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  • 1. Ahmed, Omar Jamil
    et al.
    McFarland, James
    Kumar, Arvind
    Brown University, United States.
    Reactivation in ventral striatum during hippocampal ripples: evidence for the binding of reward and spatial memories?2008In: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 28, no 40, p. 9895-9897Article in journal (Refereed)
  • 2.
    Bahuguna, Jyotika
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. University of Freiburg, Germany.
    Aertsen, Ad
    University of Freiburg, Germany.
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. University of Freiburg, Germany.
    Existence and control of Go/No-Go decision transition threshold in the striatum2015In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 11, no 4, article id e1004233Article in journal (Refereed)
    Abstract [en]

    A typical Go/No-Go decision is suggested to be implemented in the brain via the activation of the direct or indirect pathway in the basal ganglia. Medium spiny neurons (MSNs) in the striatum, receiving input from cortex and projecting to the direct and indirect pathways express D1 and D2 type dopamine receptors, respectively. Recently, it has become clear that the two types of MSNs markedly differ in their mutual and recurrent connectivities as well as feedforward inhibition from FSIs. Therefore, to understand striatal function in action selection, it is of key importance to identify the role of the distinct connectivities within and between the two types of MSNs on the balance of their activity. Here, we used both a reduced firing rate model and numerical simulations of a spiking network model of the striatum to analyze the dynamic balance of spiking activities in D1 and D2 MSNs. We show that the asymmetric connectivity of the two types of MSNs renders the striatum into a threshold device, indicating the state of cortical input rates and correlations by the relative activity rates of D1 and D2 MSNs. Next, we describe how this striatal threshold can be effectively modulated by the activity of fast spiking interneurons, by the dopamine level, and by the activity of the GPe via pallidostriatal backprojections. We show that multiple mechanisms exist in the basal ganglia for biasing striatal output in favour of either the `Go' or the `No-Go' pathway. This new understanding of striatal network dynamics provides novel insights into the putative role of the striatum in various behavioral deficits in patients with Parkinson's disease, including increased reaction times, L-Dopa-induced dyskinesia, and deep brain stimulation-induced impulsivity.

  • 3. Bahuguna, Jyotika
    et al.
    Tetzlaff, Tom
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Morrison, Abigail
    Homologous Basal Ganglia Network Models in Physiological and Parkinsonian Conditions2017In: Frontiers in Computational Neuroscience, ISSN 1662-5188, E-ISSN 1662-5188, Vol. 11, article id 79Article in journal (Refereed)
    Abstract [en]

    The classical model of basal ganglia has been refined in recent years with discoveries of subpopulations within a nucleus and previously unknown projections. One such discovery is the presence of subpopulations of arkypallidal and prototypical neurons in external globus pallidus, which was previously considered to be a primarily homogeneous nucleus. Developing a computational model of these multiple interconnected nuclei is challenging, because the strengths of the connections are largely unknown. We therefore use a genetic algorithm to search for the unknown connectivity parameters in a firing rate model. We apply a binary cost function derived from empirical firing rate and phase relationship data for the physiological and Parkinsonian conditions. Our approach generates ensembles of over 1,000 configurations, or homologies, for each condition, with broad distributions for many of the parameter values and overlap between the two conditions. However, the resulting effective weights of connections from or to prototypical and arkypallidal neurons are consistent with the experimental data. We investigate the significance of the weight variability by manipulating the parameters individually and cumulatively, and conclude that the correlation observed between the parameters is necessary for generating the dynamics of the two conditions. We then investigate the response of the networks to a transient cortical stimulus, and demonstrate that networks classified as physiological effectively suppress activity in the internal globus pallidus, and are not susceptible to oscillations, whereas parkinsonian networks show the opposite tendency. Thus, we conclude that the rates and phase relationships observed in the globus pallidus are predictive of experimentally observed higher level dynamical features of the physiological and parkinsonian basal ganglia, and that the multiplicity of solutions generated by our method may well be indicative of a natural diversity in basal ganglia networks. We propose that our approach of generating and analyzing an ensemble of multiple solutions to an underdetermined network model provides greater confidence in its predictions than those derived from a unique solution, and that projecting such homologous networks on a lower dimensional space of sensibly chosen dynamical features gives a better chance than a purely structural analysis at understanding complex pathologies such as Parkinson's disease.

  • 4.
    Belic, Jovana
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Bernstein Center Freiburg, University of Freiburg, Freiburg Germany.
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Bernstein Center Freiburg, University of Freiburg, Freiburg Germany.
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Department of Neuroscience, Karolinska Institute, Stockholm, Sweden.
    Interactions in the Striatal Network with Different Oscillation Frequencies2017In: Artificial Neural Networks and Machine Learning – ICANN. Lecture Notes in Computer Science, Springer, 2017, Vol. 10613, p. 129-136Conference paper (Refereed)
    Abstract [en]

    Simultaneous oscillations in different frequency bands are implicated in the striatum, and understanding their interactions will bring us one step closer to restoring the spectral characteristics of striatal activity that correspond to the healthy state. We constructed a computational model of the striatum in order to investigate how different, simultaneously present, and externally induced oscillations propagate through striatal circuitry and which stimulation parameters have a significant contribution. Our results show that features of these oscillations and their interactions can be influenced via amplitude, input frequencies, and the phase offset between different external inputs. Our findings provide further untangling of the oscillatory activity that can be seen within the striatal network.

  • 5.
    Belić, Jovana
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Interplay between periodic stimulation and GABAergic inhibition in striatal network oscillations2017In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 12, no 4, p. 1-17Article in journal (Refereed)
    Abstract [en]

    Network oscillations are ubiquitous across many brain regions. In the basal ganglia, oscillations are also present at many levels and a wide range of characteristic frequencies have been reported to occur during both health and disease. The striatum, the main input nucleus of the basal ganglia, receives massive glutamatergic inputs from the cortex and is highly susceptible to external oscillations. However, there is limited knowledge about the exact nature of this routing process and therefore, it is of key importance to understand how time-dependent, external stimuli propagate through the striatal circuitry. Using a network model of the striatum and corticostriatal projections, we try to elucidate the importance of specific GABAergic neurons and their interactions in shaping striatal oscillatory activity. Here, we propose that fast-spiking interneurons can perform an important role in transferring cortical oscillations to the striatum especially to those medium spiny neurons that are not directly driven by the cortical oscillations. We show how the activity levels of different populations, the strengths of different inhibitory synapses, degree of outgoing projections of striatal cells, ongoing activity and synchronicity of inputs can influence network activity. These results suggest that the propagation of oscillatory inputs into the medium spiny neuron population is most efficient, if conveyed via the fast-spiking interneurons. Therefore, pharmaceuticals that target fast-spiking interneurons may provide a novel treatment for regaining the spectral characteristics of striatal activity that correspond to the healthy state.

