1 2011 Vol: 5. DOI: 10.3389/fninf.2011.00011

GenNet: A Platform for Hybrid Network Experiments

We describe General Network (GenNet), a software plugin for the real time experimental interface (RTXI) dynamic clamp system that allows for straightforward and flexible implementation of hybrid network experiments. This extension to RTXI allows for hybrid networks that contain an arbitrary number of simulated and real neurons, significantly improving upon previous solutions that were limited, particularly by the number of cells supported. The benefits of this system include the ability to rapidly and easily set up and perform scalable experiments with hybrid networks and the ability to scan through ranges of parameters. We present instructions for installing, running and using GenNet for hybrid network experiments and provide several example uses of the system.

Mentions
Figures
Figure 1: General Network software design diagram. Boxes represent computer code class structures and arrows represent relationships between classes. Solid arrows represent object-oriented inheritance relationships and open arrows mean that an instance of the class pointed to is contained within the class the arrows originates from. Each class lists some representative class members (functions or variables) in small text. The Network class is at the core of the simulator. It contains the list of cells and synapses that define the network as well as data logging capabilities. The Cell class is a parent class to each individual cell type. It contains functions and variables common to all cells. Individual cells can be biophysical or non-biophysical if desired. The Network class is “wrapped” by three helper classes that allow the core of GenNet to be used in three different contexts. In one, GenNet is coupled to RTXI and physical hardware channel inputs are routed between model cells and real cells in an experimental preparation. In the other context, the simulator runs “stand-alone” in a purely virtual mode. In the last, GenNet is embedded within RTXI but used for real-time, single-cell simulations rather than hybrid network applications. All cells marked (model) are computer model versions of the cells they represent. The “real cell” is a placeholder class that serves to represent a living neuron within GenNet. Figure 2: Schematic diagram of a hippocampal hybrid network. A set of model cells simulated with GenNet within RTXI (shaded box on left) interacts with a real pyramidal neuron being recorded with the patch-clamp technique in a hippocampal slice (right). Dark circles represent individual model cells that are connected via virtual synapses (dashed lines) to a real pyramidal neuron. A patch-clamp pipette is used to record the voltage from this neuron in real time which is then passed as input to GenNet (upper arrow). After a computational time-step has elapsed, GenNet computes the synaptic current that must be passed to the pyramidal neuron and RTXI sends this current into the cell via the pipette (lower arrow). Multiple adjacent pyramidal neurons indicate that an arbitrary number of real cells can be embedded into the hybrid network. Abbreviations refer to the regions of the hippocampal formation: mEC, medial entorhinal cortex; DG, dentate gyrus; CA3, cornu Ammonis 3; CA1: cornu Ammonis 1. Figure 3: General Network enables rapid parameter switching for simulating diverse network types. (A) A sample feed-forward network is implemented in GenNet to illustrate the capability of the software to quickly and easily change fundamental properties. The sample network contains two cells with noisy drive coupled either by feed-forward excitation (left) or feed-forward inhibition (right). Cell 1 is made to fire tonically triggering post-synaptic currents in Cell 2. (B) When excitatory coupling is used, the spikes of Cell 2 (middle panel) closely track those of Cell 1 (top panel). The excitatory synaptic currents (bottom panel) are sufficient to elicit a spike in Cell 2 each time a Cell 1 spikes. The synaptic current also illustrates the voltage dependence of synaptic transmission. When a post-synaptic spike raises the voltage of Cell 2 past the reversal potential of the synapse, the sign of the synaptic current changes. Vertical gray dashed lines indicate the timing of spikes in Cell 1. Horizontal gray dashed line indicates −50 mV. (C) The same network can be run with the synapse switched to be inhibitory. In this case, post-synaptic spiking in Cell 2 is irregular and does not track pre-synaptic spiking (middle panel). The effect of pre-synaptic spikes (top panel) on the post-synaptic voltage can be observed as small, hyperpolarizing deflections in the voltage. Spiking in Cell 2 occurs when the natural evolution of the voltage overcomes the periodic inhibition from Cell 1. As a result, firing in Cell 2 is slower than when inputs were excitatory. (D) Spike time histograms (plotted as Cell 2 spike times relative to the phase of Cell 1) show the distribution of spikes in Cell 2 depending on the coupling type used. Excitation causes Cell 2 to become entrained to Cell 1 (top panel) and Cell 2 spike occur in a small time window only. Inhibition causes spiking in Cell 2 to be biased to occur in the second half of the Cell 1 period when inhibition has had sufficient time to wear off (bottom panel) but overall spikes are spread over a wider time window than when excitation was used. Figure 4: Switching parameters in a larger network produces diverse activity patterns. (A) An example of a larger network containing 20 neurons coupled randomly. In the diagram, filled circles represent cells and lines represent synapses. Synaptic direction is not indicated. The connection probability between each cell pair is 20%. (B) The 20-cell network