convolutional Neural Networks

2019 ◽  
Author(s):  
Markus Frey ◽  
Sander Tanni ◽  
Catherine Perrodin ◽  
Alice O’Leary ◽  
Matthias Nau ◽  
...  
Keyword(s):  
Calcium Imaging ◽  
Wide Band ◽  
Brain Regions ◽  
Head Direction ◽  
Rapid Progress ◽  
Neural Data ◽  
User Input ◽  
Neural Recordings ◽  
Learning Framework ◽  
Using Data

AbstractRapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data often depends on manual operations and requires considerable knowledge about the nature of the representation. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning-framework able to decode sensory and behavioural variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviours, brain regions, and recording techniques. Once trained, it can be analysed to determine elements of the neural code that are informative about a given variable. We validated this approach using data from rodent auditory cortex and hippocampus, identifying a novel representation of head direction encoded by putative CA1 interneurons.

scholarly journals Interpreting wide-band neural activity using convolutional neural networks

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Markus Frey ◽  
Sander Tanni ◽  
Catherine Perrodin ◽  
Alice O'Leary ◽  
Matthias Nau ◽  
...  
Keyword(s):  
Neural Activity ◽  
Calcium Imaging ◽  
Wide Band ◽  
Brain Regions ◽  
Imaging Data ◽  
User Input ◽  
Neural Recordings ◽  
Learning Framework ◽  
Behavioral Variables ◽  
Spatial Behaviors

Rapid progress in technologies such as calcium imaging and electrophysiology has seen a dramatic increase in the size and extent of neural recordings. Even so, interpretation of this data requires considerable knowledge about the nature of the representation and often depends on manual operations. Decoding provides a means to infer the information content of such recordings but typically requires highly processed data and prior knowledge of the encoding scheme. Here, we developed a deep-learning framework able to decode sensory and behavioral variables directly from wide-band neural data. The network requires little user input and generalizes across stimuli, behaviors, brain regions, and recording techniques. Once trained, it can be analyzed to determine elements of the neural code that are informative about a given variable. We validated this approach using electrophysiological and calcium-imaging data from rodent auditory cortex and hippocampus as well as human electrocorticography (ECoG) data. We show successful decoding of finger movement, auditory stimuli, and spatial behaviors – including a novel representation of head direction - from raw neural activity.

scholarly journals Inferring brain-wide interactions using data-constrained recurrent neural network models

2020 ◽  
Author(s):  
Matthew G. Perich ◽  
Charlotte Arlt ◽  
Sofia Soares ◽  
Megan E. Young ◽  
Clayton P. Mosher ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Large Scale ◽  
Computational Models ◽  
Network Models ◽  
Brain Regions ◽  
Neural Network Models ◽  
Neural Data ◽  
Neural Recordings ◽  
Using Data

ABSTRACTBehavior arises from the coordinated activity of numerous anatomically and functionally distinct brain regions. Modern experimental tools allow unprecedented access to large neural populations spanning many interacting regions brain-wide. Yet, understanding such large-scale datasets necessitates both scalable computational models to extract meaningful features of interregion communication and principled theories to interpret those features. Here, we introduce Current-Based Decomposition (CURBD), an approach for inferring brain-wide interactions using data-constrained recurrent neural network models that directly reproduce experimentally-obtained neural data. CURBD leverages the functional interactions inferred by such models to reveal directional currents between multiple brain regions. We first show that CURBD accurately isolates inter-region currents in simulated networks with known dynamics. We then apply CURBD to multi-region neural recordings obtained from mice during running, macaques during Pavlovian conditioning, and humans during memory retrieval to demonstrate the widespread applicability of CURBD to untangle brain-wide interactions underlying behavior from a variety of neural datasets.

scholarly journals Decoding of neural data using cohomological learning

2018 ◽  
Author(s):  
Erik Rybakken ◽  
Nils Baas ◽  
Benjamin Dunn
Keyword(s):  
Spatial Organization ◽  
Large Population ◽  
Dimensional Structure ◽  
Neural Population ◽  
Head Direction ◽  
Population Activity ◽  
Neural Data ◽  
Neural Recordings ◽  
Data Driven Approach ◽  
Low Dimensional

