@article{Schmitt2022,
author = {Schmitt, Felix Johannes and Rostami, Vahid and Nawrot, Martin Paul},
doi = {10.3389/fninf.2023.941696},
journal = {Frontiers in Neuroinformatics},
keywords = {artificial agents,attractor neural network,computational neuroethology,computational neuroscience,metastability,neocortex,real-time simulation,spiking neural network},
month = {jan},
pages = {941696},
title = {{Efficient parameter calibration and real-time simulation of large scale spiking neural networks with GeNN and NEST}},
url = {https://www.frontiersin.org/articles/10.3389/fninf.2023.941696/abstract},
volume = {17},
year = {2023}
}

@article{Rostami2020,
abstract = {Both neural activity and behavior of highly trained animals are strikingly variable across repetition of behavioral trials. The neural variability consistently decreases during behavioral tasks, in both sensory and motor cortices. The behavioral variability, on the other hand, changes depending on the difficulty of the task and animal performance. Here we study a mechanism for such variability in spiking neural network models with cluster topologies that enable multistability and attractor dynamics, features subserving functional roles suchas decision-making, (working) memory and learning. Multistable attractors have been studied in spiking neural networks through clusters of strongly interconnected excitatory neurons. However, we show that this network topology results in the loss of excitation/inhibition balance and does not confer robustness against modulation of network activity. Moreover, it leads to widely separated firing rate states of single neurons, inconsistent with experimental observations. To overcome these problems we propose that a combination of excitatory and inhibitory clustering restores local excitation/inhibition balance. This network architecture is inspired by recent anatomicaland physiological studies which point to increased local inhibitory connectivity and possible inhibitory clustering through connection strengths. We find that inhibitory clustering supports realistic spiking activity in terms of a biologically realistic firing rate, spiking irregularity, and trial-to-trial spike count variability. Furthermore, with the appropriate stimulation of network clusters, this network topology enabled us to qualitatively and quantitatively reproduce in vivo firing rate, variability dynamics and behavioral reaction times for different task conditions as observed in recordings from the motor cortex of behaving monkeys.},
author = {Rostami, Vahid and Rost, Thomas and Riehle, Alexa and van Albada, Sacha J. and Nawrot, Martin P.},
doi = {10.1101/2020.02.27.968339},
file = {:Users/springer/Library/Application Support/Mendeley Desktop/Downloaded/Rostami et al. - 2020 - Spiking neural network model of motor cortex with joint excitatory and inhibitory clusters reflects task uncerta.pdf:pdf},
journal = {bioRxiv},
month = {feb},
pages = {2020.02.27.968339},
publisher = {Cold Spring Harbor Laboratory},
title = {{Spiking neural network model of motor cortex with joint excitatory and inhibitory clusters reflects task uncertainty, reaction times, and variability dynamics}},
year = {2020}
}

@article{Seeholzer2019,
abstract = {Continuous attractor models of working-memory store continuous-valued information in continuous state-spaces, but are sensitive to noise processes that degrade memory retention. Short-term synaptic plasticity of recurrent synapses has previously been shown to affect continuous attractor systems: short-term facilitation can stabilize memory retention, while short-term depression possibly increases continuous attractor volatility. Here, we present a comprehensive description of the combined effect of both short-term facilitation and depression on noise-induced memory degradation in one-dimensional continuous attractor models. Our theoretical description, applicable to rate models as well as spiking networks close to a stationary state, accurately describes the slow dynamics of stored memory positions as a combination of two processes: (i) diffusion due to variability caused by spikes; and (ii) drift due to random connectivity and neuronal heterogeneity. We find that facilitation decreases both diffusion and directed drifts, while short-term depression tends to increase both. Using mutual information, we evaluate the combined impact of short-term facilitation and depression on the ability of networks to retain stable working memory. Finally, our theory predicts the sensitivity of continuous working memory to distractor inputs and provides conditions for stability of memory.},
author = {Seeholzer, Alexander and Deger, Moritz and Gerstner, Wulfram},
doi = {10.1371/journal.pcbi.1006928},
file = {:Users/springer/Library/Application Support/Mendeley Desktop/Downloaded/Seeholzer, Deger, Gerstner - 2019 - Stability of working memory in continuous attractor networks under the control of shortterm plastici.pdf:pdf},
issn = {15537358},
journal = {PLoS Computational Biology},
number = {4},
pages = {e1006928},
publisher = {Public Library of Science},
title = {{Stability of working memory in continuous attractor networks under the control of shortterm plasticity}},
volume = {15},
year = {2019}
}

