Cengiz Günay

Channel Density Distributions Explain Spiking Variability in the Globus Pallidus: A Combined Physiology and Computer Simulation Database Approach

Globus pallidus (GP) neurons recorded in brain slices show significant variability in intrinsic electrophysiological properties. To investigate how this variability arises, we manipulated the biophysical properties of GP neurons using computer simulations. Specifically, we created a GP neuron model database with 100,602 models that had varying densities of nine membrane conductances centered on a hand-tuned model that replicated typical physiological data. To test the hypothesis that the experimentally observed variability can be attributed to variations in conductance densities, we compared our model database results to a physiology database of 146 slice recordings. The electrophysiological properties of generated models and recordings were assessed with identical current injection protocols and analyzed with a uniform set of measures, allowing a systematic analysis of the effects of varying voltage-gated and calcium-gated conductance densities on the measured properties and a detailed comparison between models and recordings. Our results indicated that most of the experimental variability could be matched by varying conductance densities, which we confirmed with additional partial block experiments. Further analysis resulted in two key observations: (1) each voltage-gated conductance had effects on multiple measures such as action potential waveform and spontaneous or stimulated spike rates; and (2) the effect of each conductance was highly dependent on the background context of other conductances present. In some cases, such interactions could reverse the effect of the density of one conductance on important excitability measures. This context dependence of conductance density effects is important to understand drug and neuromodulator effects that work by affecting ion channels.

Activity-dependent alternative splicing increases persistent sodium current and promotes seizure

Activity of voltage-gated Na channels (Nav) is modified by alternative splicing. However, whether altered splicing of human Navs contributes to epilepsy remains to be conclusively shown. We show here that altered splicing of the Drosophila Nav (paralytic, DmNav) contributes to seizure-like behavior in identified seizure mutants. We focus attention on a pair of mutually exclusive alternate exons (termed K and L), which form part of the voltage sensor (S4) in domain III of the expressed channel. The presence of exon L results in a large, non-inactivating, persistent INap. Many forms of human epilepsy are associated with an increase in this current. In wild-type (WT) Drosophila larvae, ∼70–80% of DmNav transcripts contain exon L, and the remainder contain exon K. Splicing of DmNav to include exon L is increased to ∼100% in both the slamdance and easily-shocked seizure mutants. This change to splicing is prevented by reducing synaptic activity levels through exposure to the antiepileptic phenytoin or the inhibitory transmitter GABA. Conversely, enhancing synaptic activity in WT, by feeding of picrotoxin is sufficient to increase INap and promote seizure through increased inclusion of exon L to 100%. We also show that the underlying activity-dependent mechanism requires the presence of Pasilla, an RNA-binding protein. Finally, we use computational modeling to show that increasing INap is sufficient to potentiate membrane excitability consistent with a seizure phenotype. Thus, increased synaptic excitation favors inclusion of exon L, which, in turn, further increases neuronal excitability. Thus, at least in Drosophila, this self-reinforcing cycle may promote the incidence of seizure.

Model Calcium Sensors for Network Homeostasis: Sensor and Readout Parameter Analysis from a Database of Model Neuronal Networks

In activity-dependent homeostatic regulation (ADHR) of neuronal and network properties, the intracellular Ca2+ concentration is a good candidate for sensing activity levels because it is correlated with the electrical activity of the cell. Previous ADHR models, developed with abstract activity sensors for model pyloric neurons and networks of the crustacean stomatogastric ganglion, showed that functional activity can be maintained by a regulation mechanism that senses activity levels solely from Ca2+. At the same time, several intracellular pathways have been discovered for Ca2+-dependent regulation of ion channels. To generate testable predictions for dynamics of these signaling pathways, we undertook a parameter study of model Ca2+ sensors across thousands of model pyloric networks. We found that an optimal regulation signal can be generated for 86% of model networks with a sensing mechanism that activates with a time constant of 1 ms and that inactivates within 1 s. The sensor performed robustly around this optimal point and did not need to be specific to the role of the cell. When multiple sensors with different time constants were used, coverage extended to 88% of the networks. Without changing the sensors, it extended to 95% of the networks by letting the sensors affect the readout nonlinearly. Specific to this pyloric network model, the sensor of the follower pyloric constrictor cell was more informative than the pacemaker anterior burster cell for producing a regulatory signal. Conversely, a global signal indicating network activity that was generated by summing the sensors in individual cells was less informative for regulation.