AbstractWe introduce a novel data-driven approach to discover and decode features in the neural code coming from large population neural recordings with minimal assumptions, using cohomological learning. We apply our approach to neural recordings of mice moving freely in a box, where we find a circular feature. We then observe that the decoded value corresponds well to the head direction of the mouse. Thus we capture head direction cells and decode the head direction from the neural population activity without having to process the behaviour of the mouse. Interestingly, the decoded values convey more information about the neural activity than the tracked head direction does, with differences that have some spatial organization. Finally, we note that the residual population activity, after the head direction has been accounted for, retains some low-dimensional structure which is correlated with the speed of the mouse.

scholarly journals T-SNE visualization of large-scale neural recordings

2016 ◽  
Author(s):  
George Dimitriadis ◽  
Joana Neto ◽  
Adam R. Kampff
Keyword(s):  
Large Scale ◽  
New Technologies ◽  
Dimensional Space ◽  
Clustering Algorithms ◽  
Brain Regions ◽  
Data Sets ◽  
Neural Recordings ◽  
Sorting Problem ◽  
Feature Spaces ◽  
High Degree

AbstractElectrophysiology is entering the era of ‘Big Data’. Multiple probes, each with hundreds to thousands of individual electrodes, are now capable of simultaneously recording from many brain regions. The major challenge confronting these new technologies is transforming the raw data into physiologically meaningful signals, i.e. single unit spikes. Sorting the spike events of individual neurons from a spatiotemporally dense sampling of the extracellular electric field is a problem that has attracted much attention [22, 23], but is still far from solved. Current methods still rely on human input and thus become unfeasible as the size of the data sets grow exponentially.Here we introduce the t-student stochastic neighbor embedding (t-sne) dimensionality reduction method [27] as a visualization tool in the spike sorting process. T-sne embeds the n-dimensional extracellular spikes (n = number of features by which each spike is decomposed) into a low (usually two) dimensional space. We show that such embeddings, even starting from different feature spaces, form obvious clusters of spikes that can be easily visualized and manually delineated with a high degree of precision. We propose that these clusters represent single units and test this assertion by applying our algorithm on labeled data sets both from hybrid [23] and paired juxtacellular/extracellular recordings [15]. We have released a graphical user interface (gui) written in python as a tool for the manual clustering of the t-sne embedded spikes and as a tool for an informed overview and fast manual curration of results from other clustering algorithms. Furthermore, the generated visualizations offer evidence in favor of the use of probes with higher density and smaller electrodes. They also graphically demonstrate the diverse nature of the sorting problem when spikes are recorded with different methods and arise from regions with different background spiking statistics.

scholarly journals Spatially structured inhibition defined by polarized parvalbumin interneuron axons promotes head direction tuning

2021 ◽  
Vol 7 (25) ◽  
pp. eabg4693
Author(s):  
Yangfan Peng ◽  
Federico J. Barreda Tomas ◽  
Paul Pfeiffer ◽  
Moritz Drangmeister ◽  
Susanne Schreiber ◽  
...  
Keyword(s):  
Spatial Organization ◽  
Pyramidal Cells ◽  
Brain Regions ◽  
Head Direction ◽  
Spatial Connectivity ◽  
Parvalbumin Interneurons ◽  
Parvalbumin Interneuron ◽  
Network Simulations ◽  
Fast Spiking ◽  
Direction Tuning

In cortical microcircuits, it is generally assumed that fast-spiking parvalbumin interneurons mediate dense and nonselective inhibition. Some reports indicate sparse and structured inhibitory connectivity, but the computational relevance and the underlying spatial organization remain unresolved. In the rat superficial presubiculum, we find that inhibition by fast-spiking interneurons is organized in the form of a dominant super-reciprocal microcircuit motif where multiple pyramidal cells recurrently inhibit each other via a single interneuron. Multineuron recordings and subsequent 3D reconstructions and analysis further show that this nonrandom connectivity arises from an asymmetric, polarized morphology of fast-spiking interneuron axons, which individually cover different directions in the same volume. Network simulations assuming topographically organized input demonstrate that such polarized inhibition can improve head direction tuning of pyramidal cells in comparison to a “blanket of inhibition.” We propose that structured inhibition based on asymmetrical axons is an overarching spatial connectivity principle for tailored computation across brain regions.