is simulated with either excitatory or inhibitory synapses. While the total amount of spiking is similar in both simulations, the instantaneous rate differs considerably between the simulations. While the rate is nearly constant over time with inhibitory synapses, the rate rapidly changes in a burst-like pattern with excitatory synapses. (C) Rastergram of spiking in the network when inhibitory synapses are used. Spike rate remains approximately constant over time. (D) Rastergram of spiking when excitatory synapses are used. The network bursts periodically. As single-neurons fire, they recruit their post-synaptic partners until every member of the network is activated. After a brief period of activation, the cells become refractory together and the network becomes nearly silent until the cycle repeats. Figure 5: Ring network shows how different network connectivity can lead to different patterns of activity propagation. (A) A 20-cell ring network is constructed in GenNet with each neuron coupled via excitation to its two immediate neighbors. (B) Spontaneous activity propagates through this network along the edge of the ring. A single spike triggers a wave of spikes that travels around the ring in both directions. The window of time indicated by the gray box is magnified in the (C). (C) Magnified view of a single wave. Arrows indicate the bidirectional propagation of the activity wave from a single source. (D) A different activity pattern emerges when the connectivity is changed to include a few random connections along with connections to nearest neighbors. In this case, activity waves have the opportunity to short cut the path along the edge and can thus recruit the remaining cells in the network more quickly after the initial spiking event. (F) Magnified view [gray box in (E)] of a single activity wave shows how the propagating activity uses the long-distance connections to activate remote portions of the ring more rapidly. This causes the cells to be more simultaneously active as opposed to sequentially active. Figure 6: Spikes in a hub neuron strongly influence spike times in the remaining network. (A) A 20-cell network is constructed in GenNet with a single-neuron (the hub neuron) synapsing onto the remaining cells in the network with feed-forward inhibition. In such a network, a spike in the hub neuron has the capability to profoundly influence the activity in the remaining cells in the network. The hub neuron is drawn as the large circle with the remaining cells represented as smaller circles. Synapses are drawn as lines without indication of direction. (B) Each time the hub neuron spikes, the voltage of all post-synaptic cells is averaged (solid line) indicating that a single inhibitory pulse caused approximately a −4 mV hyperpolarization in the target cells (right axis). The rastergram indicates the spiking activity of each neuron in the network after a hub neuron spike has occurred and shows that the inhibitory input imposes a delay of approximately 20 ms before spiking can resume again in the target neurons. Figure 7: Degree of coupling of O–LM cells determines network frequency in hippocampal hybrid network. (A) Network diagram (top left) showing layout of the simulated network and the integration of a real cell. The single pyramidal cell and two basket cells are simulated in real-time within RTXI. This reduced hippocampal network is connected to a real, patch-clamped O–LM cell (shown with dark shading). Connectivity in the network is all-to-all with the pyramidal cell being the only source of excitation in the network. Closed triangles indicate excitation and open circles indicate inhibition. For synapses, thick, solid lines indicate strong connections, normal lines indicate intermediate connections and dotted lines indicate weak connections. In this experiment, the outgoing connections from the O–LM cell are strong. Voltage traces of the simulated network (right panel) show how the real and simulated neurons influence each other via post-synaptic currents and that the network fires in a sustained theta rhythm due to strong inhibition from the O–LM cell. A rasterplot of spiking activity (bottom left panel) shows a longer window of ongoing activity. The voltage traces correspond to the region of the rasterplot in the shaded box. (B) Weak connectivity (dotted lines, top left panel) from the real O–LM cell to the rest of the simulated network results in an ongoing gamma rhythm (spike raster and voltage traces). Excitation from the pyramidal cells and subsequent rapid feedback inhibition forms a gamma rhythm between the pyramidal cell and the basket cells. Weak, theta-frequency inputs from the real O–LM cell are insufficient to prevent gamma frequency firing. (C) Hybrid network experiments performed with GenNet can be used in combination with existing RTXI protocols. The same hybrid network is run with synapses of intermediate strength (top left panel). In addition, RTXI is used to inject conductance noise into the real O–LM cell. Noisy drive combined with intermediate synapses results in a network in which O–LM spike times are variable and the network has periods of faster spiking interspersed with periods of slower spiking (top right panel and spike raster). The noisy voltage deflections in the O–LM cell due to noise injection are visible in its voltage trace.
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References
  1. C. D. Acker; N. Kopell; J. A. White Synchronization of strongly coupled excitatory neurons: relating network behavior to biophysics J. Comput. Neurosci. 15, 71-90 (2003) .
  2. A. B. Ali; A. P. Bannister; A. M. Thomson IPSPs elicited in CA1 pyramidal cells by putative basket cells in slices of adult rat hippocampus Eur. J. Neurosci. 11, 1741-1753 (1999) .