@article{Setareh2018,
abstract = {The time scale of neuronal network dynamics is determined by synaptic interactions and neuronal signal integration, both of which occur on the time scale of milliseconds. Yet many behaviors like the generation of movements or vocalizations of sounds occur on the much slower time scale of seconds. Here we ask the question of how neuronal networks of the brain can support reliable behavior on this time scale. We argue that excitable neuronal assemblies with spike-frequency adaptation may serve as building blocks that can flexibly adjust the speed of execution of neural circuit function. We show in simulations that a chain of neuronal assemblies can propagate signals reliably, similar to the well-known synfire chain, but with the crucial difference that the propagation speed is slower and tunable to the behaviorally relevant range. Moreover we study a grid of excitable neuronal assemblies as a simplified model of the somatosensory barrel cortex of the mouse and demonstrate that various patterns of experimentally observed spatial activity propagation can be explained.},
author = {Setareh, Hesam and Deger, Moritz and Gerstner, Wulfram},
doi = {10.1371/journal.pcbi.1006216},
file = {::},
issn = {15537358},
journal = {PLoS Computational Biology},
month = {jul},
number = {7},
pages = {e1006216},
publisher = {Public Library of Science},
title = {{Excitable neuronal assemblies with adaptation as a building block of brain circuits for velocity-controlled signal propagation}},
volume = {14},
year = {2018}
}

@article{Rost2018,
abstract = {{\textcopyright} 2017 The Author(s) Balanced networks are a frequently employed basic model for neuronal networks in the mammalian neocortex. Large numbers of excitatory and inhibitory neurons are recurrently connected so that the numerous positive and negative inputs that each neuron receives cancel out on average. Neuronal firing is therefore driven by fluctuations in the input and resembles the irregular and asynchronous activity observed in cortical in vivo data. Recently, the balanced network model has been extended to accommodate clusters of strongly interconnected excitatory neurons in order to explain persistent activity in working memory-related tasks. This clustered topology introduces multistability and winnerless competition between attractors and can capture the high trial-to-trial variability and its reduction during stimulation that has been found experimentally. In this prospect article, we review the mean field description of balanced networks of binary neurons and apply the theory to clustered networks. We show that the stable fixed points of networks with clustered excitatory connectivity tend quickly towards firing rate saturation, which is generally inconsistent with experimental data. To remedy this shortcoming, we then present a novel perspective on networks with locally balanced clusters of both excitatory and inhibitory neuron populations. This approach allows for true multistability and moderate firing rates in activated clusters over a wide range of parameters. Our findings are supported by mean field theory and numerical network simulations. Finally, we discuss possible applications of the concept of joint excitatory and inhibitory clustering in future cortical network modelling studies.},
author = {Rost, Thomas and Deger, Moritz and Nawrot, Martin Paul},
doi = {10.1007/s00422-017-0737-7},
file = {:Users/springer/Library/Application Support/Mendeley Desktop/Downloaded/Rost, Deger, Nawrot - 2018 - Winnerless competition in clustered balanced networks inhibitory assemblies do the trick.pdf:pdf},
issn = {14320770},
journal = {Biological Cybernetics},
keywords = {Attractor networks,Binary networks,Cortical variability,Mean field theory,Multistability,attractor network,clustered network,spiking neural network},
mendeley-tags = {attractor network,clustered network,spiking neural network},
number = {1-2},
pages = {81--98},
publisher = {Springer Berlin Heidelberg},
title = {{Winnerless competition in clustered balanced networks: inhibitory assemblies do the trick}},
volume = {112},
year = {2018}
}

