Dagmara Panas PhD

Dagmara Panas


Research Interests

The research interest that first drove me from Physics towards Computational Neuroscience was sleep. This area is still far beyond our full understanding and sports fascinating questions on all levels: from the macrostructure of EEG sleep stages to the cellular and molecular basis underlying (likely, but still hotly debated) memory consolidation. Especially fascinating to me was the intermediate level - the network dynamics underlying neural activity during sleep, which is what motivated my first research project (a Master's project with The University of Warsaw, under the supervision of Dr. Piotr Suffczynski): Statistical analysis of sleep spindles occurrences. Employing Matching Pursuit time-frequency analysis to extract spindles form EEG traces and Maximum Likelihood to estimate parameters of probability density distributions of spindle occurrences, I investigated whether two extant types of sleep spindles are generated by two distinct mechanisms. Mathematical models of thalamo-cortical interaction predict one type of spindles to occur randomly and another type of spindles to recur rhythmically in a deterministic manner, and probability distributions should reflect those two modes of generation. It transpired from the study that, while often topographically and morphologically distinct, sleep spindles could not reliably be distinguished by their statistical properties.

My interest in the behaviour of neural networks is strongly coupled with the interest in information processing and the emergent behavioural outcome, i.e. various aspects of cognition. In particular, I find the Bayesian approach in perception both useful and insightful - the idea of the brain functioning as a sub-optimal observer is simple, elegant, widely applicable and finds increasing support in studies. This, in turn, had motivated my Master's research project with the Doctoral Training Center, under the supervision of Dr Peggy Series: Specificity of prior expectations in visual statistical learning; a psychophysical study of the process of implicit acquisition of statistical knowledge. My aim was to assess whether the brain can detect statistical independence of features within simple stimuli and, if so, whether it uses the acquired information in a Bayesian manner. However, in our data it appeared that expectations acquisition was in competition with adaptation and this interfering effect rendered our results inconclusive.

While my interests cluster around various computational, modelling and statistical approaches in application to understanding human brain function, I was also keen to explore a more basic avenue of research. This has led me to the current doctoral project: Model-based analysis of stability in networks of neurons. Under the supervision of Dr. Matthias Hennig and co-supervised by Dr. Luca Berdondini I am investigating the spontaneous activity of cultured hippocampal neurons and the relationship between single-neuron and group stability. The results of this research are currently submitted and pending peer review.

Publications:
2015
  Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays
Muthmann, J-O, Amin, H, Sernagor, E, Maccione, A, Panas, D, Berdondini, L, Bhalla, US & Hennig, M 2015, 'Spike Detection for Large Neural Populations Using High Density Multielectrode Arrays' Frontiers in Neuroinformatics, vol 9, 28. DOI: 10.3389/fninf.2015.00028

An emerging generation of high-density microelectrode arrays (MEAs) is now capable of recording spiking activity simultaneously from thousands of neurons with closely spaced electrodes. Reliable spike detection and analysis in such recordings is challenging due to the large amount of raw data and the dense sampling of spikes with closely spaced electrodes. Here, we present a highly efficient, online capable spike detection algorithm, and an offline method with improved detection rates, which enables estimation of spatial event locations at a resolution higher than that provided by the array by combining information from multiple electrodes. Data acquired with a 4096 channel MEA from neuronal cultures and the neonatal retina, as well as synthetic data, was used to test and validate these methods. We demonstrate that these algorithms outperform conventional methods due to a better noise estimate and an improved signal-to-noise ratio (SNR) through combining information from multiple electrodes. Finally, we present a new approach for analyzing population activity based on the characterization of the spatio-temporal event profile, which does not require the isolation of single units. Overall, we show how the improved spatial resolution provided by high density, large scale MEAs can be reliably exploited to characterize activity from large neural populations and brain circuits.