    • . . . The model neurons and patch-clamped O–LM neuron were connected in an all-to-all fashion with appropriate synaptic kinetics at each synapse (Wang and Buzsáki, 1996; Hajos and Mody, 1997; Ali et al., 1999; Goldin et al., 2007) . . .
  3. J. C. Bettencourt; K. P. Lillis; L. R. Stupin; J. A. White Effects of imperfect dynamic clamp: computational and experimental results J. Neurosci. Methods 169, 282-289 (2008) .
    • . . . Similar tools exist for dynamic clamp systems that do not operate in hard real-time and are specialized to work with particular neuron types (Hughes et al., 2008) . . .
    • . . . Although similar tool kits have been described (Hughes et al., 2008), to our knowledge, no other system is capable of creating hybrid networks with arbitrary cell types, topologies, and network size . . .
    • . . . Our dynamic clamp system (Dorval et al., 2001; Bettencourt et al., 2008; Lin et al., 2010) is based on a Linux kernel extension, real-time application interface, which is freely available6 . . .
  4. P. Bonifazi; M. Goldin; M. A. Picardo; I. Jorquera; A. Cattani; G. Bianconi; A. Represa; Y. Ben-Ari; R. Cossart GABAergic hub neurons orchestrate synchrony in developing hippocampal networks Science 326, 1419-1424 (2009) .
    • . . . This simulation provides another example of a biologically relevant topology (Bonifazi et al., 2009) and would have been precluded by the limitations of other hybrid network systems . . .
    • . . . First, if the goal is to study how the biophysical properties of recorded neurons affect network behavior, one should take care to verify that the recorded biological neuron or neurons can in principle have measurable effects on network activity, either because the total number of network elements is small, or because the biological elements have disproportionate influence on the rest of the network (Bonifazi et al., 2009) . . .
  5. J. M. Bower; D. Beeman The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System , (1998) .
    • . . . Although many neural simulators have been described in the literature that efficiently simulate the behavior of neural networks [e.g., BRIAN (Goodman, 2008)] and the voltage of spatially-extended neurons [NEURON, GENESIS (Hines and Carnevale, 1997; Bower and Beeman, 1998)], GenNet was primarily developed to facilitate integration with dynamic clamp software for the construction of hybrid networks . . .
    • . . . The ability to simulate spatially extended neural models in a stand-alone fashion is already provided by simulation packages such as NEURON and GENESIS (Hines and Carnevale, 1997; Bower and Beeman, 1998) and other efforts have endeavored to make spatially extended models compatible with dynamic clamp experiments (Hughes et al., 2008; Cornelis and Coop, 2010) . . .
  6. R. Brette; W. Gerstner Adaptive exponential integrate-and-fire model as an effective description of neuronal activity J. Neurophysiol. 94, 3637-3642 (2005) .
  7. H. Cornelis; A. Coop Realtime tuning and verification of compartmental cell models using RTXI and GENESIS BMC Neurosci. 11, P68 (2010) .
    • . . . The ability to simulate spatially extended neural models in a stand-alone fashion is already provided by simulation packages such as NEURON and GENESIS (Hines and Carnevale, 1997; Bower and Beeman, 1998) and other efforts have endeavored to make spatially extended models compatible with dynamic clamp experiments (Hughes et al., 2008; Cornelis and Coop, 2010) . . .
  8. P. Dayan; L. F. Abbott Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, Computational Neuroscience , (2001) .
  9. D. Debay; J. Wolfart; Y. Le Franc; G. Le Masson; T. Bal Exploring spike transfer through the thalamus using hybrid artificial-biological neuronal networks J. Physiol. Paris 98, 540-558 (2004) .
    • . . . This experimental paradigm represents one of the major uses of dynamic clamp (Prinz et al., 2004; White et al., 2009; Economo et al., 2010) and has been applied successfully by several groups to study the phasing and synchronization of groups of cells (Sharp et al., 1993; Ulrich and Huguenard, 1996; Debay et . . .
  10. A. Destexhe; D. Pare Impact of network activity on the integrative properties of neocortical pyramidal neurons in vivo J. Neurophysiol. 81, 1531-1547 (1999) .
    • . . . For example, the Cell class includes a description of an Ornstein–Uhlenbeck conductance process (Uhlenbeck and Ornstein, 1930), commonly used to approximate the post-synaptic effect of many irregularly firing pre-synaptic neurons (Softky and Koch, 1993; Destexhe and Pare, 1999; Destexhe et al., 2001, 2003) . . .
  11. A. Destexhe; M. Rudolph; J. M. Fellous; T. J. Sejnowski Fluctuating synaptic conductances recreate in vivo-like activity in neocortical neurons Neuroscience 107, 13-24 (2001) .