@article{Nashef2018,
abstract = {{\textcopyright} 2017, Springer-Verlag GmbH Germany. The cerebellar-thalamo-cortical (CTC) system plays a major role in controlling timing and coordination of voluntary movements. However, the functional impact of this system on motor cortical sites has not been documented in a systematic manner. We addressed this question by implanting a chronic stimulating electrode in the superior cerebellar peduncle (SCP) and recording evoked multiunit activity (MUA) and the local field potential (LFP) in the primary motor cortex (n= 926), the premotor cortex (n= 357) and the somatosensory cortex (n= 345). The area-dependent response properties were estimated using the MUA response shape (quantified by decomposing into principal components) and the time-dependent frequency content of the evoked LFP. Each of these signals alone enabled good classification between the somatosensory and motor sites. Good classification between the primary motor and premotor areas could only be achieved when combining features from both signal types. Topographical single-site representation of the predicted class showed good recovery of functional organization. Finally, the probability for misclassification had a broad topographical organization. Despite the area-specific response features to SCP stimulation, there was considerable site-to-site variation in responses, specifically within the motor cortical areas. This indicates a substantial SCP impact on both the primary motor and premotor cortex. Given the documented involvement of these cortical areas in preparation and execution of movement, this result may suggest a CTC contribution to both motor execution and motor preparation. The stimulation responses in the somatosensory cortex were sparser and weaker. However, a functional role of the CTC system in somatosensory computation must be taken into consideration.},
author = {Nashef, Abdulraheem and Rapp, Hannes and Nawrot, Martin P. and Prut, Yifat},
doi = {10.1007/s00422-017-0738-6},
file = {::},
issn = {14320770},
journal = {Biological Cybernetics},
keywords = {Cerebellum,Local field potential,Machine learning,Motor control,Multiunit activity,Thalamocortical,local field potential,machine learning,monkey,motor cortex,premotor cortex,somatosensory cortex},
mendeley-tags = {local field potential,machine learning,monkey,motor cortex,premotor cortex,somatosensory cortex},
number = {1-2},
pages = {141--152},
title = {{Area-specific processing of cerebellar-thalamo-cortical information in primates}},
volume = {112},
year = {2018}
}

@article{Riehle2018,
abstract = {Variability of spiking activity is ubiquitous throughout the brain but little is known about its contextual dependence. Trial-to-trial spike count variability, estimated by the Fano Factor (FF), and within-trial spike time irregularity, quantified by the coefficient of variation (CV), reflect variability on long and short time scales, respectively. We co-analyzed FF and the local coefficient of variation (CV2) in monkey motor cortex comparing two behavioral contexts, movement preparation (wait) and execution (movement). We find that the FF significantly decreases from wait to movement, while the CV2 increases. The more regular firing (expressed by a low CV2) during wait is related to an increased power of local field potential beta oscillations and phase locking of spikes to these oscillations. In renewal processes, a widely used model for spiking activity under stationary input conditions, both measures are related as FF≈CV². This expectation was met during movement, but not during wait where FF{\textgreater}{\textgreater}CV2². Our interpretation is that during movement preparation, ongoing brain processes result in changing network states and thus in high trial-to-trial variability (expressed by a high FF). During movement execution, the network is recruited for performing the stereotyped motor task, resulting in reliable single neuron output. Our interpretation is in the light of recent computational models that generate non-stationary network conditions.},
author = {Riehle, Alexa and Brochier, Thomas and Nawrot, Martin and Gr{\"{u}}n, Sonja},
doi = {10.3389/fncir.2018.00052},
file = {::},
issn = {16625110},
journal = {Frontiers in Neural Circuits},
keywords = {Behavioral context,Monkey motor cortex,Renewal processes,Spike count variability,Spike time irregularity},
pages = {52},
title = {{Behavioral Context Determines Network State and Variability Dynamics in Monkey Motor Cortex}},
volume = {12},
year = {2018}
}

@article{Deger2018,
abstract = {Excitatory synaptic connections in the adult neocortex consist of multiple synaptic contacts, almost exclusively formed on dendritic spines. Changes of spine volume, a correlate of synaptic strength, can be tracked in vivo for weeks. Here, we present a combined model of structural and spike-timing-dependent plasticity that explains the multicontact configuration of synapses in adult neocortical networks under steady-state and lesion-induced conditions. Our plasticity rule with Hebbian and anti-Hebbian terms stabilizes both the postsynaptic firing rate and correlations between the pre-and postsynaptic activity at an active synaptic contact. Contacts appear spontaneously at a low rate and disappear if their strength approaches zero. Many presynaptic neurons compete to make strong synaptic connections onto a postsynaptic neuron, whereas the synaptic contacts of a given presynaptic neuron co-operate via postsynaptic firing. We find that cooperation of multiple synaptic contacts is crucial for stable, long-term synaptic memories. In simulations of a simplified network model of barrel cortex, our plasticity rule reproduces whisker-trimming-induced rewiring of thalamocortical and recurrent synaptic connectivity on realistic time scales.},
author = {Deger, Moritz and Seeholzer, Alexander and Gerstner, Wulfram},
doi = {10.1093/cercor/bhx339},
file = {:Users/springer/Downloads/bhx339.pdf:pdf},
issn = {14602199},
journal = {Cerebral Cortex},
keywords = {Barrel cortex model,Cortical reorganization,Dendritic spine motility,Synaptic plasticity},
number = {4},
pages = {1396--1415},
title = {{Multicontact co-operativity in spike-timing-dependent structural plasticity stabilizes networks}},
volume = {28},
year = {2018}
}