  • 6. Bujan, Alejandro
    et al.
    Aertsen, Ad
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. University of Freiburg, Freiburg, Germany .
    Role of Input Correlations in Shaping the Variability and Noise Correlations of Evoked Activity in the Neocortex2015In: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 35, no 22, p. 8611-8625Article in journal (Refereed)
    Abstract [en]

    Recent analysis of evoked activity recorded across different brain regions and tasks revealed a marked decrease in noise correlations and trial-by-trial variability. Given the importance of correlations and variability for information processing within the rate coding paradigm, several mechanisms have been proposed to explain the reduction in these quantities despite an increase in firing rates. These models suggest that anatomical clusters and/or tightly balanced excitation-inhibition can generate intrinsic network dynamics that may exhibit a reduction in noise correlations and trial-by-trial variability when perturbed by an external input. Such mechanisms based on the recurrent feedback crucially ignore the contribution of feedforward input to the statistics of the evoked activity. Therefore, we investigated how statistical properties of the feedforward input shape the statistics of the evoked activity. Specifically, we focused on the effect of input correlation structure on the noise correlations and trial-by-trial variability. We show that the ability of neurons to transfer the input firing rate, correlation, and variability to the output depends on the correlations within the presynaptic pool of a neuron, and that an input with even weak within-correlations can be sufficient to reduce noise correlations and trial-by-trial variability, without requiring any specific recurrent connectivity structure. In general, depending on the ongoing activity state, feedforward input could either increase or decrease noise correlation and trial-by-trial variability. Thus, we propose that evoked activity statistics are jointly determined by the feedforward and feedback inputs.

  • 7.
    Ekeberg, Örjan
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Fransén, Erik
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Hellgren Kotaleski, Jeanette
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Herman, Pawel
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Lansner, Anders
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Lindeberg, Tony
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Computational Brain Science at CST, CSC, KTH2016Other (Other academic)
    Abstract [en]

    Mission and Vision - Computational Brain Science Lab at CST, CSC, KTH

    The scientific mission of the Computational Brain Science Lab at CSC is to be at the forefront of mathematical modelling, quantitative analysis and mechanistic understanding of brain function. We perform research on (i) computational modelling of biological brain function and on (ii) developing theory, algorithms and software for building computer systems that can perform brain-like functions. Our research answers scientific questions and develops methods in these fields. We integrate results from our science-driven brain research into our work on brain-like algorithms and likewise use theoretical results about artificial brain-like functions as hypotheses for biological brain research.

    Our research on biological brain function includes sensory perception (vision, hearing, olfaction, pain), cognition (action selection, memory, learning) and motor control at different levels of biological detail (molecular, cellular, network) and mathematical/functional description. Methods development for investigating biological brain function and its dynamics as well as dysfunction comprises biomechanical simulation engines for locomotion and voice, machine learning methods for analysing functional brain images, craniofacial morphology and neuronal multi-scale simulations. Projects are conducted in close collaborations with Karolinska Institutet and Karolinska Hospital in Sweden as well as other laboratories in Europe, U.S., Japan and India.

    Our research on brain-like computing concerns methods development for perceptual systems that extract information from sensory signals (images, video and audio), analysis of functional brain images and EEG data, learning for autonomous agents as well as development of computational architectures (both software and hardware) for neural information processing. Our brain-inspired approach to computing also applies more generically to other computer science problems such as pattern recognition, data analysis and intelligent systems. Recent industrial collaborations include analysis of patient brain data with MentisCura and the startup company 13 Lab bought by Facebook.

    Our long term vision is to contribute to (i) deeper understanding of the computational mechanisms underlying biological brain function and (ii) better theories, methods and algorithms for perceptual and intelligent systems that perform artificial brain-like functions by (iii) performing interdisciplinary and cross-fertilizing research on both biological and artificial brain-like functions. 

    On one hand, biological brains provide existence proofs for guiding our research on artificial perceptual and intelligent systems. On the other hand, applying Richard Feynman’s famous statement ”What I cannot create I do not understand” to brain science implies that we can only claim to fully understand the computational mechanisms underlying biological brain function if we can build and implement corresponding computational mechanisms on a computerized system that performs similar brain-like functions.

  • 8. Froriep, Ulrich P
    et al.
    Kumar, Arvind
    University of Freiburg, Germany.
    Cosandier-Rimélé, Delphine
    Häussler, Ute
    Kilias, Antje
    Haas, Carola A
    Egert, Ulrich
    Altered theta coupling between medial entorhinal cortex and dentate gyrus in temporal lobe epilepsy2012In: Epilepsia, ISSN 0013-9580, E-ISSN 1528-1167, Vol. 53, no 11, p. 1937-1947Article in journal (Refereed)
    Abstract [en]

    Purpose: Temporal lobe epilepsy is often accompanied by neuron loss and rewiring in the hippocampus. We hypothesized that the interaction of subnetworks of the entorhinalhippocampal loop between epileptic events should show significant signatures of these pathologic changes.

    Methods: We combined simultaneous recording of local field potentials in entorhinal cortex (EC) and dentate gyrus (DG) in freely behaving kainate-injected mice with histologic analyses and computational modeling.

    Key Findings: In healthy mice, theta band activity was synchronized between EC and DG. In contrast, in epileptic mice, theta activity in the EC was delayed with respect to the DG. A computational neural mass model suggests that hippocampal cell loss imbalances the coupling of subnetworks, introducing the shift.

