Journal of Computational Neuroscience

Multisensory integration in the superior colliculus: a neural network model.

Publication Date: 2008 May 14 PMID: 18478323
Authors: Ursino, M. - Cuppini, C. - Magosso, E. - Serino, A. - di Pellegrino, G.
Journal: J Comput Neurosci

Neurons in the superior colliculus (SC) are known to integrate stimuli of different modalities (e.g., visual and auditory) following specific properties. In this work, we present a mathematical model of the integrative response of SC neurons, in order to suggest a possible physiological mechanism underlying multisensory integration in SC. The model includes three distinct neural areas: two unimodal areas (auditory and visual) are devoted to a topological representation of external stimuli, and communicate via synaptic connections with a third downstream area (in the SC) responsible for multisensory integration. The present simulations show that the model, with a single set of parameters, can mimic various responses to different combinations of external stimuli including the inverse effectiveness, both in terms of multisensory enhancement and contrast, the existence of within- and cross-modality suppression between spatially disparate stimuli, a reduction of network settling time in response to cross-modal stimuli compared with individual stimuli. The model suggests that non-linearities in neural responses and synaptic (excitatory and inhibitory) connections can explain several aspects of multisensory integration.

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Dendrites determine the contribution of after depolarization potentials (ADPs) to generation of repetitive action potentials in hypothalamic gonadotropin releasing-hormone (GnRH) neurons.

Publication Date: 2008 May 7 PMID: 18461432
Authors: Roberts, C. B. - O'Boyle, M. P. - Suter, K. J.
Journal: J Comput Neurosci

The impact of structure in modulating synaptic signals originating in dendrites is widely recognized. In this study, we focused on the impact of dendrite morphology on a local spike generating mechanism which has been implicated in hormone secretion, the after depolarization potential (ADP). Using multi-compartmental models of hypothalamic GnRH neurons, we systematically truncated dendrite length and determined the consequence on ADP amplitude and repetitive firing. Decreasing the length of the dendrite significantly increased the amplitude of the ADP and increased repetitive firing. These effects were observed in dendrites both with and without active conductances suggesting they largely reflect passive characteristics of the dendrite. In order to test the findings of the model, we performed whole-cell recordings in GnRH neurons and elicited ADPs using current injection. During recordings, neurons were filled with biocytin so that we could determine dendritic and total projection (dendrite plus axon) length. Neurons exhibited ADPs and increasing ADP amplitude was associated with decreasing dendrite length, in keeping with the predictions of the models. Thus, despite the relatively simple morphology of the GnRH neuron's dendrite, it can still exert a substantial impact on the final neuronal output.

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Compartmental neural simulations with spatial adaptivity.

Publication Date: 2008 May 6 PMID: 18459041
Authors: Rempe, M. J. - Spruston, N. - Kath, W. L. - Chopp, D. L.
Journal: J Comput Neurosci

Since their inception, computational models have become increasingly complex and useful counterparts to laboratory experiments within the field of neuroscience. Today several software programs exist to solve the underlying mathematical system of equations, but such programs typically solve these equations in all parts of a cell (or network of cells) simultaneously, regardless of whether or not all of the cell is active. This approach can be inefficient if only part of the cell is active and many simulations must be performed. We have previously developed a numerical method that provides a framework for spatial adaptivity by making the computations local to individual branches rather than entire cells (Rempe and Chopp, SIAM Journal on Scientific Computing, 28: 2139-2161, 2006). Once the computation is reduced to the level of branches instead of cells, spatial adaptivity is straightforward: the active regions of the cell are detected and computational effort is focused there, while saving computations in other regions of the cell that are at or near rest. Here we apply the adaptive method to four realistic neuronal simulation scenarios and demonstrate its improved efficiency over non-adaptive methods. We find that the computational cost of the method scales with the amount of activity present in the simulation, rather than the physical size of the system being simulated. For certain problems spatial adaptivity reduces the computation time by up to 80%.

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Path-finding in real and simulated rats: assessing the influence of path characteristics on navigation learning.

