Anna Chambers

Chambers, A.R.*, Salazar, J.J.*, Polley, D.B. (2016). Persistent thalamic sound processing despite profound cochlear denervation. Front. Neural Circuits. 10:72. doi: 10.3389/fncir.2016.00072. *equal author contribution

Sloas, D.C., Zhuo, R., Xue, H., Chambers, A.R., Kolaczyk, E., Polley, D.B., Sen, K. (2016) Interactions across multiple stimulus dimensions in primary auditory cortex. eNeuro, Aug 2016, ENEURO.0124-16.2016.

Chambers, A.R., Resnik, J., Whitton, J.P., Yuan, Y., Edge, A.S., Liberman, M.C., Polley, D.B. (2016).  Central gain restores auditory processing following near-complete cochlear denervation. Neuron, 89(4); 867-79.

Chambers, A.R., Hancock, K.E., Sen, K., Polley, D.B. (2014). Online stimulus optimization rapidly reveals multidimensional selectivity in auditory cortical neurons. J. Neurosci., 34, 8963-75.

Guo, W., Chambers, A.R., Darrow, K.N., Hancock, K.E., Shinn-Cunningham, B.G., Polley, D.B. (2012). Robustness of cortical topography across fields, laminae, anesthetic states, and neurophysiological signal types. J. Neurosci., 32, 9159-72.

Chambers, A.R., Hancock, K.E., Maison, S.F., Liberman, M.C., Polley, D.B. (2012). Sound-evoked olivocochlear activation in unanesthetized mice. J. Assoc. Res. Otolaryngol., 13, 209-17.

• Hagen, Espen; Chambers, Anna; Pettersen, Klas H.; Einevoll, Gaute; Enger, Rune & Stasik, Alexander Johannes (2021). RippleNet: a Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection. Neuroinformatics.  ISSN 1539-2791. . doi: 10.1007/s12021-020-09496-2 Vis sammendrag

  Hippocampal sharp wave ripples (SPW-R) have been identified as key bio-markers of important brain functions such as memory consolidation and decision making. Understanding their underlying mechanisms in healthy and pathological brain function and behaviour rely on accurate SPW-R detection. In this multidisciplinary study, we propose a novel, self-improving artificial intelligence (AI) detection method in the form of deep Recurrent Neural Networks (RNN) with Long Short-Term memory (LSTM) layers that can learn features of SPW-R events from raw, labeled input data. The approach contrasts conventional routines that typically relies on hand-crafted, heuristic feature extraction and often laborious manual curation. The algorithm is trained using supervised learning on hand-curated data sets with SPW-R events obtained under controlled conditions. The input to the algorithm is the local field potential (LFP), the low-frequency part of extracellularly recorded electric potentials from the CA1 region of the hippocampus. Its output predictions can be interpreted as time-varying probabilities of SPW-R events for the duration of the inputs. A simple thresholding applied to the output probabilities is found to identify times of SPW-R events with high precision. The non-causal, or bidirectional variant of the proposed algorithm demonstrates consistently better accuracy compared to the causal, or unidirectional counterpart. Reference implementations of the algorithm, named ‘RippleNet’, are open source, freely available, and implemented using a common open-source framework for neural networks (tensorflow.keras) and can be easily incorporated into existing data analysis workflows for processing experimental data.

• Bojarskaite, Laura; Bjørnstad, Daniel Marelius; Pettersen, Klas; Cunen, Celine; Hermansen, Gudmund Horn; Åbjørsbråten, Knut Sindre; Chambers, Anna; Sprengel, Rolf; Vervaeke, Koen Gerard Alois; Tang, Wannan; Enger, Rune & Nagelhus, Erlend Arnulf (2020). Astrocytic Ca2+ signaling is reduced during sleep and is involved in the regulation of slow wave sleep. Nature Communications.  ISSN 2041-1723. . doi: 10.1038/s41467-020-17062-2 Fulltekst i vitenarkiv.
• Hagen, Espen; Chambers, Anna; Einevoll, Gaute; Pettersen, Klas Henning; Enger, Rune & Stasik, Alexander Johannes (2020). RippleNet: A Recurrent Neural Network for Sharp Wave Ripple (SPW-R) Detection. bioRxiv - the preprint server for biology. . doi: 10.1101/2020.05.11.087874 Vis sammendrag

  Hippocampal sharp wave ripples (SPW-R) have been identified as key bio-markers of important brain functions such as memory consolidation and decision making. SPW-R detection typically relies on hand-crafted feature extraction, and laborious manual curation is often required. In this multidisciplinary study, we propose a novel, self-improving artificial intelligence (AI) method in the form of deep Recurrent Neural Networks (RNN) with Long Short-Term memory (LSTM) layers that can learn features of SPW-R events from raw, labeled input data. The algorithm is trained using supervised learning on hand-curated data sets with SPW-R events. The input to the algorithm is the local field potential (LFP), the low-frequency part of extracellularly recorded electric potentials from the CA1 region of the hippocampus. The output prediction can be interpreted as the time-varying probability of SPW-R events for the duration of the input. A simple thresholding applied to the output probabilities is found to identify times of events with high precision. The reference implementation of the algorithm, named ‘RippleNet’, is open source, freely available, and implemented using a common open-source framework for neural networks (tensorflow.keras) and can be easily incorporated into existing data analysis workflows for processing experimental data.

• Chambers, Anna; Pilati, Nadia; Balaram, Pooja; Large, Charles H; Kaczmarek, Leonard K & Polley, Daniel B. (2017). Pharmacological modulation of Kv3.1 mitigates auditory midbrain temporal processing deficits following auditory nerve damage. Scientific Reports.  ISSN 2045-2322.  7(1) . doi: 10.1038/s41598-017-17406-x
• Chambers, Anna & Rumpel, Simon (2017). A stable brain from unstable components: Emerging concepts and implications for neural computation. Neuroscience.  ISSN 0306-4522.  357, s 172- 184 . doi: 10.1016/j.neuroscience.2017.06.005
• Chambers, Anna; Salazar, Juan & Polley, Daniel B. (2016). Persistent thalamic sound processing despite profound cochlear denervation. Frontiers in Neural Circuits.  ISSN 1662-5110.  10:72, s 1- 13 . doi: 10.3389/fncir.2016.00072

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