    Significance: We show that pathologic dynamics in epilepsy encompass ongoing activity in the entorhinal-hippocampal loop beyond acute epileptiform activity. This predominantly affects theta band activity, which links this shift in entorhinal-hippocampal interaction to behavioral aspects in epilepsy.

  • 9. Grah, Gunnar
    et al.
    Kumar, Arvind
    Wettstreit der Metaphern2014In: Geist und Gehirn, ISSN 1618-8519, no 7, p. 60-65Article, review/survey (Other (popular science, discussion, etc.))
  • 10. Grah, Gunnar
    et al.
    Kumar, Arvind
    Zittern in zahlen2013In: Geist und Gehirn, ISSN 1618-8519, no 5, p. 68-73Article, review/survey (Other (popular science, discussion, etc.))
  • 11.
    Grangeray-Vilmint, Anais
    et al.
    Univ Strasbourg, CNRS, Inst Neurosci Cellulaires & Integrat, F-67084 Strasbourg, France..
    Valera, Antoine M.
    Univ Strasbourg, CNRS, Inst Neurosci Cellulaires & Integrat, F-67084 Strasbourg, France.;UCL, Dept Neurosci Physiol & Pharmacol, London, England..
    Kumar, Arvind
    KTH, School of Electrical Engineering and Computer Science (EECS).
    Isope, Philippe
    Univ Strasbourg, CNRS, Inst Neurosci Cellulaires & Integrat, F-67084 Strasbourg, France..
    Short-Term Plasticity Combines with Excitation-Inhibition Balance to Expand Cerebellar Purkinje Cell Dynamic Range2018In: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 38, no 22, p. 5153-5167Article in journal (Refereed)
    Abstract [en]

    The balance between excitation (E) and inhibition (I) in neuronal networks controls the firing rate of principal cells through simple network organization, such as feedforward inhibitory circuits. Here, we demonstrate in male mice, that at the granule cell (GrC)molecular layer interneuron (MLI)-Purkinje cell (PC) pathway of the cerebellar cortex, E/I balance is dynamically controlled by short-term dynamics during bursts of stimuli, shaping cerebellar output. Using a combination of electrophysiological recordings, optogenetic stimulation, and modeling, we describe the wide range of bidirectional changes in PC discharge triggered by GrC bursts, from robust excitation to complete inhibition. At high frequency (200 Hz), increasing the number of pulses in a burst (from 3 to 7) can switch a net inhibition of PC to a net excitation. Measurements of EPSCs and IPSCs during bursts and modeling showed that this feature can be explained by the interplay between short-term dynamics of the GrC-MLI-PC pathway and E/I balance impinging on PC. Our findings demonstrate that PC firing rate is highly sensitive to the duration of GrC bursts, which may define a temporal-to-rate code transformation in the cerebellar cortex.

  • 12. Hahn, Gerald
    et al.
    Bujan, Alejandro F
    Frégnac, Yves
    Aertsen, Ad
    Kumar, Arvind
    Univ Freiburg, Germany.
    Communication through resonance in spiking neuronal networks2014In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 10, no 8, article id e1003811Article in journal (Refereed)
    Abstract [en]

    The cortex processes stimuli through a distributed network of specialized brain areas. This processing requires mechanisms that can route neuronal activity across weakly connected cortical regions. Routing models proposed thus far are either limited to propagation of spiking activity across strongly connected networks or require distinct mechanisms that create local oscillations and establish their coherence between distant cortical areas. Here, we propose a novel mechanism which explains how synchronous spiking activity propagates across weakly connected brain areas supported by oscillations. In our model, oscillatory activity unleashes network resonance that amplifies feeble synchronous signals and promotes their propagation along weak connections ("communication through resonance''). The emergence of coherent oscillations is a natural consequence of synchronous activity propagation and therefore the assumption of different mechanisms that create oscillations and provide coherence is not necessary. Moreover, the phase-locking of oscillations is a side effect of communication rather than its requirement. Finally, we show how the state of ongoing activity could affect the communication through resonance and propose that modulations of the ongoing activity state could influence information processing in distributed cortical networks.

  • 13. Hahn, Gerald
    et al.
    Ponce-Alvarez, Adrian
    Monier, Cyril
    Benvenuti, Giacomo
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Bernstein Ctr Computat Neurosci, Germany.
    Chavane, Frederic
    Deco, Gustavo
    Fregnac, Yves
    Spontaneous cortical activity is transiently poised close to criticality2017In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 13, no 5, article id e1005543Article in journal (Refereed)
    Abstract [en]

    Brain activity displays a large repertoire of dynamics across the sleep-wake cycle and even during anesthesia. It was suggested that criticality could serve as a unifying principle underlying the diversity of dynamics. This view has been supported by the observation of spontaneous bursts of cortical activity with scale-invariant sizes and durations, known as neuronal avalanches, in recordings of mesoscopic cortical signals. However, the existence of neuronal avalanches in spiking activity has been equivocal with studies reporting both its presence and absence. Here, we show that signs of criticality in spiking activity can change between synchronized and desynchronized cortical states. We analyzed the spontaneous activity in the primary visual cortex of the anesthetized cat and the awake monkey, and found that neuronal avalanches and thermodynamic indicators of criticality strongly depend on collective synchrony among neurons, LFP fluctuations, and behavioral state. We found that synchronized states are associated to criticality, large dynamical repertoire and prolonged epochs of eye closure, while desynchronized states are associated to sub-criticality, reduced dynamical repertoire, and eyes open conditions. Our results show that criticality in cortical dynamics is not stationary, but fluctuates during anesthesia and between different vigilance states.

  • 14. Kremkow, Jens
    et al.
    Aertsen, Ad
    Kumar, Arvind
    University of Freiburg, Germany .
    Gating of signal propagation in spiking neural networks by balanced and correlated excitation and inhibition2010In: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 30, no 47, p. 15760-8Article in journal (Refereed)
    Abstract [en]

    Both ongoing and natural stimulus driven neuronal activity are dominated by transients. Selective gating of these transients is mandatory for proper brain function and may, in fact, form the basis of millisecond-fast decision making and action selection. Here we propose that neuronal networks may exploit timing differences between correlated excitation and inhibition (temporal gating) to control the propagation of spiking activity transients. When combined with excitation-inhibition balance, temporal gating constitutes a powerful mechanism to control the propagation of mixtures of transient and tonic neural activity components.