Publication Date: 2008 Apr 30 PMID: 18446432
Authors: Tamosiunaite, M. - Ainge, J. - Kulvicius, T. - Porr, B. - Dudchenko, P. - Worgotter, F.
Journal: J Comput Neurosci

A large body of experimental evidence suggests that the hippocampal place field system is involved in reward based navigation learning in rodents. Reinforcement learning (RL) mechanisms have been used to model this, associating the state space in an RL-algorithm to the place-field map in a rat. The convergence properties of RL-algorithms are affected by the exploration patterns of the learner. Therefore, we first analyzed the path characteristics of freely exploring rats in a test arena. We found that straight path segments with mean length 23 cm up to a maximal length of 80 cm take up a significant proportion of the total paths. Thus, rat paths are biased as compared to random exploration. Next we designed a RL system that reproduces these specific path characteristics. Our model arena is covered by overlapping, probabilistically firing place fields (PF) of realistic size and coverage. Because convergence of RL-algorithms is also influenced by the state space characteristics, different PF-sizes and densities, leading to a different degree of overlap, were also investigated. The model rat learns finding a reward opposite to its starting point. We observed that the combination of biased straight exploration, overlapping coverage and probabilistic firing will strongly impair the convergence of learning. When the degree of randomness in the exploration is increased, convergence improves, but the distribution of straight path segments becomes unrealistic and paths become 'wiggly'. To mend this situation without affecting the path characteristic two additional mechanisms are implemented: A gradual drop of the learned weights (weight decay) and path length limitation, which prevents learning if the reward is not found after some expected time. Both mechanisms limit the memory of the system and thereby counteract effects of getting trapped on a wrong path. When using these strategies individually divergent cases get substantially reduced and for some parameter settings no divergence was found anymore at all. Using weight decay and path length limitation at the same time, convergence is not much improved but instead time to convergence increases as the memory limiting effect is getting too strong. The degree of improvement relies also on the size and degree of overlap (coverage density) in the place field system. The used combination of these two parameters leads to a trade-off between convergence and speed to convergence. Thus, this study suggests that the role of the PF-system in navigation learning cannot be considered independently from the animals' exploration pattern.

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Odor supported place cell model and goal navigation in rodents.

Publication Date: 2008 Apr 23 PMID: 18431616
Authors: Kulvicius, T. - Tamosiunaite, M. - Ainge, J. - Dudchenko, P. - Worgotter, F.
Journal: J Comput Neurosci

Experiments with rodents demonstrate that visual cues play an important role in the control of hippocampal place cells and spatial navigation. Nevertheless, rats may also rely on auditory, olfactory and somatosensory stimuli for orientation. It is also known that rats can track odors or self-generated scent marks to find a food source. Here we model odor supported place cells by using a simple feed-forward network and analyze the impact of olfactory cues on place cell formation and spatial navigation. The obtained place cells are used to solve a goal navigation task by a novel mechanism based on self-marking by odor patches combined with a Q-learning algorithm. We also analyze the impact of place cell remapping on goal directed behavior when switching between two environments. We emphasize the importance of olfactory cues in place cell formation and show that the utility of environmental and self-generated olfactory cues, together with a mixed navigation strategy, improves goal directed navigation.

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Modeling of quantal neurotransmitter release kinetics in the presence of fixed and mobile calcium buffers.

Publication Date: 2008 Apr 22 PMID: 18427967
Authors: Gilmanov, I. R. - Samigullin, D. V. - Vyskocil, F. - Nikolsky, E. E. - Bukharaeva, E. A.
Journal: J Comput Neurosci

The local calcium concentration in the active zone of secretion determines the number and kinetics of neurotransmitter quanta released after the arrival of a nerve action potential in chemical synapses. The small size of mammalian neuromuscular junctions does not allow direct measurement of the correlation between calcium influx, the state of endogenous calcium buffers determining the local concentration of calcium and the time course of quanta exocytosis. In this work, we used computer modeling of quanta release kinetics with various levels of calcium influx and in the presence of endogenous calcium buffers with varying mobilities. The results of this modeling revealed the desynchronization of quanta release under low calcium influx in the presence of an endogenous fixed calcium buffer, with a diffusion coefficient much smaller than that of free Ca(2+), and synchronization occurred upon adding a mobile buffer. This corresponds to changes in secretion time course parameters found experimentally (Samigullin et al., Physiol Res 54:129-132, 2005; Bukharaeva et al., J Neurochem 100:939-949, 2007).