@article{Mana2018,
abstract = {The present work shows that the maximum-entropy method can be applied to a sample of neuronal recordings along two different routes: (1) apply to the sample; or (2) apply to a larger, unsampled neuronal population from which the sample is drawn, and then marginalize to the sample. These two routes give inequivalent results. The second route can be further generalized to the case where the size of the larger population is unknown. Which route should be chosen? Some arguments are presented in favour of the second. This work also presents and discusses probability formulae that relate states of knowledge about a population and its samples, and that may be useful for sampling problems in neuroscience.},
archivePrefix = {arXiv},
arxivId = {1805.09084},
author = {Mana, PierGianLuca Porta and Rostami, Vahid and Torre, Emiliano and Roudi, Yasser},
doi = {10.31219/osf.io/uz29n},
eprint = {1805.09084},
file = {:Users/springer/Downloads/1805.09084.pdf:pdf},
journal = {arXiv},
pages = {1805.09084vl},
title = {{Maximum-entropy and representative samples of neuronal activity: a dilemma}},
url = {http://arxiv.org/abs/1805.09084},
year = {2018}
}

@article{Schwalger2017,
abstract = {Neural population equations such as neural mass or field models are widely used to study brain activity on a large scale. However, the relation of these models to the properties of single neurons is unclear. Here we derive an equation for several interacting populations at the mesoscopic scale starting from a microscopic model of randomly connected generalized integrate-and-fire neuron models. Each population consists of 50–2000 neurons of the same type but different populations account for different neuron types. The stochastic population equations that we find reveal how spike-history effects in single-neuron dynamics such as refractoriness and adaptation interact with finite-size fluctuations on the population level. Efficient integration of the stochastic mesoscopic equations reproduces the statistical behavior of the population activities obtained from microscopic simulations of a full spiking neural network model. The theory describes nonlinear emergent dynamics such as finite-size-induced stochastic transitions in multistable networks and synchronization in balanced networks of excitatory and inhibitory neurons. The mesoscopic equations are employed to rapidly integrate a model of a cortical microcircuit consisting of eight neuron types, which allows us to predict spontaneous population activities as well as evoked responses to thalamic input. Our theory establishes a general framework for modeling finite-size neural population dynamics based on single cell and synapse parameters and offers an efficient approach to analyzing cortical circuits and computations.},
archivePrefix = {arXiv},
arxivId = {1611.00294},
author = {Schwalger, Tilo and Deger, Moritz and Gerstner, Wulfram},
doi = {10.1371/journal.pcbi.1005507},
eprint = {1611.00294},
file = {:Users/springer/Downloads/journal.pcbi.1005507.pdf:pdf},
isbn = {1111111111},
issn = {15537358},
journal = {PLoS Computational Biology},
number = {4},
pages = {1--63},
title = {{Towards a theory of cortical columns: From spiking neurons to interacting neural populations of finite size}},
volume = {13},
year = {2017}
}

@incollection{Deger2017,
abstract = {Networks of neurons in the neocortex change. In the adult neocortex, structural plasticity causes continuous formation and removal of dendritic spines, the structural substrate of most excitatory synapses. Yet, despite this ongoing turnover, statistics of spine numbers remain constant and synaptically stored memories can persist for a lifetime. Several modeling approaches have been developed toward understanding structural plasticity. This chapter summarizes recent work on spike-timing-dependent plasticity models of dendritic spine plasticity and turnover. Such models provide a link between conceptual works rooted in artificial neural networks, and biophysical modeling approaches trying to identify and describe the biological substrate underlying plasticity. Recent modeling of synaptic connections made of multiple dendritic spines has demonstrated how cooperative synapse formation can be explained by the dependence of spine plasticity on pre- and postsynaptic spike timing, and how cooperation of spines may lead to long-term stable synaptic connections with highly reliable synaptic transmission.},
author = {Deger, Moritz},
booktitle = {The Rewiring Brain},
chapter = {12},
pages = {261--273},
title = {{Spike Timing-Dependent Structural Plasticity of Multicontact Synaptic Connections}},
url = {https://doi.org/10.1016/B978-0-12-803784-3.00012-3},
year = {2017}
}