  • 15. Kremkow, Jens
    et al.
    Kumar, Arvind
    Rotter, Stefan
    Aertsen, Ad
    Emergence of population synchrony in a layered network of the cat visual cortex2007In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 70, no 10-12, p. 2069-2073Article in journal (Refereed)
    Abstract [en]

    Recently, a quantitative wiring diagram for the local neuronal network of cat visual cortex was described [T. Binzegger, R.J. Douglas, K.A.C. Martin, A quantitative map of the circuit of the cat primary visual cortex, J. Neurosci. 39 (24) (2004) 8441-8453.] giving the first complete estimate of synaptic connectivity among various types of neurons in different cortical layers. Here we numerically studied the activity dynamics of the resulting heterogeneous layered network of spiking integrate-and-fire neurons, connected with conductance-based synapses. The layered network exhibited, among other states, an interesting asynchronous activity with intermittent population-wide synchronizations. These population bursts (PB) were initiated by a network hot spot, and then spread into the other parts of the network. The cause of this PB is the correlation amplifying nature of recurrent connections, which becomes significant in densely coupled networks. The hot spot was located in layer 2 / 3, the part of the network with the highest number of excitatory recurrent connections. We conclude that in structured networks, regions with a high degree of recurrence and many out-going fibres may be a source for population-wide synchronization.

  • 16.
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Reprogramming the striatal stars: A new treatment option for Parkinson's disease2017In: Movement Disorders, ISSN 0885-3185, E-ISSN 1531-8257, Vol. 32, no 7, p. 991-991Article in journal (Refereed)
  • 17.
    Kumar, Arvind
    et al.
    University of Freiburg, Germany .
    Cardanobile, Stefano
    Rotter, Stefan
    Aertsen, Ad
    The role of inhibition in generating and controlling Parkinson’s disease oscillations in the Basal Ganglia2011In: Frontiers in Systems Neuroscience, ISSN 1662-5137, E-ISSN 1662-5137, Vol. OCTOBER 2011, article id 86Article in journal (Refereed)
    Abstract [en]

    Movement disorders in Parkinson’s disease (PD) are commonly associated with slow oscillations and increased synchrony of neuronal activity in the basal ganglia. The neural mechanisms underlying this dynamic network dysfunction, however, are only poorly understood. Here, we show that the strength of inhibitory inputs from striatum to globus pallidus external (GPe) is a key parameter controlling oscillations in the basal ganglia. Specifically, the increase in striatal activity observed in PD is sufficient to unleash the oscillations in the basal ganglia. This finding allows us to propose a unified explanation for different phenomena: absence of oscillation in the healthy state of the basal ganglia, oscillations in dopamine-depleted state and quenching of oscillations under deep-brain-stimulation (DBS). These novel insights help us to better understand and optimize the function of DBS protocols. Furthermore, studying the model behavior under transient increase of activity of the striatal neurons projecting to the indirect pathway, we are able to account for both motor impairment in PD patients and for reduced response inhibition in DBS implanted patients.

  • 18.
    Kumar, Arvind
    et al.
    University of Freiburg, Germany .
    Mehta, Mayank R
    Frequency dependent changes in NMDAR-dependent synaptic plasticity2011In: Frontiers in Computational Neuroscience, ISSN 1662-5188, E-ISSN 1662-5188, Vol. 5, no 38Article in journal (Refereed)
    Abstract [en]

    The NMDAR-dependent synaptic plasticity is thought to mediate several forms of learning, and can be induced by spike trains containing a small number of spikes occurring with varying rates and timing, as well as with oscillations. We computed the influence of these variables on the plasticity induced at a single NMDAR containing synapse using a reduced model that was analytically tractable, and these findings were confirmed using detailed, multi-compartment model. In addition to explaining diverse experimental results about the rate and timing dependence of synaptic plasticity, the model made several novel and testable predictions. We found that there was a preferred frequency for inducing long-term potentiation (LTP) such that higher frequency stimuli induced lesser LTP, decreasing as 1/f when the number of spikes in the stimulus was kept fixed. Among other things, the preferred frequency for inducing LTP varied as a function of the distance of the synapse from the soma. In fact, same stimulation frequencies could induce LTP or long-term depression depending on the dendritic location of the synapse. Next, we found that rhythmic stimuli induced greater plasticity then irregular stimuli. Furthermore, brief bursts of spikes significantly expanded the timing dependence of plasticity. Finally, we found that in the ~5-15-Hz frequency range both rate- and timing-dependent plasticity mechanisms work synergistically to render the synaptic plasticity most sensitive to spike timing. These findings provide computational evidence that oscillations can have a profound influence on the plasticity of an NMDAR-dependent synapse, and show a novel role for the dendritic morphology in this process.

  • 19.
    Kumar, Arvind
    et al.
    Albert Ludwigs University, Germany; Brown University, United States .
    Rotter, Stefan
    Aertsen, Ad
    Conditions for propagating synchronous spiking and asynchronous firing rates in a cortical network model2008In: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 28, no 20, p. 5268-5280Article in journal (Refereed)
    Abstract [en]

    Isolated feedforward networks (FFNs) of spiking neurons have been studied extensively for their ability to propagate transient synchrony and asynchronous firing rates, in the presence of activity independent synaptic background noise (Diesmann et al., 1999; van Rossum et al., 2002). In a biologically realistic scenario, however, the FFN should be embedded in a recurrent network, such that the activity in the FFN and the network activity may dynamically interact. Previously, transient synchrony propagating in an FFN was found to destabilize the dynamics of the embedding network (Mehring et al., 2003). Here, we show that by modeling synapses as conductance transients, rather than current sources, it is possible to embed and propagate transient synchrony in the FFN, without destabilizing the background network dynamics. However, the network activity has a strong impact on the type of activity that can be propagated in the embedded FFN. Global synchrony and high firing rates in the embedding network prohibit the propagation of both, synchronous and asynchronous spiking activity. In contrast, asynchronous low-rate network states support the propagation of both, synchronous spiking and asynchronous, but only low firing rates. In either case, spiking activity tends to synchronize as it propagates, challenging the feasibility to transmit information in asynchronous firing rates. Finally, asynchronous network activity allows to embed more than one FFN, with the amount of cross talk depending on the degree of overlap in the FFNs. This opens the possibility of computational mechanisms using transient synchrony among the activities in multiple FFNs.