scholarly journals GWAS of Depression Phenotypes in the Million Veteran Program and Meta-analysis in More than 1.2 Million Participants Yields 178 Independent Risk Loci

2020 ◽  
Author(s):  
Daniel F Levey ◽  
Murray B Stein ◽  
Frank R Wendt ◽  
Gita A Pathak ◽  
Hang Zhou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Meta Analysis ◽  
African Ancestry ◽  
Genetic Correlations ◽  
Brain Regions ◽  
Self Report ◽  
European Ancestry ◽  
Causal Variants ◽  
Using Data ◽  
Genomic Risk

We report a large meta-analysis of depression using data from the Million Veteran Program (MVP), 23andMe Inc., UK Biobank, and FinnGen; including individuals of European ancestry (n=1,154,267; 340,591 cases) and African ancestry (n=59,600; 25,843 cases). We identified 223 and 233 independent SNPs associated with depression in European ancestry and transancestral analysis, respectively. Genetic correlations within the MVP cohort across electronic health records diagnosis, survey self-report of diagnosis, and a 2-item depression screen exceeded 0.81. Using transcriptome-wide association study (TWAS) we found significant associations for gene expression in several brain regions, including hypothalamus (NEGR1, p=3.19x10-25) and nucleus accumbens (DRD2, p=1.87x10-20). 178 genomic risk loci were fine-mapped to find likely causal variants. We identified likely pathogenicity in these variants and overlapping gene expression for 17 genes from our TWAS, including TRAF3. This study sheds light on the genetic architecture of depression and provides new insight into the interrelatedness of complex psychiatric traits.

scholarly journals Leveraging multi-way interactions for systematic prediction of pre-clinical drug combination effects

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Heli Julkunen ◽  
Anna Cichonska ◽  
Prson Gautam ◽  
Sandor Szedmak ◽  
Jane Douat ◽  
...  
Keyword(s):  
Effective Means ◽  
Cancer Cell Line ◽  
Predictive Performance ◽  
Drug Combinations ◽  
Precision Oncology ◽  
Learning Framework ◽  
Anaplastic Lymphoma ◽  
Higher Order Tensors ◽  
Using Data ◽  
Context Specific

AbstractWe present comboFM, a machine learning framework for predicting the responses of drug combinations in pre-clinical studies, such as those based on cell lines or patient-derived cells. comboFM models the cell context-specific drug interactions through higher-order tensors, and efficiently learns latent factors of the tensor using powerful factorization machines. The approach enables comboFM to leverage information from previous experiments performed on similar drugs and cells when predicting responses of new combinations in so far untested cells; thereby, it achieves highly accurate predictions despite sparsely populated data tensors. We demonstrate high predictive performance of comboFM in various prediction scenarios using data from cancer cell line pharmacogenomic screens. Subsequent experimental validation of a set of previously untested drug combinations further supports the practical and robust applicability of comboFM. For instance, we confirm a novel synergy between anaplastic lymphoma kinase (ALK) inhibitor crizotinib and proteasome inhibitor bortezomib in lymphoma cells. Overall, our results demonstrate that comboFM provides an effective means for systematic pre-screening of drug combinations to support precision oncology applications.

scholarly journals Oscillatory synchrony between head direction cells recorded bilaterally in the anterodorsal thalamic nuclei

2017 ◽  
Vol 117 (5) ◽  
pp. 1847-1852 ◽  
Author(s):  
William N. Butler ◽  
Jeffrey S. Taube
Keyword(s):  
High Frequency ◽  
Brain Regions ◽  
High Frequency Oscillation ◽  
Head Direction ◽  
Thalamic Nuclei ◽  
Head Direction Cells ◽  
Oscillatory Pattern ◽  
Cross Correlations ◽  
Left And Right ◽  
The Cross