    • . . . For example, the Cell class includes a description of an Ornstein–Uhlenbeck conductance process (Uhlenbeck and Ornstein, 1930), commonly used to approximate the post-synaptic effect of many irregularly firing pre-synaptic neurons (Softky and Koch, 1993; Destexhe and Pare, 1999; Destexhe et al., 2001, 2003) . . .
  12. A. Destexhe; M. Rudolph; D. Pare The high-conductance state of neocortical neurons in vivo Nat. Rev. Neurosci. 4, 739-751 (2003) .
    • . . . For example, the Cell class includes a description of an Ornstein–Uhlenbeck conductance process (Uhlenbeck and Ornstein, 1930), commonly used to approximate the post-synaptic effect of many irregularly firing pre-synaptic neurons (Softky and Koch, 1993; Destexhe and Pare, 1999; Destexhe et al., 2001, 2003) . . .
    • . . . A substantial body of literature suggests that neurons may behave differently when subjected to the same input commonly received in an in vivo network, where cells receive large numbers of incoherent synaptic inputs not present in slice preparations (Destexhe et al., 2003) . . .
  13. A. D. Dorval; D. J. Christini; J. A. White Real-time linux dynamic clamp: a fast and flexible way to construct virtual ion channels in living cells Ann. Biomed. Eng. 29, 897-907 (2001) .
    • . . . Our dynamic clamp system (Dorval et al., 2001; Bettencourt et al., 2008; Lin et al., 2010) is based on a Linux kernel extension, real-time application interface, which is freely available6 . . .
  14. M. N. Economo; F. R. Fernandez; J. A. White Dynamic clamp: alteration of response properties and creation of virtual realities in neurophysiology J. Neurosci. 30, 2407-2413 (2010) .
    • . . . This experimental paradigm represents one of the major uses of dynamic clamp (Prinz et al., 2004; White et al., 2009; Economo et al., 2010) and has been applied successfully by several groups to study the phasing and synchronization of groups of cells (Sharp et al., 1993; Ulrich and Huguenard, 1996; Debay et al., 2004; Netoff et al., 2005) . . .
  15. F. R. Fernandez; T. Broicher; A. Truong; J. A. White Membrane voltage fluctuations reduce spike frequency adaptation and preserve output gain in CA1 pyramidal neurons in a high-conductance state J. Neurosci. 31, 3880-3893 (2011) .
    • . . . If feedback from the biological neurons is unimportant, an alternative approach would be study the behavior of the recorded neuron(s) in response to predefined inputs (Fernandez and White, 2008, 2009, 2010; Fernandez et al., 2011) instead of using the hybrid network technique . . .
  16. F. R. Fernandez; J. A. White Artificial synaptic conductances reduce subthreshold oscillations and periodic firing in stellate cells of the entorhinal cortex J. Neurosci. 28, 3790-3803 (2008) .
    • . . . If feedback from the biological neurons is unimportant, an alternative approach would be study the behavior of the recorded neuron(s) in response to predefined inputs (Fernandez and White, 2008, 2009, 2010; Fernandez et al., 2011) instead of using the hybrid network technique . . .
    • . . . All experimental data was collected with standard patch-clamp methods described in detail elsewhere (Fernandez and White, 2008) . . .
  17. F. R. Fernandez; J. A. White Reduction of spike afterdepolarization by increased leak conductance alters interspike interval variability J. Neurosci. 29, 973-986 (2009) .
    • . . . First, if the goal is to study how the biophysical properties of recorded neurons affect network behavior, one should take care to verify that the recorded biological neuron or neurons can in principle have measurable effects on network activity, either because the total number of network elements is small, or because the biological elements have disproportionate influence on the rest of the network (Bonifazi et al., 2009) . . .
  18. F. R. Fernandez; J. A. White Gain control in CA1 pyramidal cells using changes in somatic conductance J. Neurosci. 30, 230-241 (2010) .
    • . . . If feedback from the biological neurons is unimportant, an alternative approach would be study the behavior of the recorded neuron(s) in response to predefined inputs (Fernandez and White, 2008, 2009, 2010; Fernandez et al., 2011) instead of using the hybrid network technique . . .
  19. M. J. Gillies; R. D. Traub; F. E. N. LeBeau; C. H. Davies; T. Gloveli; E. H. Buhl; M. A. Whittington A model of atropine-resistant theta oscillations in rat hippocampal area CA1 J. Physiol. 543, 779-793 (2002) .
    • . . . The following hybrid network experiment was motivated by previous studies (Gillies et al., 2002; Gloveli et al., 2005; Tort et al., 2007) suggesting that the theta (4–12 Hz) and gamma (30–80 Hz) rhythms may be generated by the interaction of hippocampal pyramidal neurons in region CA1 with neighboring basket and oriens–lacunosum moleculare (O–LM) interneurons . . .