@article{Farkhooi2011,
abstract = {Sequences of events in noise-driven excitable systems with slow variables often show serial correlations among their intervals of events. Here, we employ a master equation for generalized non-renewal processes to calculate the interval and count statistics of superimposed processes governed by a slow adaptation variable. For an ensemble of neurons with spike-frequency adaptation, this results in the regularization of the population activity and an enhanced postsynaptic signal decoding. We confirm our theoretical results in a population of cortical neurons recorded in vivo. {\textcopyright} 2011 American Physical Society.},
author = {Farkhooi, Farzad and Muller, Eilif and Nawrot, Martin P.},
doi = {10.1103/PhysRevE.83.050905},
file = {::},
issn = {15393755},
journal = {Physical Review E - Statistical, Nonlinear, and Soft Matter Physics},
keywords = {spike train statistics,variability},
mendeley-tags = {spike train statistics,variability},
number = {5},
title = {{Adaptation reduces variability of the neuronal population code}},
volume = {83},
year = {2011}
}

@article{Boucsein2011,
abstract = {Current concepts of cortical information processing and most cortical network models largely rest on the assumption that well-studied properties of local synaptic connectivity are sufficient to understand the generic properties of cortical networks. This view seems to be justified by the observation that the vertical connectivity within local volumes is strong, whereas horizontally, the connection probability between pairs of neurons drops sharply with distance. Recent neuroanatomical studies, however, have emphasized that a substantial fraction of synapses onto neocortical pyramidal neurons stems from cells outside the local volume. Here, we discuss recent findings on the signal integration from horizontal inputs, showing that they could serve as a substrate for reliable and temporally precise signal propagation. Quantification of connection probabilities and parameters of synaptic physiology as a function of lateral distance indicates that horizontal projections constitute a considerable fraction, if not the majority, of inputs from within the cortical network. Taking these non-local horizontal inputs into account may dramatically change our current view on cortical information processing.},
author = {Boucsein, Clemens and Nawrot, Martin P. and Schnepel, Philipp and Aertsen, Ad},
doi = {10.3389/fnins.2011.00032},
file = {:Users/springer/Library/Application Support/Mendeley Desktop/Downloaded/Boucsein et al. - 2011 - Beyond the cortical column Abundance and physiology of horizontal connections imply a strong role for inputs fr.pdf:pdf},
issn = {16624548},
journal = {Frontiers in Neuroscience},
keywords = {Cortical column,Dendritic integration,Reliability,Synaptic transmission,Temporal coding,cortex,in vitro,synaptic transmission},
mendeley-tags = {cortex,in vitro,synaptic transmission},
pages = {32},
title = {{Beyond the cortical column: Abundance and physiology of horizontal connections imply a strong role for inputs from the surround}},
volume = {5},
year = {2011}
}

@article{Fucke2011,
abstract = {Alternating epochs of activity and silence are a characteristic feature of neocortical networks during certain sleep cycles and deep states of anesthesia. The mechanism and functional role of these slow oscillations ({\textless}1 Hz) have not yet been fully characterized. Experimental and theoretical studies show that slow-wave oscillations can be generated autonomously by neocortical tissue but become more regular through a thalamo-cortical feedback loop. Evidence for a functional role of slow-wave activity comes from EEG recordings in humans during sleep, which show that activity travels as stereotypical waves over the entire brain, thought to play a role in memory consolidation. We used an animal model to investigate activity wave propagation on a smaller scale, namely within the rat somatosensory cortex. Signals from multiple extracellular microelectrodes in combination with one intracellular recording in the anesthetized animal in vivo were utilized to monitor the spreading of activity. We found that activity propagation in most animals showed a clear preferred direction, suggesting that it often originated from a similar location in the cortex. In addition, the breakdown of active states followed a similar pattern with slightly weaker direction preference but a clear correlation to the direction of activity spreading, supporting the notion of a wave-like phenomenon similar to that observed after strong sensory stimulation in sensory areas. Taken together, our findings support the idea that activity waves during slow-wave sleep do not occur spontaneously at random locations within the network, as was suggested previously, but follow preferred synaptic pathways on a small spatial scale. {\textcopyright} 2011 the American Physiological Society.},
author = {Fucke, Thomas and Suchanek, Dymphie and Nawrot, Martin P. and Seamari, Yamina and Heck, Detlef H. and Aertsen, Ad and Boucsein, Clemens},
doi = {10.1152/jn.00811.2010},
file = {::},
issn = {00223077},
journal = {Journal of Neurophysiology},
keywords = {Extracellular electrode array,Ketamine/ xylazine,Slow-wave sleep,Traveling waves,Up/down state},
number = {6},
pages = {3035--3044},
title = {{Stereotypical spatiotemporal activity patterns during slow-wave activity in the neocortex}},
volume = {106},
year = {2011}
}