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Fluctuation-driven rhythmogenesis in an excitatory neuronal network with slow adaptation.

Publication Date: 2008 Apr 22 PMID: 18427966
Authors: Nesse, W. H. - Borisyuk, A. - Bressloff, P. C.
Journal: J Comput Neurosci

We study an excitatory all-to-all coupled network of N spiking neurons with synaptically filtered background noise and slow activity-dependent hyperpolarization currents. Such a system exhibits noise-induced burst oscillations over a range of values of the noise strength (variance) and level of cell excitability. Since both of these quantities depend on the rate of background synaptic inputs, we show how noise can provide a mechanism for increasing the robustness of rhythmic bursting and the range of burst frequencies. By exploiting a separation of time scales we also show how the system dynamics can be reduced to low-dimensional mean field equations in the limit N --> infinity. Analysis of the bifurcation structure of the mean field equations provides insights into the dynamical mechanisms for initiating and terminating the bursts.

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Motor pattern selection by combinatorial code of interneuronal pathways.

Publication Date: 2008 Apr 19 PMID: 18425570
Authors: Stein, W. - Straub, O. - Ausborn, J. - Mader, W. - Wolf, H.
Journal: J Comput Neurosci

We use a modeling approach to examine ideas derived from physiological network analyses, pertaining to the switch of a motor control network between two opposite control modes. We studied the femur-tibia joint control system of the insect leg, and its switch between resistance reflex in posture control and "active reaction" in walking, both elicited by the same sensory input. The femur-tibia network was modeled by fitting the responses of model neurons to those obtained in animals. The strengths of 16 interneuronal pathways that integrate sensory input were then assigned three different values and varied independently, generating a database of more than 43 million network variants. We demonstrate that the same neural network can produce the two different behaviors, depending on the combinatorial code of interneuronal pathways. That is, a switch between behaviors, such as standing to walking, can be brought about by altering the strengths of selected sensory integration pathways.

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Spontaneous coordinated activity in cultured networks: Analysis of multiple ignition sites, primary circuits, and burst phase delay distributions.

Publication Date: 2008 Jun PMID: 18066657
Authors: Ham, M. I. - Bettencourt, L. M. - McDaniel, F. D. - Gross, G. W.
Journal: J Comput Neurosci

All higher order central nervous systems exhibit spontaneous neural activity, though the purpose and mechanistic origin of such activity remains poorly understood. We quantitatively analyzed the ignition and spread of collective spontaneous electrophysiological activity in networks of cultured cortical neurons growing on microelectrode arrays. Leader neurons, which form a mono-synaptically connected primary circuit, and initiate a majority of network bursts were found to be a small subset of recorded neurons. Leader/follower firing delay times formed temporally stable positively skewed distributions. Blocking inhibitory synapses usually resulted in shorter delay times with reduced variance. These distributions are characterizations of general aspects of internal network dynamics and provide estimates of pair-wise synaptic distances. The resulting analysis produced specific quantitative constraints and insights into the activation patterns of collective neuronal activity in self-organized cortical networks, which may prove useful for models emulating spontaneously active systems.

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A modeling comparison of projection neuron- and neuromodulator-elicited oscillations in a central pattern generating network.

Publication Date: 2008 Jun PMID: 18046635
Authors: Kintos, N. - Nusbaum, M. P. - Nadim, F.
Journal: J Comput Neurosci