  20. T. Gloveli; T. Dugladze; H. G. Rotstein; R. D. Traub; H. Monyer; U. Heinemann; M. A. Whittington; N. J. Kopell Orthogonal arrangement of rhythm-generating microcircuits in the hippocampus Proc. Natl. Acad. Sci. U.S.A. 102, 13295-13300 (2005) .
    • . . . The following hybrid network experiment was motivated by previous studies (Gillies et al., 2002; Gloveli et al., 2005; Tort et al., 2007) suggesting that the theta (4–12 Hz) and gamma (30–80 Hz) rhythms may be generated by the interaction of hippocampal pyramidal neurons in region CA1 with neighboring basket and oriens–lacunosum moleculare (O–LM) interneurons . . .
    • . . . In one study, Gloveli et al. (2005) used kainate to induce rhythmic activity in hippocampal brain slices cut either in the transverse or coronal planes . . .
    • . . . We tested this by patch clamping onto an O–LM neuron and coupling it, using GenNet, to a simulated network containing one pyramidal neuron and two basket interneurons (Figure 7A) meant to mimic the local hippocampal microcircuit (Gloveli et al., 2005; Rotstein et . . .
  21. M. Goldin; J. Epsztein; I. Jorquera; A. Represa; Y. Ben-Ari; V. Crépel; R. Cossart Synaptic kainate receptors tune oriens-lacunosum moleculare interneurons to operate at theta frequency J. Neurosci. 27, 9560-9572 (2007) .
    • . . . The following hybrid network experiment was motivated by previous studies (Gillies et al., 2002; Gloveli et al., 2005; Tort et al., 2007) suggesting that the theta (4–12 Hz) and gamma (30–80 Hz) rhythms may be generated by the interaction of hippocampal pyramidal neurons in region CA1 with neighboring basket and oriens–lacunosum moleculare (O–LM) interneurons . . .
    • . . . We tested this by patch clamping onto an O–LM neuron and coupling it, using GenNet, to a simulated network containing one pyramidal neuron and two basket interneurons (Figure 7A) meant to mimic the local hippocampal microcircuit (Gloveli et al., 2005; Rotstein et al., 2005; Tort et al., 2007) . . .
    • . . . Kinetics for these synaptic waveforms were taken from the literature (Maccaferri et al., 2000; Netoff et al., 2005; Goldin et al., 2007). . . .
  22. D. Golomb; K. Donner; L. Shacham; D. Shlosberg; Y. Amitai; D. Hansel Mechanisms of firing patterns in fast-spiking cortical interneurons PLoS Comput. Biol. 3, e156 (2007) .
  23. D. Goodman Brian: a simulator for spiking neural networks in Python Front. Neuroinform. 2, 5 (2008) .
    • . . . Although many neural simulators have been described in the literature that efficiently simulate the behavior of neural networks [e.g., BRIAN (Goodman, 2008)] and the voltage of spatially-extended neurons [NEURON, GENESIS (Hines and Carnevale, 1997; Bower and Beeman, 1998)], GenNet was primarily developed to facilitate integration with dynamic clamp software for the construction of hybrid networks . . .
  24. R. Grashow; T. Brookings; E. Marder Compensation for variable intrinsic neuronal excitability by circuit-synaptic interactions J. Neurosci. 30, 9145-9156 (2010) .
    • . . . In previous studies, implementations of hybrid networks often followed ad hoc approaches, in which specific networks containing the cell types and topology of interest were created in a static fashion (Sorensen et al., 2004; Netoff et al., 2005; Olypher et al., 2006; Grashow et al., 2010) . . .
    • . . . It has the additional advantage of operating within a dynamic clamp system that is widely used in many laboratories (Iravanian and Christini, 2007; Bettencourt et al., 2008; Grashow et al., 2010; Lin et . . .
  25. K. Graubard; J. A. Raper; D. K. Hartline Graded synaptic transmission between spiking neurons Proc. Natl. Acad. Sci. U.S.A. 77, 3733-3735 (1980) .
    • . . . This extensibility may be important in the future for individuals desiring to study model systems which employ graded synaptic transmission, such as the crab stomatogastric ganglion (Graubard et al., 1980; Manor et al., 1997). . . .
  26. N. Hajos; I. Mody Synaptic communication among hippocampal interneurons: properties of spontaneous IPSCs in morphologically identified cells J. Neurosci. 17, 8427-8442 (1997) .
    • . . . The model neurons and patch-clamped O–LM neuron were connected in an all-to-all fashion with appropriate synaptic kinetics at each synapse (Wang and Buzsáki, 1996; Hajos and Mody, 1997; Ali et al., 1999; Goldin et al., 2007) . . .