General Information
Organisations: Edinburgh Neuroscience.
Authors: Muthmann, Jens-Oliver, Amin, Hayder, Sernagor, Evelyne, Maccione, Alessandro, Panas, Dagmara, Berdondini, Luca, Bhalla, Upinder S. & Hennig, Matthias.
Number of pages: 21
Publication Date: 18 Dec 2015
Publication Information
Category: Article
Journal: Frontiers in Neuroinformatics
Volume: 9
ISSN: 1662-5196
Original Language: English
DOIs: 10.3389/fninf.2015.00028
  Sloppiness in spontaneously active neuronal networks
Panas, D, Amin, H, Maccione, A, Muthmann, O, van Rossum, M, Berdondini, L & Hennig, MH 2015, 'Sloppiness in spontaneously active neuronal networks' Journal of Neuroscience, vol 35, no. 22, pp. 8480-92. DOI: 10.1523/JNEUROSCI.4421-14.2015

Various plasticity mechanisms, including experience-dependent, spontaneous, as well as homeostatic ones, continuously remodel neural circuits. Yet, despite fluctuations in the properties of single neurons and synapses, the behavior and function of neuronal assemblies are generally found to be very stable over time. This raises the important question of how plasticity is coordinated across the network. To address this, we investigated the stability of network activity in cultured rat hippocampal neurons recorded with high-density multielectrode arrays over several days. We used parametric models to characterize multineuron activity patterns and analyzed their sensitivity to changes. We found that the models exhibited sloppiness, a property where the model behavior is insensitive to changes in many parameter combinations, but very sensitive to a few. The activity of neurons with sloppy parameters showed faster and larger fluctuations than the activity of a small subset of neurons associated with sensitive parameters. Furthermore, parameter sensitivity was highly correlated with firing rates. Finally, we tested our observations from cell cultures on an in vivo recording from monkey visual cortex and we confirm that spontaneous cortical activity also shows hallmarks of sloppy behavior and firing rate dependence. Our findings suggest that a small subnetwork of highly active and stable neurons supports group stability, and that this endows neuronal networks with the flexibility to continuously remodel without compromising stability and function.


General Information
Organisations: Edinburgh Neuroscience.
Authors: Panas, Dagmara, Amin, Hayder, Maccione, Alessandro, Muthmann, Oliver, van Rossum, Mark, Berdondini, Luca & Hennig, Matthias H.
Number of pages: 13
Pages: 8480-92
Publication Date: 3 Jun 2015
Publication Information
Category: Article
Journal: Journal of Neuroscience
Volume: 35
Issue number: 22
ISSN: 0270-6474
Original Language: English
DOIs: 10.1523/JNEUROSCI.4421-14.2015
2013
  Homeostasis in large networks of neurons through the Ising model - do higher order interactions matter?
Panas, D, Maccione, A, Berdondini, L & Hennig, MH 2013, 'Homeostasis in large networks of neurons through the Ising model - do higher order interactions matter?' BMC Neuroscience, vol 14, no. Suppl 1, pp. P166. DOI: 10.1186/1471-2202-14-S1-P166
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Panas, Dagmara, Maccione, Alessandro, Berdondini, Luca & Hennig, Matthias H.
Pages: P166
Publication Date: 1 Jan 2013
Publication Information
Category: Article
Journal: BMC Neuroscience
Volume: 14
Issue number: Suppl 1
ISSN: 1471-2202
Original Language: English
DOIs: 10.1186/1471-2202-14-S1-P166
  Statistical Analysis of Sleep Spindle Occurrences
Panas, D, Malinowska, U, Piotrowski, T, Zygierewicz, J & Suffczynski, P 2013, 'Statistical Analysis of Sleep Spindle Occurrences' PLoS Neglected Tropical Diseases, vol 8, no. 4, pp. e59318. DOI: 10.1371/journal.pone.0059318

Spindles - a hallmark of stage II sleep - are a transient oscillatory phenomenon in the EEG believed to reflect thalamocortical activity contributing to unresponsiveness during sleep. Currently spindles are often classified into two classes: fast spindles, with a frequency of around 14 Hz, occurring in the centro-parietal region; and slow spindles, with a frequency of around 12 Hz, prevalent in the frontal region. Here we aim to establish whether the spindle generation process also exhibits spatial heterogeneity. Electroencephalographic recordings from 20 subjects were automatically scanned to detect spindles and the time occurrences of spindles were used for statistical analysis. Gamma distribution parameters were fit to each inter-spindle interval distribution, and a modified Wald-Wolfowitz lag-1 correlation test was applied. Results indicate that not all spindles are generated by the same statistical process, but this dissociation is not spindle-type specific. Although this dissociation is not topographically specific, a single generator for all spindle types appears unlikely.