  • 20.
    Kumar, Arvind
    et al.
    University of Freiburg,Germany .
    Rotter, Stefan
    Aertsen, Ad
    Spiking activity propagation in neuronal networks: reconciling different perspectives on neural coding2010In: Nature Reviews Neuroscience, ISSN 1471-003X, E-ISSN 1471-0048, Vol. 11, no 9, p. 615-627Article in journal (Refereed)
    Abstract [en]

    The brain is a highly modular structure. To exploit modularity, it is necessary that spiking activity can propagate from one module to another while preserving the information it carries. Therefore, reliable propagation is one of the key properties of a candidate neural code. Surprisingly, the conditions under which spiking activity can be propagated have received comparatively little attention in the experimental literature. By contrast, several computational studies in the last decade have addressed this issue. Using feedforward networks (FFNs) as a generic network model, they have identified two dynamical activity modes that support the propagation of either asynchronous (rate code) or synchronous (temporal code) spiking. Here, we review the dichotomy of asynchronous and synchronous propagation in FFNs, propose their integration into a single extended conceptual framework and suggest experimental strategies to test our hypothesis.

  • 21.
    Kumar, Arvind
    et al.
    Bernstein Center for Computational Neuroscience, Germany .
    Schrader, Sven
    Aertsen, Ad
    Rotter, Stefan
    The high-conductance state of cortical networks2008In: Neural Computation, ISSN 0899-7667, E-ISSN 1530-888X, Vol. 20, p. 1-43Article in journal (Refereed)
    Abstract [en]

    We studied the dynamics of large networks of spiking neurons with conductance-based (nonlinear) synapses and compared them to networks with current-based (linear) synapses. For systems with sparse and inhibition-dominated recurrent connectivity, weak external inputs induced asynchronous irregular firing at low rates. Membrane potentials fluctuated a few millivolts below threshold, and membrane conductances were increased by a factor 2 to 5 with respect to the resting state. This combination of parameters characterizes the ongoing spiking activity typically recorded in the cortex in vivo. Many aspects of the asynchronous irregular state in conductance-based networks could be sufficiently well characterized with a simple numerical mean field approach. In particular, it correctly predicted an intriguing property of conductance-based networks that does not appear to be shared by current-based models: they exhibit states of low-rate asynchronous irregular activity that persist for some period of time even in the absence of external inputs and without cortical pacemakers. Simulations of larger networks (up to 350,000 neurons) demonstrated that the survival time of self-sustained activity increases exponentially with network size.

  • 22.
    Kumar, Arvind
    et al.
    University of Freiburg, Germany .
    Singh, Harinder Pal
    Information homeostasis as a fundamental principle governing the cell division and death2011In: Medical Hypotheses, ISSN 0306-9877, E-ISSN 1532-2777, Vol. 77, no 3, p. 318-322Article in journal (Refereed)
    Abstract [en]

    To express the genetic information with minimal error is one of the key functions of a cell. Here we propose an information theory based, phenomenological model for the expression of genetic information. Based on the model we propose the concept of 'information homeostasis' which ensures that genetic information is expressed with minimal error. We suggest that together with energy homeostasis, information homeostasis is a fundamental working principle of a biological cell. This model proposes a novel explanation of why a cell divides and why it stops to divide and, thus, provides novel insights into oncogenesis and various neuro-degenerative diseases. Moreover, the model suggests a theoretical framework to understand cell division and death, beyond specific biochemical pathways.

  • 23.
    Kumar, Arvind
    et al.
    University of Freiburg, Germany .
    Vlachos, Ioannis
    Aertsen, Ad
    Boucsein, Clemens
    Challenges of understanding brain function by selective modulation of neuronal subpopulations2013In: TINS - Trends in Neurosciences, ISSN 0166-2236, E-ISSN 1878-108X, Vol. 36, no 10, p. 579-586Article, review/survey (Refereed)
    Abstract [en]

    Neuronal networks confront researchers with an overwhelming complexity of interactions between their elements. A common approach to understanding neuronal processing is to reduce complexity by defining subunits and infer their functional role by selectively modulating them. However, this seemingly straightforward approach may lead to confusing results if the network exhibits parallel pathways leading to recurrent connectivity. We demonstrate limits of the selective modulation approach and argue that, even though highly successful in some instances, the approach fails in networks with complex connectivity. We argue to refine experimental techniques by carefully considering the structural features of the neuronal networks involved. Such methods could dramatically increase the effectiveness of selective modulation and may lead to a mechanistic understanding of principles underlying brain function.

  • 24. Meier, Ralph
    et al.
    Kumar, Arvind
    Institute of Biology III, Albert-Ludwigs-University, Germany .
    Schulze-Bonhage, Andreas
    Aertsen, Ad
    Comparison of dynamical states of random networks with human EEG2007In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 70, no 10-12, p. 1843-1847Article in journal (Refereed)
    Abstract [en]

    Existing models of EEG have mainly focused on relations to network dynamics characterized by firing rates [L. de Arcangelis, H.J. Herrmann, C. Perrone-Capano, Activity-dependent brain model explaining EEG spectra, arXiv:q-bio.NC/0411043 v1, 23 Nov 2004; D.T. Liley, D.M. Alexander, J.J. Wright, M.D. Aldous, Alpha rhythm emerges from large-scale networks of realistically coupled multicompartmental model cortical neurons, Network 10(1) (1999) 79-92; O. David, J.K. Friston, A neural mass model for MEG/EEG: coupling and neuronal dynamics, NeuroImage 20 (2003) 1743-1755]. Generally, these models assume that there exists a linear mapping between network firing rates and EEG states. However, firing rate is only one of several descriptors for network activity states. Other relevant descriptors are synchrony and irregularity of firing patterns [N. Brunel, Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons, J. Comput. Neurosci. 8(3) (2000) 183-208]. To develop a better understanding of the EEG we need to relate these state descriptors to EEG states. Here, we try to go beyond the firing rate based approaches described in [D.T. Liley, D.M. Alexander, J.J. Wright, M.D. Aldous, Alpha rhythm emerges from large-scale networks of realistically coupled multicompartmental model cortical neurons, Network 10(1) (1999) 79-92; O. David, J.K. Friston, A neural mass model for MEG/EEG: coupling and neuronal dynamics, NeuroImage 20 (2003) 1743-1755] and relate synchronicity and irregularity in the network to EEG states. We show that the transformation between network activity and EEG can be approximately mediated by linear kernel with the shape of an α- or γ-function, allowing us a comparison between EEG states and network activity space. We find that the simulated EEG generated from asynchronous irregular type network activity is closely related to the human EEG recorded in the awake state, evaluated using power spectral density characteristics.