  27. M. L. Hines; N. T. Carnevale The NEURON simulation environment Neural. Comput. 9, 1179-1209 (1997) .
    • . . . Although many neural simulators have been described in the literature that efficiently simulate the behavior of neural networks [e.g., BRIAN (Goodman, 2008)] and the voltage of spatially-extended neurons [NEURON, GENESIS (Hines and Carnevale, 1997; Bower and Beeman, 1998)], GenNet was primarily developed to facilitate integration with dynamic clamp software for the construction of hybrid networks . . .
    • . . . The ability to simulate spatially extended neural models in a stand-alone fashion is already provided by simulation packages such as NEURON and GENESIS (Hines and Carnevale, 1997; Bower and Beeman, 1998) and other efforts have endeavored to make spatially extended models compatible with dynamic clamp experiments (Hughes et al., 2008; Cornelis and Coop, 2010) . . .
  28. S. W. Hughes; M. Lorincz; D. W. Cope; V. Crunelli NeuReal: an interactive simulation system for implementing artificial dendrites and large hybrid networks J. Neurosci. Methods 169, 290-301 (2008) .
    • . . . Later, more sophisticated approaches have incorporated detailed biophysical representations of model cells (Hughes et al., 2008) . . .
    • . . . Similar tools exist for dynamic clamp systems that do not operate in hard real-time and are specialized to work with particular neuron types (Hughes et al., 2008) . . .
    • . . . Although similar tool kits have been described (Hughes et al., 2008), to our knowledge, no other system is capable of creating hybrid networks with arbitrary cell types, topologies, and network size . . .
    • . . . The ability to simulate spatially extended neural models in a stand-alone fashion is already provided by simulation packages such as NEURON and GENESIS (Hines and Carnevale, 1997; Bower and Beeman, 1998) and other efforts have endeavored to make spatially extended models compatible with dynamic clamp experiments (Hughes et al., 2008; Cornelis and Coop, 2010) . . .
  29. S. Iravanian; D. J. Christini Optical mapping system with real-time control capability Am. J. Physiol. Heart Circ. Physiol. 293, H2605-H2611 (2007) .
    • . . . It has the additional advantage of operating within a dynamic clamp system that is widely used in many laboratories (Iravanian and Christini, 2007; Bettencourt et al., 2008; Grashow et al., 2010; Lin et al., 2010; Lobb and Paladini, 2010) . . .
  30. E. M. Izhikevich Which model to use for cortical spiking neurons? IEEE Trans. Neural Netw. 15, 1063-1070 (2004) .
    • . . . To qualitatively illustrate the performance of GenNet running on a single core Pentium 4 CPU running with a 3.6-GHz clock rate (modest hardware by modern standards), a network of 100 Izhikevich neurons (each containing two differential equations; Izhikevich, 2004) connected by 500 randomly assigned synapses could be simulated in real-time when being integrated at 10 kHz . . .
  31. G. Le Masson; S. Le Masson; M. Moulins From conductances to neural network properties: analysis of simple circuits using the hybrid network method Prog. Biophys. Mol. Biol. 64, 201-220 (1995) .
    • . . . Initial hybrid network studies were used to inject timed, conductance-based synaptic inputs into cells in vitro (Ulrich and Huguenard, 1996) and to analyze invertebrate circuits (Le Masson et al., 1995) . . .
  32. R. J. Lin; J. Bettencourt; J. Wha Ite; D. J. Christini; R. J. Butera Real-time experiment interface for biological control applications Conf. Proc. IEEE Eng. Med. Biol. Soc. 1, 4160-4163 (2010) .
    • . . . It has the additional advantage of operating within a dynamic clamp system that is widely used in many laboratories (Iravanian and Christini, 2007; Bettencourt et al., 2008; Grashow et al., 2010; Lin et . . .
    • . . . Our dynamic clamp system (Dorval et al., 2001; Bettencourt et al., 2008; Lin et al., 2010) is based on a Linux kernel extension, real-time application interface, which is freely available6 . . .
  33. C. J. Lobb; C. A. Paladini Application of a NMDA receptor conductance in rat midbrain dopaminergic neurons using the dynamic clamp technique J. Vis. Exp. , (2010) .
    • . . . It has the additional advantage of operating within a dynamic clamp system that is widely used in many laboratories (Iravanian and Christini, 2007; Bettencourt et al., 2008; Grashow et al., 2010; Lin et al., 2010; Lobb and Paladini, 2010) . . .
  34. G. Maccaferri; J. D. Roberts; P. Szucs; C. A. Cottingham; P. Somogyi Cell surface domain specific postsynaptic currents evoked by identified GABAergic neurones in rat hippocampus in vitro J. Physiol. 524, Pt 1-91-116 (2000) .
    • . . . Kinetics for these synaptic waveforms were taken from the literature (Maccaferri et al., 2000; Netoff et al., 2005; Goldin et al., 2007). . . .