General Information
Organisations: Neuroinformatics DTC.
Authors: Panas, Dagmara, Malinowska, Urszula, Piotrowski, Tadeusz, Zygierewicz, Jaroslaw & Suffczynski, Piotr.
Pages: e59318
Publication Date: 1 Apr 2013
Publication Information
Category: Article
Journal: PLoS Neglected Tropical Diseases
Volume: 8
Issue number: 4
ISSN: 1935-2727
Original Language: English
DOIs: 10.1371/journal.pone.0059318
2012
  Towards tracking homeostais on high-density multi-electrode arrays
Panas, D, Maccione, A, Berdondini, L & Hennig, M 2012, 'Towards tracking homeostais on high-density multi-electrode arrays' The Bernstein Conference on Computational Neuroscience 2012, Munich, Germany, 12/09/12 - 14/09/12, .
Homeostatic plasticity is one of the key mechanisms ensuring the remarkable adaptive abilities of the brain. However, this is still a relatively scantly explored branch of both experimental and computational neuroscience - in particular on a large, multi-neuronal scale. With recent advance in recording techniques, the lack of experimental data can be easily overcome – novel multielectrode arrays allow for high-density recordings from in vitro cultures consisting of thousands of neurons. What is needed to complement this rich data is analysis techniques that would be able to shed some light on the mechanism of the underlying process – in contrast to most conventional analysis techniques, such as firing rates, correlations or inter-burst intervals, which provide little more than descriptive information. In search for measures able to capture more complex phenomena, over the last decade a new approach has been developed - pairwise maximum entropy modelling (MaxEnt). It is a statistical model that fits two sets of parameters to explain the probability of spiking patterns in the network: individual neuron parameters that could be interpreted as excitability; and pairwise interaction parameters that could be interpreted as the functional connection strength between neurons. Successful application of this model to a variety of recordings has helped reevaluate the importance of neuronal interactions in shaping network activity (Schneidman et al., 2006; Shlens et al., 2006). Additionally, the shortcomings of MaxEnt in certain cases can serve as an indicator of higher-order interactions between neurons (Ohiorhenuan et al., 2010). In present work we examine the extent to which the statistics of MaxEnd model fits and parameters can assist in understanding different modes of activity of a neuronal culture – specifically, along the duration of a homeostatic experiment. Neural activity from primary neuron cultures was recorded with the 4096 channel Active Pixel Sensor (APS) MEA, allowing for reliable isolation of single unit activity at near-cellular resolution (Berdondini et al., 2009). 20-minute datasets were obtained at different stages of homeostatic compensation during and after long-term CNQX application. For the data sets with a stationary activity state, large numbers of four-unit MaxEnt models were constructed for randomly chosen neurons on two spatial scales. Comparison of the statistics of the fits and parameters across the scales and across conditions indicates that different activity modes exhibit different profiles of local clustering and higher-order interactions.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Panas, Dagmara, Maccione, Alessandro, Berdondini, Luca & Hennig, Matthias.
Publication Date: Sep 2012
Publication Information
Category: Poster
Original Language: English
2011
  What can MaxEnt reveal about high-density recordings and what can high-density recordings reveal about MaxEnt?
Panas, D, Maccione, A, Berdondini, L & Hennig, M 2011, 'What can MaxEnt reveal about high-density recordings and what can high-density recordings reveal about MaxEnt?' BMC Neuroscience, vol 12, no. Supplement 1, P146. DOI: 10.1186/1471-2202-12-S1-P146
Recent advances in neural recording techniques open exciting possibilities of better understanding whole populations of neurons. Devices such as APS MEA (Active Pixel Sensor Microelectrode Array) [1,2] allow for simultaneous recordings from 4096 channels (64x64 grid) at near-cellular resolution (electrode size: 21?m, electrode spacing: 42?m) and constitute a potentially very rich and detailed source of information on the dynamics of neural systems. Such volumes of data are however difficult to analyse: simple measures such as mean firing rates and correlations are often insufficient to capture interesting phenomena, while more sophisticated approaches can be computationally intensive and hard to interpret. Here we examine the applicability of pairwise maximum entropy (MaxEnt) [3-5] modelling to describe APS MEA data.