  • 25.
    Mengiste, Simachew
    et al.
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. University of Freiburg, German.
    Aertsen, Ad
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. University of Freiburg, German.
    Effect of edge pruning on structural controllability and observability of complex networks2015In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 5, p. 18145-, article id 18145Article in journal (Refereed)
    Abstract [en]

    Controllability and observability of complex systems are vital concepts in many fields of science. The network structure of the system plays a crucial role in determining its controllability and observability. Because most naturally occurring complex systems show dynamic changes in their network connectivity, it is important to understand how perturbations in the connectivity affect the controllability of the system. To this end, we studied the control structure of different types of artificial, social and biological neuronal networks (BNN) as their connections were progressively pruned using four different pruning strategies. We show that the BNNs are more similar to scale-free networks than to small-world networks, when comparing the robustness of their control structure to structural perturbations. We introduce a new graph descriptor, 'the cardinality curve', to quantify the robustness of the control structure of a network to progressive edge pruning. Knowing the susceptibility of control structures to different pruning methods could help design strategies to destroy the control structures of dangerous networks such as epidemic networks. On the other hand, it could help make useful networks more resistant to edge attacks.

  • 26. Mirzaei, Amin
    et al.
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). University of Freiburg, Germany.
    Leventhal, Daniel
    Mallet, Nicolas
    Aertsen, Ad
    Berke, Joshua
    Schmidt, Robert
    Sensorimotor Processing in the Basal Ganglia Leads to Transient Beta Oscillations during Behavior2017In: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 37, no 46, p. 11220-11232Article in journal (Refereed)
    Abstract [en]

    Brief epochs of beta oscillations have been implicated in sensorimotor control in the basal ganglia of task-performing healthy animals. However, which neural processes underlie their generation and how they are affected by sensorimotor processing remains unclear. To determine the mechanisms underlying transient beta oscillations in the LFP, we combined computational modeling of the subthalamo-pallidal network for the generation of beta oscillations with realistic stimulation patterns derived from single-unit data recorded from different basal ganglia subregions in rats performing a cued choice task. In the recordings, we found distinct firing patterns in the striatum, globus pallidus, and subthalamic nucleus related to sensory and motor events during the behavioral task. Using these firing patterns to generate realistic inputs to our network model led to transient beta oscillations with the same time course as the rat LFP data. In addition, our model can account for further nonintuitive aspects of beta modulation, including beta phase resets after sensory cues and correlations with reaction time. Overall, our model can explain how the combination of temporally regulated sensory responses of the subthalamic nucleus, ramping activity of the subthalamic nucleus, and movement-related activity of the globus pallidus leads to transient beta oscillations during behavior.

  • 27. Najac, Marion
    et al.
    Diez, Alvaro
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Benito, Nuria
    Charpak, Serge
    De Saint Jan, Didier
    Intraglomerular Lateral Inhibition Promotes Spike Timing Variability in Principal Neurons of the Olfactory Bulb2015In: Journal of Neuroscience, ISSN 0270-6474, E-ISSN 1529-2401, Vol. 35, no 10, p. 4319-4331Article in journal (Refereed)
    Abstract [en]

    The activity of mitral and tufted cells, the principal neurons of the olfactory bulb, is modulated by several classes of interneurons. Among them, diverse periglomerular (PG) cell types interact with the apical dendrites of mitral and tufted cells inside glomeruli at the first stage of olfactory processing. We used paired recording in olfactory bulb slices and two-photon targeted patch-clamp recording in vivo to characterize the properties and connections of a genetically identified population of PG cells expressing enhanced yellow fluorescent protein (EYFP) under the control of the Kv3.1 potassium channel promoter. Kv3.1–EYFP+ PG cells are axonless and monoglomerular neurons that constitute ∼30% of all PG cells and include calbindin-expressing neurons. They respond to an olfactory nerve stimulation with a short barrage of excitatory inputs mediated by mitral, tufted, and external tufted cells, and, in turn, they indiscriminately release GABA onto principal neurons. They are activated by even the weakest olfactory nerve input or by the discharge of a single principal neuron in slices and at each respiration cycle in anesthetized mice. They participate in a fast-onset intraglomerular lateral inhibition between principal neurons from the same glomerulus, a circuit that reduces the firing rate and promotes spike timing variability in mitral cells. Recordings in other PG cell subtypes suggest that this pathway predominates in generating glomerular inhibition. Intraglomerular lateral inhibition may play a key role in olfactory processing by reducing the similarity of principal cells discharge in response to the same incoming input.

  • 28. Sahasranamam, Ajith
    et al.
    Vlachos, Ioannis
    Aertsen, Ad
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). Bernstein Center Freiburg, University of Freiburg, Freiburg, Germany.
    Dynamical state of the network determines the efficacy of single neuron properties in shaping the network activity2016In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 6, article id 26029Article in journal (Refereed)
    Abstract [en]

    Spike patterns are among the most common electrophysiological descriptors of neuron types. Surprisingly, it is not clear how the diversity in firing patterns of the neurons in a network affects its activity dynamics. Here, we introduce the state-dependent stochastic bursting neuron model allowing for a change in its firing patterns independent of changes in its input-output firing rate relationship. Using this model, we show that the effect of single neuron spiking on the network dynamics is contingent on the network activity state. While spike bursting can both generate and disrupt oscillations, these patterns are ineffective in large regions of the network state space in changing the network activity qualitatively. Finally, we show that when single-neuron properties are made dependent on the population activity, a hysteresis like dynamics emerges. This novel phenomenon has important implications for determining the network response to time-varying inputs and for the network sensitivity at different operating points.