  35. Z. F. Mainen; T. J. Sejnowski Influence of dendritic structure on firing pattern in model neocortical neurons Nature 382, 363-366 (1996) .
    • . . . To test the performance of a network with a more biophysically realistic neuron model we replaced the Izhikevitch model with a seven-equation model of a pyramidal neuron (Mainen and Sejnowski, 1996) . . .
  36. Y. Manor; F. Nadim; L. F. Abbott; E. Marder Temporal dynamics of graded synaptic transmission in the lobster stomatogastric ganglion J. Neurosci. 17, 5610-5621 (1997) .
    • . . . This extensibility may be important in the future for individuals desiring to study model systems which employ graded synaptic transmission, such as the crab stomatogastric ganglion (Graubard et al., 1980; Manor et al., 1997). . . .
  37. T. I. Netoff; M. I. Banks; A. D. Dorval; C. D. Acker; J. S. Haas; N. Kopell; J. A. White Synchronization in hybrid neuronal networks of the hippocampal formation J. Neurophysiol. 93, 1197-1208 (2005) .
    • . . . This experimental paradigm represents one of the major uses of dynamic clamp (Prinz et al., 2004; White et al., 2009; Economo et al., 2010) and has been applied successfully by several groups to study the phasing and synchronization of groups of cells (Sharp et al., 1993; Ulrich and Huguenard, 1996; Debay et al., 2004; Netoff et al., 2005) . . .
    • . . . In previous studies, implementations of hybrid networks often followed ad hoc approaches, in which specific networks containing the cell types and topology of interest were created in a static fashion (Sorensen et al., 2004; Netoff et al., 2005; Olypher et al., 2006; Grashow et al., 2010) . . .
    • . . . For this reason, in expanding hybrid networks beyond simple cases (Netoff et al., 2005), one must impose constraints upon network organization that keep the parameter space manageable for realistic recording epochs. . . .
    • . . . Kinetics for these synaptic waveforms were taken from the literature (Maccaferri et al., 2000; Netoff et al., 2005; Goldin et al., 2007). . . .
  38. A. Olypher; G. Cymbalyuk; R. L. Calabrese Hybrid systems analysis of the control of burst duration by low-voltage-activated calcium current in leech heart interneurons J. Neurophysiol. 96, 2857-2867 (2006) .
    • . . . In previous studies, implementations of hybrid networks often followed ad hoc approaches, in which specific networks containing the cell types and topology of interest were created in a static fashion (Sorensen et al., 2004; Netoff et al., 2005; Olypher et al., 2006; Grashow et al., 2010) . . .
  39. F. G. Pike; R. S. Goddard; J. M. Suckling; P. Ganter; N. Kasthuri; O. Paulsen Distinct frequency preferences of different types of rat hippocampal neurones in response to oscillatory input currents J. Physiol. 529, 205-213 (2000) .
    • . . . Other studies (Pike et al., 2000; Gillies et al., 2002; Goldin et al., 2007) have postulated that O–LM neurons are well-suited for the generation of theta rhythms due to the presence of HCN channels in these cells, the relatively slow kinetics of synapses formed between these cells and neighboring principal neurons, and their ability to integrate inputs at theta frequencies preferentially . . .
  40. A. A. Prinz; L. F. Abbott; E. Marder The dynamic clamp comes of age Trends Neurosci. 27, 218-224 (2004) .
    • . . . This experimental paradigm represents one of the major uses of dynamic clamp (Prinz et al., 2004; White et al., 2009; Economo et al., 2010) and has been applied successfully by several groups to study the phasing and synchronization of groups of cells (Sharp et al., 1993; Ulrich and Huguenard, 1996; Debay et . . .
  41. H. G. Rotstein; D. D. Pervouchine; C. D. Acker; M. J. Gillies; J. A. White; E. H. Buhl; M. A. Whittington; N. Kopell Slow and fast inhibition and an H-current interact to create a theta rhythm in a model of CA1 interneuron network J. Neurophysiol. 94, 1509-1518 (2005) .
    • . . . The following hybrid network experiment was motivated by previous studies (Gillies et al., 2002; Gloveli et al., 2005; Tort et al., 2007) suggesting that the theta (4–12 Hz) and gamma (30–80 Hz) rhythms may be generated by the interaction of hippocampal pyramidal neurons in region CA1 with neighboring basket and oriens–lacunosum moleculare (O–LM) interneurons . . .
    • . . . We tested this by patch clamping onto an O–LM neuron and coupling it, using GenNet, to a simulated network containing one pyramidal neuron and two basket interneurons (Figure 7A) meant to mimic the local hippocampal microcircuit (Gloveli et al., 2005; Rotstein et . . .
  42. F. Saraga; C. P. Wu; L. Zhang; F. K. Skinner Active dendrites and spike propagation in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons J. Physiol. 552, 673-689 (2003) .