Pairwise maximum entropy model (equivalent to Ising model in physics), when fit to the data, yields a minimally structured probability distribution of network states that respects first and second order interactions. It is a convex, parsimonious and readily interpretable model that has been shown to characterize spiking patterns surprisingly robustly in many cases [3,4]. Additionally, it can provide a sensitive tool in detecting higher-order interactions. As reported in [5], the significant failure of the Ising model in close range (<300 ?m) uncovers a high-order processing mode in local clusters of neurons, a mode of processing absent on larger scale (>600 ?m) and undetectable with correlations.

In present work we examine the results and performance of the MaxEnt model fitting in different preparation types and parameter regimes; owing to high resolution recordings we can specifically focus on varying spatial scales. As can be seen in Fig.1, indeed even in cultured tissue data there are indicators of certain discrepancies between local populations and populations further apart. Firstly (panel A), it is in local populations where the advantage of Ising model over the independent model is most prominent. Secondly (panel B), the interactions within local populations reveal a different structure than those among groups of neurons spread further apart (Kolmogorov-Smirnov test, p<0.05); and, importantly, this is not a feature that can be shown by correlation analysis.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Panas, Dagmara, Maccione, Alessandro, Berdondini, Luca & Hennig, Matthias.
Number of pages: 2
Publication Date: 2011
Publication Information
Category: Meeting abstract
Journal: BMC Neuroscience
Volume: 12
Issue number: Supplement 1
ISSN: 1471-2202
Original Language: English
DOIs: 10.1186/1471-2202-12-S1-P146
  Beyond correlations in MEA recordings - how far can we go?
Panas, D, Maccione, A, Berdondini, L & Hennig, M 2011, 'Beyond correlations in MEA recordings - how far can we go?' Society for Neuroscience (SfN) 2011, Washington DC, United States, 12/11/11 - 16/11/11, .
Understanding interactions among neurons and the behaviour of whole neuronal populations is a necessary step to understanding brain function. Recent advances in experimental techniques (multielectrode array recordings, MEA) provide a wealth of data that could yield insight into the collective activity of large groups of neurons. However, most conventional analysis techniques, such as firing rates or correlations, are insufficient to draw far-reaching conclusions. In search for measures able to capture more complex phenomena, over the last decade a new approach has been developed - pairwise maximum entropy modelling (MaxEnt). It is a statistical model that fits two sets of parameters to explain the probability of spiking patterns in the network: individual neuron parameters that could be interpreted as excitability; and pairwise interaction parameters that could be interpreted as connection strength between neurons. Successful application of this model to a variety of recordings has shown that, despite low correlation values, neuronal interactions play an important role in shaping network activity (Schneidman et al., 2006). Additionally, the failure of MaxEnt in certain cases could be an indicator of higher-order interactions between neurons (Ohiorhenuan et al., 2010).
In present work we examine the performance of the MaxEnt model in application to novel, high-density recordings. Neural activity from primary neuron cultures was recorded with the 4096 channel Active Pixel Sensor (APS) MEA, allowing for reliable isolation of single unit activity at near-cellular resolution (Berdondini et al., 2009). Such data allows us to explore the stability of the model, its performance on varying spatial scales and utility in providing information about the network. To this end, large numbers of four-unit MaxEnt models are constructed for randomly chosen neurons on two spatial scales. Results of the fits indicate that the model is able to detect a difference in interaction strengths between groups of nearby neurons and those further apart. Additionally, it appears that the advantage of MaxEnt over independent model is more apparent in close range. Those results could be interpreted as an indicator of local clustering.
General Information
Organisations: Institute for Adaptive and Neural Computation .
Authors: Panas, Dagmara, Maccione, Alessandro, Berdondini, Luca & Hennig, Matthias.
Publication Date: 2011
Publication Information
Category: Poster
Original Language: English

Projects:
Model-based analysis of stability in networks of neurons (PhD)