@inbook{Nawrot2010,
abstract = {Understanding the nature and origin of neural variability at the level of single neurons and neural networks is fundamental to our understanding of how neural systems can reliably process information. This chapter provides a starting point to the empirical analysis and interpretation of the variability of single neuron spike trains. In the first part, we cover a number of practical issues of measuring the inter-spike interval variability with the coefficient of variation (CV) and the trial-by-trial count variability with the Fano factor (FF), including the estimation bias for finite observations, the measurement from rate-modulated spike trains, and the time-resolved analysis of variability dynamics. In the second part, we specifically explore the effect of serial interval correlation in nonrenewal spike trains and the impact of slow fluctuations of neural activity on the relation of interval and count variability in stochastic models and in in vivo recordings from cortical neurons. Finally, we discuss how we can interpret the empirical results with respect to potential neuron-intrinsic and neuron-extrinsic sources of single neuron output variability.},
address = {Boston, MA},
author = {Nawrot, Martin Paul},
booktitle = {Analysis of Parallel Spike Trains},
doi = {10.1007/978-1-4419-5675-0_3},
editor = {Gr{\"{u}}n, Sonja and Rotter, Stefan},
file = {::},
isbn = {978-1-4419-5675-0},
pages = {37--58},
publisher = {Springer US},
title = {{Analysis and Interpretation of Interval and Count Variability in Neural Spike Trains}},
url = {https://doi.org/10.1007/978-1-4419-5675-0{\_}3},
year = {2010}
}

@article{Nawrot2009,
abstract = {The mammalian neocortex has a remarkable ability to precisely reproduce behavioral sequences or to reliably retrieve stored information. In contrast, spiking activity in behaving animals shows a considerable trial-to-trial variability and temporal irregularity. The signal propagation and processing underlying these conflicting observations is based on fundamental neurophysiological processes like synaptic transmission, signal integration within single cells, and spike formation. Each of these steps in the neuronal signaling chain has been studied separately to a great extend, but it has been difficult to judge how they interact and sum up in active sub-networks of neocortical cells. In the present study, we experimentally assessed the precision and reliability of small neocortical networks consisting of trans-columnar, intermediate-range projections (200-1000 $\mu$m) on a millisecond time-scale. Employing photo-uncaging of glutamate in acute slices, we activated a number of distant presynaptic cells in a spatio-temporally precisely controlled manner, while monitoring the resulting membrane potential fluctuations of a postsynaptic cell. We found that signal integration in this part of the network is highly reliable and temporally precise. As numerical simulations showed, the residual membrane potential variability can be attributed to amplitude variability in synaptic transmission and may significantly contribute to trial-to-trial output variability of a rate signal. However, it does not impair the temporal accuracy of signal integration. We conclude that signals from intermediate-range projections onto neocortical neurons are propagated and integrated in a highly reliable and precise manner, and may serve as a substrate for temporally precise signal transmission in neocortical networks. {\textcopyright} 2009 Nawrot, Schnepel, Aertsen and Boucsein.},
author = {Nawrot, Martin Paul and Schnepel, Philipp and Aertsen, Ad and Boucsein, Clemens},
doi = {10.3389/neuro.04.001.2009},
file = {::},
issn = {16625110},
journal = {Frontiers in Neural Circuits},
keywords = {Cell assembly,Coincidence detector,Cortex,Dynamic photo stimulation,Rate coding,Synaptic variability,Temporal coding,cortex,in vitro,synaptic transmission},
mendeley-tags = {cortex,in vitro,synaptic transmission},
title = {{Precisely timed signal transmission in neocortical networks with reliable intermediate-range projections}},
volume = {3},
year = {2009}
}