  43. A. A. Sharp; M. B. O'Neil; L. F. Abbott; E. Marder The dynamic clamp: artificial conductances in biological neurons Trends Neurosci. 16, 389-394 (1993) .
    • . . . This experimental paradigm represents one of the major uses of dynamic clamp (Prinz et al., 2004; White et al., 2009; Economo et al., 2010) and has been applied successfully by several groups to study the phasing and synchronization of groups of cells (Sharp et al., 1993; Ulrich and Huguenard, 1996; Debay et al., 2004; Netoff et al., 2005) . . .
  44. W. Softky; C. Koch The highly irregular firing of cortical cells is inconsistent with temporal integration of random EPSPs J. Neurosci. 13, 334-350 (1993) .
    • . . . For example, the Cell class includes a description of an Ornstein–Uhlenbeck conductance process (Uhlenbeck and Ornstein, 1930), commonly used to approximate the post-synaptic effect of many irregularly firing pre-synaptic neurons (Softky and Koch, 1993; Destexhe and Pare, 1999; Destexhe et al., 2001, 2003) . . .
  45. M. Sorensen; S. DeWeerth; G. Cymbalyuk; R. L. Calabrese Using a hybrid neural system to reveal regulation of neuronal network activity by an intrinsic current J. Neurosci. 24, 5427-5438 (2004) .
    • . . . In previous studies, implementations of hybrid networks often followed ad hoc approaches, in which specific networks containing the cell types and topology of interest were created in a static fashion (Sorensen et al., 2004; Netoff et al., 2005; Olypher et al., 2006; Grashow et al., 2010) . . .
  46. A. B. L. Tort; H. G. Rotstein; T. Dugladze; T. Gloveli; N. J. Kopell On the formation of gamma-coherent cell assemblies by oriens lacunosum-moleculare interneurons in the hippocampus Proc. Natl. Acad. Sci. U.S.A. 104, 13490-13495 (2007) .
    • . . . The following hybrid network experiment was motivated by previous studies (Gillies et al., 2002; Gloveli et al., 2005; Tort et al., 2007) suggesting that the theta (4–12 Hz) and gamma (30–80 Hz) rhythms may be generated by the interaction of hippocampal pyramidal neurons in region CA1 with neighboring basket and oriens–lacunosum moleculare (O–LM) interneurons . . .
    • . . . We tested this by patch clamping onto an O–LM neuron and coupling it, using GenNet, to a simulated network containing one pyramidal neuron and two basket interneurons (Figure 7A) meant to mimic the local hippocampal microcircuit (Gloveli et al., 2005; Rotstein et al., 2005; Tort et al., 2007) . . .
  47. G. E. Uhlenbeck; L. S. Ornstein On the theory of the Brownian motion Phys. Rev. 36, 823 (1930) .
    • . . . For example, the Cell class includes a description of an Ornstein–Uhlenbeck conductance process (Uhlenbeck and Ornstein, 1930), commonly used to approximate the post-synaptic effect of many irregularly firing pre-synaptic neurons (Softky and Koch, 1993; Destexhe and Pare, 1999; Destexhe et al., 2001, 2003) . . .
  48. D. Ulrich; J. R. Huguenard Gamma-aminobutyric acid type B receptor-dependent burst-firing in thalamic neurons: a dynamic clamp study Proc. Natl. Acad. Sci. U.S.A. 93, 13245-13249 (1996) .
    • . . . This experimental paradigm represents one of the major uses of dynamic clamp (Prinz et al., 2004; White et al., 2009; Economo et al., 2010) and has been applied successfully by several groups to study the phasing and synchronization of groups of cells (Sharp et al., 1993; Ulrich and Huguenard, 1996; Debay et al., 2004; Netoff et al., 2005) . . .
  49. X. J. Wang; G. Buzsáki Gamma oscillation by synaptic inhibition in a hippocampal interneuronal network model J. Neurosci. 16, 6402-6413 (1996) .
    • . . . The model neurons and patch-clamped O–LM neuron were connected in an all-to-all fashion with appropriate synaptic kinetics at each synapse (Wang and Buzsáki, 1996; Hajos and Mody, 1997; Ali et al., 1999; Goldin et al., 2007) . . .
  50. J. A. White; F. R. Fernandez; M. N. Economo; T. J. Kispersky “Using ‘hard’ real-time dynamic clamp to study cellular and network mechanisms of synchronization in the hippocampal formation,” Dynamic-Clamp , 199-215 (2009) .
    • . . . This experimental paradigm represents one of the major uses of dynamic clamp (Prinz et al., 2004; White et al., 2009; Economo et al., 2010) and has been applied successfully by several groups to study the phasing and synchronization of groups of cells (Sharp et al., 1993; Ulrich and Huguenard, 1996; Debay et al., 2004; Netoff et al., 2005) . . .
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