Dendritic sodium channels regulate network integration in globus pallidus neurons: A modeling study

The globus pallidus (GP) predominantly contains GABAergic projection neurons that occupy a central position in the indirect pathway of the basal ganglia. They have long dendrites that can extend through one-half the diameter of the GP in rats, potentially enabling convergence and interaction between segregated basal ganglia circuits. Because of the length and fine diameter of GP dendrites, however, it is unclear how much influence distal synapses have on spiking activity. Dendritic expression of fast voltage-dependent Na+ channels (NaF channels) can enhance the importance of distal excitatory synapses by allowing for dendritic spike initiation and by subthreshold boosting of EPSPs. Antibody labeling has demonstrated the presence of NaF channel proteins in GP dendrites, but the quantitative expression density of the channels remains unknown. We built a series of nine GP neuron models that differed only in their dendritic NaF channel expression level to assess the functional impact of this parameter. The models were all similar in their basic electrophysiological features; however, higher expression levels of dendritic NaF channels increased the relative effectiveness of distal inputs for both excitatory and inhibitory synapses, broadening the effective extent of the dendritic tree. Higher dendritic NaF channel expression also made the neurons more resistant to tonic inhibition and highly sensitive to clustered synchronous excitation. The dendritic NaF channel expression pattern may therefore be a critical determinant of convergence for both the striatopallidal and subthalamopallidal projections, while also dictating which spatiotemporal input patterns are most effective at driving GP neuron output.

Using temporal binding for hierarchical recruitment of conjunctive concepts over delayed lines

The temporal correlation hypothesis proposes using distributed synchrony for the binding of different stimulus features. However, synchronized spikes must travel over cortical circuits that have varying-length pathways, leading to mismatched arrival times. This raises the question of how initial stimulus-dependent synchrony might be preserved at a destination binding site. Earlier, we proposed constraints on tolerance and segregation parameters for a phase-coding approach, within cortical circuits, to address this question [C. Gunay, A.S. Maida, Temporal binding as an inducer for connectionist recruitment learning over delayed lines, Neural Networks 16 (5-6) (2003) 593-600]. The purpose of the present paper is twofold. First, we conduct simulation studies that explore the effectiveness of the proposed constraints. Second, we place the studies in a broader context of synchrony-driven recruitment learning [L. Shastri, V. Ajjanagadde, From simple associations to systematic reasoning: a connectionist representation of rules, variables, and dynamic bindings using temporal synchrony, Behav. Brain Sci. 16 (3) (1993) 417-451; L.G. Valiant, Circuits of the Mind, Oxford University Press, Oxford, 1994] which brings together von der Malsburgs temporal binding [C. von der Malsburg, The correlation theory of brain function, in: E. Domany, J.L. van Hemmen, K. Schulten (Ed.), Models of Neural Networks, vol. 2, Physics of Neural Networks, Chapter 2, Springer, New York, 1994, pp. 95-120, (Originally appeared as a Technical Report at the Max-Planck Institute for Biophysical Chemistry, Gottingen, 1981)] and Feldmans recruitment learning [J.A. Feldman, Dynamic connections in neural networks, Biol. Cybern. 46 (1982) 27-39]. A network based on Valiants neuroidal architecture is used to implement synchrony-driven recruitment learning. Complementing similar approaches, we use a continuous-time learning procedure allowing computation with spiking neurons. The viability of the proposed binding scheme is investigated by conducting simulation studies which examine binding errors. In the simulation, binding errors cause the formation of illusory conjunctions among features belonging to separate objects. Our results indicate that when tolerance and segregation parameters obey our proposed constraints, the sets of correct bindings are dominant over sets of spurious bindings in reasonable operating conditions. We also improve the stability of the recruitment method in deep hierarchies for use in limited size structures suitable for computer simulations. e also improve the stability of the recruitment method in deep hierarchies for use in limited size structures suitable for computer simulations.

Temporal binding as an inducer for connectionist recruitment learning over delayed lines

The temporal correlation hypothesis proposes using distributed synchrony for the binding of different stimulus features. However, synchronized spikes must travel over cortical circuits that have varying length pathways, leading to mismatched arrival times. This raises the question of how initial stimulus-dependent synchrony might be preserved at a destination binding site. Earlier, we proposed constraints on tolerance and segregation parameters for a phase-coding approach, within cortical circuits, to address this question [Proceedings of the International Joint Conference on Neural Networks, Washington, DC, 2001]. The purpose of the present paper is twofold. First, we conduct simulation experiments to test the proposed constraints. Second, we explore the practicality of temporal binding to drive a process of long-term memory formation based on a recruitment learning method [Biol. Cybernet. 46 (1982) 27]. A network based on Valiants neuroidal architecture [Circuits of the mind, 1994] is used to demonstrate the coalition between temporal binding and recruitment. Complementing similar approaches, we implement a continuous-time learning procedure allowing computation with spiking neurons. The viability of the proposed binding scheme is investigated by conducting simulation studies which examine binding errors. In the simulation, binding errors cause the perception of illusory conjunctions among features belonging to separate objects. Our results indicate that when tolerance and segregation parameters obey our proposed constraints, the assemblies of correct bindings are dominant over assemblies of spurious bindings in reasonable operating conditions.