The head direction (HD) circuit is a complex interconnected network of brain regions ranging from the brain stem to the cortex. Recent work found that HD cells corecorded ipsilaterally in the anterodorsal nucleus (ADN) of the thalamus displayed coordinated firing patterns. A high-frequency oscillation pattern (130–160 Hz) was visible in the cross-correlograms of these HD cell pairs. Spectral analysis further found that the power of this oscillation was greatest at 0 ms and decreased at greater lags, and demonstrated that there was greater synchrony between HD cells with similar preferred firing directions. Here, we demonstrate that the same high-frequency synchrony exists in HD cell pairs recorded contralaterally from one another in the bilateral ADN. When we examined the cross-correlograms of HD cells that were corecorded bilaterally, we observed the same high-frequency (~150- to 200-Hz) oscillatory relationship. The strength of this synchrony was similar to the synchrony seen in ipsilateral HD cell pairs, and the degree of synchrony in each cross-correlogram was dependent on the difference in tuning between the two cells. Additionally, the frequency rate of this oscillation appeared to be independent of the firing rates of the two cross-correlated cells. Taken together, these results imply that the left and right thalamic HD network are functionally related despite an absence of direct anatomical projections. However, anatomical tracing has found that each of the lateral mammillary nuclei (LMN) project bilaterally to both of the ADN, suggesting the LMN may be responsible for the functional connectivity observed between the two ADN. NEW & NOTEWORTHY This study used bilateral recording electrodes to examine whether head direction cells recorded simultaneously in both the left and right thalamus show coordinated firing. Cross-correlations of the cells’ spike trains revealed a high-frequency oscillatory pattern similar to that seen in cross-correlations between pairs of ipsilateral head direction cells, demonstrating that the bilateral thalamic head direction signals may be part of a single unified network.

scholarly journals Multiple spatial codes for navigating 2-D semantic spaces

2020 ◽  
Author(s):  
Simone Viganò ◽  
Valerio Rubino ◽  
Antonio Di Soccio ◽  
Marco Buiatti ◽  
Manuela Piazza
Keyword(s):  
Physical Space ◽  
Semantic Space ◽  
Brain Regions ◽  
Head Direction ◽  
Word Meanings ◽  
Neuronal Coding ◽  
Coding Schemes ◽  
Direction Of Movement ◽  
Human Participants ◽  
The Right

SummaryWhen mammals navigate in the physical environment, specific neurons such as grid-cells, head-direction cells, and place-cells activate to represent the navigable surface, the faced direction of movement, and the specific location the animal is visiting. Here we test the hypothesis that these codes are also activated when humans navigate abstract language-based representational spaces. Human participants learnt the meaning of novel words as arbitrary signs referring to specific artificial audiovisual objects varying in size and sound. Next, they were presented with sequences of words and asked to process them semantically while we recorded the activity of their brain using fMRI. Processing words in sequence was conceivable as movements in the semantic space, thus enabling us to systematically search for the different types of neuronal coding schemes known to represent space during navigation. By applying a combination of representational similarity and fMRI-adaptation analyses, we found evidence of i) a grid-like code in the right postero-medial entorhinal cortex, representing the general bidimensional layout of the novel semantic space; ii) a head-direction-like code in parietal cortex and striatum, representing the faced direction of movements between concepts; and iii) a place-like code in medial prefrontal, orbitofrontal, and mid cingulate cortices, representing the Euclidean distance between concepts. We also found evidence that the brain represents 1-dimensional distances between word meanings along individual sensory dimensions: implied size was encoded in secondary visual areas, and implied sound in Heschl’s gyrus/Insula. These results reveal that mentally navigating between 2D word meanings is supported by a network of brain regions hosting a variety of spatial codes, partially overlapping with those recruited for navigation in physical space.

Sign in / Sign up

Export Citation Format

Share Document