@article{Rickert2009,
abstract = {When we perform a skilled movement such as reaching for an object, we can make use of prior information, for example about the location of the object in space. This helps us to prepare the movement, and we gain improved accuracy and speed during movement execution. Here, we investigate how prior information affects the motor cortical representation of movements during preparation and execution. We trained two monkeys in a delayed reaching task and provided a varying degree of prior information about the final target location. We decoded movement direction from multiple single-unit activity recorded from M1 (primary motor cortex) in one monkey and from PMd (dorsal premotor cortex) in a second monkey. Our results demonstrate that motor cortical cells in both areas exhibit individual encoding characteristics that change dynamically in time and dependent on prior information. On the population level, the information about movement direction is at any point in time accurately represented in a neuronal ensemble of time-varying composition. We conclude that movement representation in the motor cortex is not a static one, but one in which neurons dynamically allocate their computational resources to meet the demands defined by the movement task and the context of the movement. Consequently, we find that the decoding accuracy decreases if the precise task time, or the previous information that was available to the monkey, were disregarded in the decoding process. An optimal strategy for the readout of movement parameters from motor cortex should therefore take into account time and contextual parameters. Copyright {\textcopyright} 2009 Society for Neuroscience.},
author = {Rickert, J. and Riehle, A. and Aertsen, A. and Rotter, S. and Nawrot, M.P.},
doi = {10.1523/JNEUROSCI.5441-08.2009},
file = {::},
issn = {02706474},
journal = {Journal of Neuroscience},
keywords = {Fano factor,monkey,motor cortex,variability},
mendeley-tags = {Fano factor,monkey,motor cortex,variability},
number = {44},
title = {{Dynamic encoding of movement direction in motor cortical neurons}},
volume = {29},
year = {2009}
}

@article{Nawrot2008,
abstract = {We propose a method for the time-resolved joint analysis of two related aspects of single neuron variability, the spiking irregularity measured by the squared coefficient of variation (CV2) of the ISIs and the trial-by-trial variability of the spike count measured by the Fano factor (FF). We provide a calibration of both estimators using the theory of renewal processes, and verify it for spike trains recorded in vitro. Both estimators exhibit a considerable bias for short observations that count less than about 5-10 spikes on average. The practical difficulty of measuring the CV2 in rate modulated data can be overcome by a simple procedure of spike train demodulation which was tested in numerical simulations and in real spike trains. We propose to test neuronal spike trains for deviations from the null-hypothesis FF = CV2. We show that cortical pyramidal neurons, recorded under controlled stationary input conditions in vitro, comply with this assumption. Performing a time-resolved joint analysis of CV2 and FF of a single unit recording from the motor cortex of a behaving monkey we demonstrate how the dynamic change of their quantitative relation can be interpreted with respect to neuron intrinsic and extrinsic factors that influence cortical variability in vivo. Finally, we discuss the effect of several additional factors such as serial interval correlation and refractory period on the empiric relation of FF and CV2. {\textcopyright} 2007 Elsevier B.V. All rights reserved.},
author = {Nawrot, Martin P. and Boucsein, Clemens and Rodriguez-Molina, Victor and Riehle, Alexa and Aertsen, Ad and Rotter, Stefan},
doi = {10.1016/j.jneumeth.2007.10.013},
file = {::},
issn = {01650270},
journal = {Journal of Neuroscience Methods},
keywords = {Coefficient of variation,Cortical variability,Fano factor,Gamma process,Monkey motor cortex,Noise current injection,Renewal process,Spiking irregularity,in vitro,monkey,spike train statistics,spiking irregularity,variability},
mendeley-tags = {Fano factor,in vitro,monkey,spike train statistics,spiking irregularity,variability},
number = {2},
pages = {374--390},
title = {{Measurement of variability dynamics in cortical spike trains}},
volume = {169},
year = {2008}
}

@article{Nawrot2007,
abstract = {Stationary spiking of single neurons is often modelled by a renewal point process. Here, we tested the underlying model assumption that the inter-spike intervals are mutually independent by analyzing stationary spike train recordings from individual rat neocortical neurons in vivo and in vitro. All neurons exhibited moderate (in vivo) or weak (in vitro) negative first order serial correlation of neighboring intervals which was found to be significant in most cases. No significant higher order serial correlations were detected. The observed negative correlation lead to a strong reduction of the spike count variability by about 30{\%} in vivo. {\textcopyright} 2006 Elsevier B.V. All rights reserved.},
author = {Nawrot, Martin P. and Boucsein, Clemens and Rodriguez-Molina, Victor and Aertsen, Ad and Gr{\"{u}}n, Sonja and Rotter, Stefan},
doi = {10.1016/j.neucom.2006.10.101},
file = {::},
issn = {09252312},
journal = {Neurocomputing},
keywords = {Fano factor,Markov order,Renewal process,Serial interval correlation},
number = {10-12},
pages = {1717--1722},
title = {{Serial interval statistics of spontaneous activity in cortical neurons in vivo and in vitro}},
volume = {70},
year = {2007}
}