Many central pattern generating networks are influenced by synaptic input from modulatory projection neurons. The network response to a projection neuron is sometimes mimicked by bath applying the neuronally-released modulator, despite the absence of network interactions with the projection neuron. One interesting example occurs in the crab stomatogastric ganglion (STG), where bath applying the neuropeptide pyrokinin (PK) elicits a gastric mill rhythm which is similar to that elicited by the projection neuron modulatory commissural neuron 1 (MCN1), despite the absence of PK in MCN1 and the fact that MCN1 is not active during the PK-elicited rhythm. MCN1 terminals have fast and slow synaptic actions on the gastric mill network and are presynaptically inhibited by this network in the STG. These local connections are inactive in the PK-elicited rhythm, and the mechanism underlying this rhythm is unknown. We use mathematical and biophysically-realistic modeling to propose potential mechanisms by which PK can elicit a gastric mill rhythm that is similar to the MCN1-elicited rhythm. We analyze slow-wave network oscillations using simplified mathematical models and, in parallel, develop biophysically-realistic models that account for fast, action potential-driven oscillations and some spatial structure of the network neurons. Our results illustrate how the actions of bath-applied neuromodulators can mimic those of descending projection neurons through mathematically similar but physiologically distinct mechanisms.

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Inferring connection proximity in networks of electrically coupled cells by subthreshold frequency response analysis.

Publication Date: 2008 Jun PMID: 18044016
Authors: Cali, C. - Berger, T. K. - Pignatelli, M. - Carleton, A. - Markram, H. - Giugliano, M.
Journal: J Comput Neurosci

Electrical synapses continuously transfer signals bi-directionally from one cell to another, directly or indirectly via intermediate cells. Electrical synapses are common in many brain structures such as the inferior olive, the subcoeruleus nucleus and the neocortex, between neurons and between glial cells. In the cortex, interneurons have been shown to be electrically coupled and proposed to participate in large, continuous cortical syncytia, as opposed to smaller spatial domains of electrically coupled cells. However, to explore the significance of these findings it is imperative to map the electrical synaptic microcircuits, in analogy with in vitro studies on monosynaptic and disynaptic chemical coupling. Since "walking" from cell to cell over large distances with a glass pipette is challenging, microinjection of (fluorescent) dyes diffusing through gap-junctions remains so far the only method available to decipher such microcircuits even though technical limitations exist. Based on circuit theory, we derive analytical descriptions of the AC electrical coupling in networks of isopotential cells. We then suggest an operative electrophysiological protocol to distinguish between direct electrical connections and connections involving one or more intermediate cells. This method allows inferring the number of intermediate cells, generalizing the conventional coupling coefficient, which provides limited information. We validate our method through computer simulations, theoretical and numerical methods and electrophysiological paired recordings.

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Parameter estimation for bursting neural models.

Publication Date: 2008 Jun PMID: 17999167
Authors: Tien, J. H. - Guckenheimer, J.
Journal: J Comput Neurosci

This paper presents work on parameter estimation methods for bursting neural models. In our approach we use both geometrical features specific to bursting, as well as general features such as periodic orbits and their bifurcations. We use the geometry underlying bursting to introduce defining equations for burst initiation and termination, and restrict the estimation algorithms to the space of bursting periodic orbits when trying to fit periodic burst data. These geometrical ideas are combined with automatic differentiation to accurately compute parameter sensitivities for the burst timing and period. In addition to being of inherent interest, these sensitivities are used in standard gradient-based optimization algorithms to fit model burst duration and period to data. As an application, we fit Butera et al.'s (Journal of Neurophysiology 81, 382-397, 1999) model of preBotzinger complex neurons to empirical data both in control conditions and when the neuromodulator norepinephrine is added (Viemari and Ramirez, Journal of Neurophysiology 95, 2070-2082, 2006). The results suggest possible modulatory mechanisms in the preBotzinger complex, including modulation of the persistent sodium current.

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A model for modulation of neuronal synchronization by D4 dopamine receptor-mediated phospholipid methylation.

Publication Date: 2008 Jun PMID: 17929154
Authors: Kuznetsova, A. Y. - Deth, R. C.
Journal: J Comput Neurosci

We describe a new molecular mechanism of dopamine-induced membrane protein modulation that can tune neuronal oscillation frequency to attention-related gamma rhythm. This mechanism is based on the unique ability of D4 dopamine receptors (D4R) to carry out phospholipid methylation (PLM) that may affect the kinetics of ion channels. We show that by deceasing the inertia of the delayed rectifier potassium channel, a transition to 40 Hz oscillations can be achieved. Decreased potassium channel inertia shortens spike duration and decreases the interspike interval via its influence on the calcium-dependent potassium current. This mechanism leads to a transition to attention-related gamma oscillations in a pyramidal cell-interneuron network. The higher frequency and better synchronization is observed with PLM affecting pyramidal neurons only, and recurrent excitation between pyramidal neurons is important for synchronization. Thus dopamine-stimulated methylation of membrane phospholipids may be an important mechanism for modulating firing activity, while impaired methylation can contribute to disorders of attention.