  • 29.
    Schnepel, Philipp
    et al.
    University of California Berkeley, USA.
    Kumar, Arvind
    Bernstein Center Freiburg, Germany and University of Freiburg, Germany.
    Zohar, Mihael
    University of Freiburg, Germany.
    Aertsen, Ad
    University of Freiburg, Germany.
    Boucsein, Clemens
    University of Freiburg, Germany.
    Physiology and impact of horizontal connections in rat neocortex2014In: Cerebral Cortex, ISSN 1047-3211, E-ISSN 1460-2199Article in journal (Refereed)
    Abstract [en]

    Cortical information processing at the cellular level has predominantly been studied in local networks, which are dominated by strong vertical connectivity between layers. However, recent studies suggest that the bulk of axons targeting pyramidal neurons most likely originate from outside this local range, emphasizing the importance of horizontal connections. We mapped a subset of these connections to L5B pyramidal neurons in rat somatosensory cortex with photostimulation, identifying intact projections up to a lateral distance of 2 mm. Our estimates of the spatial distribution of cells presynaptic to L5B pyramids support the idea that the majority is located outside the local volume. The synaptic physiology of horizontal connections does not differ markedly from that of local connections, whereas the layer and cell-type-dependent pattern of innervation does. Apart from L2/3, L6A provides a strong source of horizontal connections. Implementing our data into a spiking neuronal network model shows that more horizontal connections promote robust asynchronous ongoing activity states and reduce noise correlations in stimulus-induced activity.

  • 30. Spreizer, Sebastian
    et al.
    Angelhuber, Martin
    Bahuguna, Jyotika
    Aertsen, Ad
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST).
    Activity Dynamics and Signal Representation in a Striatal Network Model with Distance-Dependent Connectivity2017In: eNeuro, ISSN 2373-2822Article in journal (Refereed)
    Abstract [en]

    The striatum is the main input nucleus of the basal ganglia. Characterizing striatal activity dynamics is crucial to understanding mechanisms underlying action-selection, -initiation and -execution. Here, we studied the effects of spatial network connectivity on the spatio-temporal structure of striatal activity. We show that a striatal network with non-monotonically changing distance-dependent connectivity (according to a Gamma distribution) can exhibit a wide repertoire of spatio-temporal dynamics, ranging from spatially homogeneous asynchronous-irregular (AI) activity to a state with stable, spatially localized activity bumps, as in ‘winner-take-all’ (WTA) dynamics. Among these regimes, the unstable activity bumps (Transition Activity, TA) regime closely resembles the experimentally observed spatio-temporal activity dynamics and neuronal assemblies in the striatum. By contrast, striatal networks with monotonically decreasing distance-dependent connectivity (in a Gaussian fashion) can only exhibit an AI state. Thus, given the observation of spatially compact neuronal clusters in the striatum, our model suggests that recurrent connectivity among striatal projection neurons should vary non-monotonically. In brain disorders such as Parkinson’s disease, increased cortical inputs and high striatal firing rates are associated with a reduction in stimulus sensitivity. Consistent with this, our model suggests that strong cortical inputs drive the striatum to a WTA state, leading to low stimulus sensitivity and high variability. By contrast, the AI and TA states show high stimulus sensitivity and reliability. Thus, based on these results, we propose that in a healthy state the striatum operates in a AI/TA state and that lack of dopamine pushes it into a WTA state.Significance Statement Recent findings suggest that striatal activity is organized in spatially compact neuron clusters. Here, we show that striatal projection neurons should have a non-monotonically changing distance-dependent connectivity to obtain spatially localized activity patterns in striatum. Among the different states a striatal network can show, asynchronous-irregular and transition activity states closely resemble striatal activity in the healthy state. By contrast, strong cortical inputs as observed in Parkinson’s disease (PD) drive the network into a winner-take-all state, in which the striatum looses its stimulus sensitivity. Thus, our model makes specific predictions about the spatial network connectivity in the striatum and provides new insights about how the striatum might make a transition from a healthy state to a PD state.

  • 31.
    Vlachos, Ioannis
    et al.
    University of Freiburg, Freiburg, Germany .
    Aertsen, Ad
    University of Freiburg, Freiburg, Germany .
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB. University of Freiburg, Freiburg, Germany .
    Beyond Statistical Significance: Implications of Network Structure on Neuronal Activity2012In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 8, no 1, p. e1002311-Article in journal (Refereed)
    Abstract [en]

    It is a common and good practice in experimental sciences to assess the statistical significance of measured outcomes. For this, the probability of obtaining the actual results is estimated under the assumption of an appropriately chosen null-hypothesis. If this probability is smaller than some threshold, the results are deemed statistically significant and the researchers are content in having revealed, within their own experimental domain, a "surprising" anomaly, possibly indicative of a hitherto hidden fragment of the underlying "ground-truth". What is often neglected, though, is the actual importance of these experimental outcomes for understanding the system under investigation. We illustrate this point by giving practical and intuitive examples from the field of systems neuroscience. Specifically, we use the notion of embeddedness to quantify the impact of a neuron's activity on its downstream neurons in the network. We show that the network response strongly depends on the embeddedness of stimulated neurons and that embeddedness is a key determinant of the importance of neuronal activity on local and downstream processing. We extrapolate these results to other fields in which networks are used as a theoretical framework.

  • 32. Vlachos, Ioannis
    et al.
    Deniz, Taskin
    Aertsen, Ad
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Science and Technology (CST). University of Freiburg, Germany.
    Recovery of dynamics and function in spiking neural networks with closed-loop control2016In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 12, no 2, p. e1004720-Article in journal (Refereed)
    Abstract [en]

    There is a growing interest in developing novel brain stimulation methods to control disease-related aberrant neural activity and to address basic neuroscience questions. Conventional methods for manipulating brain activity rely on open-loop approaches that usually lead to excessive stimulation and, crucially, do not restore the original computations performed by the network. Thus, they are often accompanied by undesired side-effects. Here, we introduce delayed feedback control (DFC), a conceptually simple but effective method, to control pathological oscillations in spiking neural networks (SNNs). Using mathematical analysis and numerical simulations we show that DFC can restore a wide range of aberrant network dynamics either by suppressing or enhancing synchronous irregular activity. Importantly, DFC, besides steering the system back to a healthy state, also recovers the computations performed by the underlying network. Finally, using our theory we identify the role of single neuron and synapse properties in determining the stability of the closed-loop system.