@incollection{Ball2006,
author = {Ball, Tonio and Mehring, Carsten and Nawrot, Martin Paul and Weiskopf, N},
booktitle = {Jahrbuch der Heidelberger Akademie der Wissenschaften f{\"{u}}r 2005},
title = {{Neuronale Kodierung von Bewegung bei Affe und Mensch: Von Einzelzellen und Zellenensembles zum Brain-Computer-Interface IV}},
year = {2006}
}

@article{Boucsein2005,
abstract = {Recent experimental and theoretical work indicates that both the intensity and the temporal structure of synaptic activity strongly modulate the integrative properties of single neurons in the intact brain. However, studying these effects experimentally is complicated by the fact that, in experimental systems, network activity is either absent, as in the acute slice preparation, or difficult to monitor and to control, as in in vivo recordings. Here, we present a new implementation of neurotransmitter uncaging in acute brain slices that uses functional projections to generate tightly controlled, spatio-temporally structured synaptic input patterns in individual neurons. For that, a set of presynaptic neurons is activated in a precisely timed sequence through focal photolytic release of caged glutamate with the help of a fast laser scanning system. Integration of synaptic inputs can be studied in postsynaptic neurons that are not directly stimulated with the laser, but receive input from the targeted neurons through intact axonal projections. Our new approach of dynamic photo stimulation employs functional synapses, accounts for their spatial distribution on the dendrites, and thus allows study of the integrative properties of single neurons with physiologically realistic input. Data obtained with our new technique suggest that, not only the neuronal spike generator, but also synaptic transmission and dendritic integration in neocortical pyramidal cells, can be highly reliable. Copyright {\textcopyright} 2005 The American Physiological Society.},
author = {Boucsein, Clemens and Nawrot, Martin Paul and Rotter, Stefan and Aertsen, Ad and Heck, Detlef},
doi = {10.1152/jn.00245.2005},
file = {::},
issn = {00223077},
journal = {Journal of Neurophysiology},
number = {4},
pages = {2948--2958},
title = {{Controlling synaptic input patterns in vitro by dynamic photo stimulation}},
volume = {94},
year = {2005}
}

@article{Mehring2004,
abstract = {Cortical field potentials have been used for decades in neurophysiological studies to probe spatio-temporal activity patterns of local populations of neurons. Recently, however, interest in these signals was spurred as they were proposed as potential control signals for neuronal motor prostheses, i.e., for devices fit to record and decode brain activity to restore motor functions in paralyzed patients. Little is known, however, about the functional significance of these cortical field potentials. Here we compared information about arm movement direction in two types of movement related cortical field potentials, obtained during a four direction center-out arm reaching paradigm: local field potentials (LFPs) recorded with intracortical micro-electrodes from monkey motor cortex, and epicortical field potentials (EFPs) recorded with macro-electrode arrays subdurally implanted on the surface of the human cerebral cortex. While monkey LFPs showed a typical sequence of positive and negative potential peaks, an initial negative peak was the most salient feature of human EFPs. Individual contralateral LFPs from the monkey motor cortex carried approximately twice as much decoded information (DI) about arm movement direction (median 0.27 bit) as did individual EFPs from the contralateral hand/arm area of primary motor cortex in humans (median 0.12 bit). This relation was similar to the relation between median peak signal-to-noise ratios for directional modulation of movement related potentials (MRPs) of both types of signals. We discuss possible reasons for the observed differences, amongst them epi- vs. intracortical recording and the different electrode dimensions used to measure EFPs and LFPs. {\textcopyright} 2005 Elsevier Ltd. All rights reserved.},
author = {Mehring, Carsten and Nawrot, Martin Paul and DeOliveira, Simone Cardoso and Vaadia, Eilon and Schulze-Bonhage, Andreas and Aertsen, Ad and Ball, Tonio},
doi = {10.1016/j.jphysparis.2005.09.016},
issn = {09284257},
journal = {Journal of Physiology Paris},
keywords = {Brain-machine interface,Motor control,Movement decoding,Neuronal signal types,Subdural electrodes,brain machine interface,human,machine learning,monkey,motor cortex},
mendeley-tags = {brain machine interface,human,machine learning,monkey,motor cortex},
number = {4-6},
pages = {498--506},
title = {{Comparing information about arm movement direction in single channels of local and epicortical field potentials from monkey and human motor cortex}},
volume = {98},
year = {2004}
}