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Estimation of population firing rates and current source densities from laminar electrode recordings.

Publication Date: 2008 Jun PMID: 17926125
Authors: Pettersen, K. H. - Hagen, E. - Einevoll, G. T.
Journal: J Comput Neurosci

This model study investigates the validity of methods used to interpret linear (laminar) multielectrode recordings. In computer experiments extracellular potentials from a synaptically activated population of about 1,000 pyramidal neurons are calculated using biologically realistic compartmental neuron models combined with electrostatic forward modeling. The somas of the pyramidal neurons are located in a 0.4 mm high and wide columnar cylinder, mimicking a stimulus-evoked layer-5 population in a neocortical column. Current-source density (CSD) analysis of the low-frequency part (<500 Hz) of the calculated potentials (local field potentials, LFP) based on the 'inverse' CSD method is, in contrast to the 'standard' CSD method, seen to give excellent estimates of the true underlying CSD. The high-frequency part (>750 Hz) of the potentials (multi-unit activity, MUA) is found to scale approximately as the population firing rate to the power 3/4 and to give excellent estimates of the underlying population firing rate for trial-averaged data. The MUA signal is found to decay much more sharply outside the columnar populations than the LFP.

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Distributed representation of perceptual categories in the auditory cortex.

Publication Date: 2008 Jun PMID: 17917802
Authors: Kim, H. - Bao, S.
Journal: J Comput Neurosci

Categorical perception is a process by which a continuous stimulus space is partitioned to represent discrete sensory events. Early experience has been shown to shape categorical perception and enlarge cortical representations of experienced stimuli in the sensory cortex. The present study examines the hypothesis that enlargement in cortical stimulus representations is a mechanism of categorical perception. Perceptual discrimination and identification behaviors were analyzed in model auditory cortices that incorporated sound exposure-induced plasticity effects. The model auditory cortex with over-representations of specific stimuli exhibited categorical perception behaviors for those specific stimuli. These results indicate that enlarged stimulus representations in the sensory cortex may be a mechanism for categorical perceptual learning.

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Systems level circuit model of C. elegans undulatory locomotion: mathematical modeling and molecular genetics.

Publication Date: 2008 Jun PMID: 17768672
Authors: Karbowski, J. - Schindelman, G. - Cronin, C. J. - Seah, A. - Sternberg, P. W.
Journal: J Comput Neurosci

To establish the relationship between locomotory behavior and dynamics of neural circuits in the nematode C. elegans we combined molecular and theoretical approaches. In particular, we quantitatively analyzed the motion of C. elegans with defective synaptic GABA and acetylcholine transmission, defective muscle calcium signaling, and defective muscles and cuticle structures, and compared the data with our systems level circuit model. The major experimental findings are: (1) anterior-to-posterior gradients of body bending flex for almost all strains both for forward and backward motion, and for neuronal mutants, also analogous weak gradients of undulatory frequency, (2) existence of some form of neuromuscular (stretch receptor) feedback, (3) invariance of neuromuscular wavelength, (4) biphasic dependence of frequency on synaptic signaling, and (5) decrease of frequency with increase of the muscle time constant. Based on (1) we hypothesize that the Central Pattern Generator (CPG) is located in the head both for forward and backward motion. Points (1) and (2) are the starting assumptions for our theoretical model, whose dynamical patterns are qualitatively insensitive to the details of the CPG design if stretch receptor feedback is sufficiently strong and slow. The model reveals that stretch receptor coupling in the body wall is critical for generation of the neuromuscular wave. Our model agrees with our behavioral data (3), (4), and (5), and with other pertinent published data, e.g., that frequency is an increasing function of muscle gap-junction coupling.

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