  • 33. Vlachos, Ioannis
    et al.
    Herry, Cyril
    Lüthi, Andreas
    Aertsen, Ad
    University of Freiburg, Germany.
    Kumar, Arvind
    University of Freiburg, Germany.
    Context-Dependent Encoding of Fear and Extinction Memories in a Large-Scale Network Model of the Basal Amygdala2011In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 7, no 3, article id e1001104Article in journal (Refereed)
    Abstract [en]

    The basal nucleus of the amygdala (BA) is involved in the formation of context-dependent conditioned fear and extinction memories. To understand the underlying neural mechanisms we developed a large-scale neuron network model of the BA, composed of excitatory and inhibitory leaky-integrate-and-fire neurons. Excitatory BA neurons received conditioned stimulus (CS)-related input from the adjacent lateral nucleus (LA) and contextual input from the hippocampus or medial prefrontal cortex (mPFC). We implemented a plasticity mechanism according to which CS and contextual synapses were potentiated if CS and contextual inputs temporally coincided on the afferents of the excitatory neurons. Our simulations revealed a differential recruitment of two distinct subpopulations of BA neurons during conditioning and extinction, mimicking the activation of experimentally observed cell populations. We propose that these two subgroups encode contextual specificity of fear and extinction memories, respectively. Mutual competition between them, mediated by feedback inhibition and driven by contextual inputs, regulates the activity in the central amygdala (CEA) thereby controlling amygdala output and fear behavior. The model makes multiple testable predictions that may advance our understanding of fear and extinction memories.

  • 34. Vlachos, Ioannis
    et al.
    Zaytsev, Yury V
    Spreizer, Sebastian
    Aertsen, Ad
    Kumar, Arvind
    KTH, School of Computer Science and Communication (CSC), Computational Biology, CB.
    Neural system prediction and identification challenge2013In: Frontiers in Neuroinformatics, ISSN 1662-5196, E-ISSN 1662-5196, Vol. 7, no DEC, p. 43-Article in journal (Refereed)
    Abstract [en]

    Can we infer the function of a biological neural network (BNN) if we know the connectivity and activity of all its constituent neurons? This question is at the core of neuroscience and, accordingly, various methods have been developed to record the activity and connectivity of as many neurons as possible. Surprisingly, there is no theoretical or computational demonstration that neuronal activity and connectivity are indeed sufficient to infer the function of a BNN. Therefore, we pose the Neural Systems Identification and Prediction Challenge (nuSPIC). We provide the connectivity and activity of all neurons and invite participants (1) to infer the functions implemented (hard-wired) in spiking neural networks (SNNs) by stimulating and recording the activity of neurons and, (2) to implement predefined mathematical/biological functions using SNNs. The nuSPICs can be accessed via a web-interface to the NEST simulator and the user is not required to know any specific programming language. Furthermore, the nuSPICs can be used as a teaching tool. Finally, nuSPICs use the crowd-sourcing model to address scientific issues. With this computational approach we aim to identify which functions can be inferred by systematic recordings of neuronal activity and connectivity. In addition, nuSPICs will help the design and application of new experimental paradigms based on the structure of the SNN and the presumed function which is to be discovered.

  • 35. Yim, Man Yi
    et al.
    Aertsen, Ad
    Kumar, Arvind
    Bernstein Center Freiburg and Neurobiology & Biophysics, Faculty of Biology, University of Freiburg, Freiburg, Germany.
    Significance of Input Correlations in Striatal Function2011In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 7, no 11Article in journal (Refereed)
    Abstract [en]

    The striatum is the main input station of the basal ganglia and is strongly associated with motor and cognitive functions. Anatomical evidence suggests that individual striatal neurons are unlikely to share their inputs from the cortex. Using a biologically realistic large-scale network model of striatum and cortico-striatal projections, we provide a functional interpretation of the special anatomical structure of these projections. Specifically, we show that weak pairwise correlation within the pool of inputs to individual striatal neurons enhances the saliency of signal representation in the striatum. By contrast, correlations among the input pools of different striatal neurons render the signal representation less distinct from background activity. We suggest that for the network architecture of the striatum, there is a preferred cortico-striatal input configuration for optimal signal representation. It is further enhanced by the low-rate asynchronous background activity in striatum, supported by the balance between feedforward and feedback inhibitions in the striatal network. Thus, an appropriate combination of rates and correlations in the striatal input sets the stage for action selection presumably implemented in the basal ganglia.

  • 36.
    Yim, Man Yi
    et al.
    University of Hong Kong, Hong Kong.
    Kumar, Arvind
    Bernstein Center Freiburg, University of Freiburg, Germany .
    Aertsen, Ad
    Rotter, Stefan
    Impact of correlated inputs to neurons: modeling observations from in vivo intracellular recordings2014In: Journal of Computational Neuroscience, ISSN 0929-5313, E-ISSN 1573-6873, Vol. 37, no 2, p. 293-304Article in journal (Refereed)
    Abstract [en]

    In vivo recordings in rat somatosensory cortex suggest that excitatory and inhibitory inputs are often correlated during spontaneous and sensory-evoked activity. Using a computational approach, we study how the interplay of input correlations and timing observed in experiments controls the spiking probability of single neurons. Several correlation-based mechanisms are identified, which can effectively switch a neuron on and off. In addition, we investigate the transfer of input correlation to output correlation in pairs of neurons, at the spike train and the membrane potential levels, by considering spike-driving and non-spike-driving inputs separately. In particular, we propose a plausible explanation for the in vivo finding that membrane potentials in neighboring neurons are correlated, but the spike-triggered averages of membrane potentials preceding a spike are not: Neighboring neurons possibly receive an ongoing bombardment of correlated subthreshold background inputs, and occasionally uncorrelated spike-driving inputs.

1 - 36 of 36
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