The required measures of phase segregation in distributed cortical processing

Many neuroscience studies suggest that synchronous and oscillatory activity plays an important role in coordinating computations among cortical areas. Elsewhere, the temporal correlation hypothesis has been put forth which relies on synchrony. Activity in distributed cortical areas represents features of an object if it appears synchronously and features of different objects are distinguished if they are desynchronized with others, overcoming the binding problem. The segregation of neural activity into distinct phase windows helps preserve coherence as it propagates to deeper layers of the cortex. Here, the timing is crucial if synchronized spike volleys must meet after crossing different cortical paths, which impose different transmission delays. We propose that a phase segregation mechanism is needed to desynchronize neural responses at their origin for representing multiple objects. In particular, the inhibitory inter-neurons that are found in the cortex give the desired behavior as demonstrated in other studies. The purpose of the present research is to analyze required measures of phase segregation for ensuring coherence in topologies that contain direct and indirect pathways from a source to a destination area. Our results are compatible with psychological studies. We employ the spike response model on top of a localist, connectionist architecture designed for representing symbolic and relational information for verifying our results.

Distal spike initiation zone location estimation by morphological simulation of ionic current filtering demonstrated in a novel model of an identified Drosophila motoneuron.

Studying ion channel currents generated distally from the recording site is difficult because of artifacts caused by poor space clamp and membrane filtering. A computational model can quantify artifact parameters for correction by simulating the currents only if their exact anatomical location is known. We propose that the same artifacts that confound current recordings can help pinpoint the source of those currents by providing a signature of the neuron’s morphology. This method can improve the recording quality of currents initiated at the spike initiation zone (SIZ) that are often distal to the soma in invertebrate neurons. Drosophila being a valuable tool for characterizing ion currents, we estimated the SIZ location and quantified artifacts in an identified motoneuron, aCC/MN1-Ib, by constructing a novel multicompartmental model. Initial simulation of the measured biophysical channel properties in an isopotential Hodgkin-Huxley type neuron model partially replicated firing characteristics. Adding a second distal compartment, which contained spike-generating Na+ and K+ currents, was sufficient to simulate aCC’s in vivo activity signature. Matching this signature using a reconstructed morphology predicted that the SIZ is on aCC’s primary axon, 70 μm after the most distal dendritic branching point. From SIZ to soma, we observed and quantified selective morphological filtering of fast activating currents. Non-inactivating K+ currents are filtered ∼3 times less and despite their large magnitude at the soma they could be as distal as Na+ currents. The peak of transient component (NaT) of the voltage-activated Na+ current is also filtered more than the magnitude of slower persistent component (NaP), which can contribute to seizures. The corrected NaP/NaT ratio explains the previously observed discrepancy when the same channel is expressed in different cells. In summary, we used an in vivo signature to estimate ion channel location and recording artifacts, which can be applied to other neurons.

Calcium sensor parameters and readout configurations for activity-dependent homeostatic regulation of pyloric network rhythms in the lobster stomatogastric ganglion

In central pattern generating (CPG) neural networks,activity-dependent homeostatic regulation (ADHR) hasbeen proposed to explain the experimentally observedrobust activity that persists in spite of constant molecularturnover and environmental changes. In the pyloric CPGnetwork of the lobster stomatogastric ganglion (STG),ADHR is dependent on and correlated with levels of intra-cellular calcium, which acts as a second messenger thataffects ion channel and synaptic properties of the cell. Pre-vious studies showed that calcium sensors can be used tomaintain stable activity levels in individual model neu-rons [1] and pyloric rhythms in one model network [2].For regulation, these studies used deviations of the cal-cium current from a target value. However, they did notaddress the choice of sensor activation and inactivationvariables, and the robustness of selected parameters andsensor configurations in the network. To address theseissues, we developed a testbed that judges the quality of asensor by using its readings to make a prediction aboutwhether a network activity pattern is functional.To make predictions, we used a classifier trained with sen-sor readings from a model pyloric network database [3].Based on their selected activity characteristics being simi-lar to biological data, 2% of these networks were labeledas functional. In each testbed with different sensor place-ments and parameters, the percentage of functional net-works correctly predicted by the classifier is indicated witha success rate.Directly using the average calcium concentration from thethree model cells of the network resulted in a 52% predic-tion success if shuffled, establishing a control case, com-pared to 77% without shuffling. Using average calciumcurrent instead of the concentration, we obtained a simi-lar success (77%), supporting the choice by earlier cal-cium sensor models [1,2]. We confirmed that the successrate increased by the addition of activation (78%) andinactivation (86%) variables in the averaged sensors,showing that the inactivation component is indispensable(see Figure 1). By testing all combinations of selected acti-vation and inactivation parameters, we found their opti-mal values. It is biologically reasonable for the sensorminimal and maximal values to be involved in regulationand using them in addition to the sensor averagesincreased the success to 87%. Finally, using the fast, slowand DC sensors proposed earlier [1] together in the samecell marginally increased the success further to 88%.Taken together, our results suggest that activity sensing forADHR of the pyloric network can potentially be achievedwith relatively few, simple calcium sensors and that theproperties of these sensors need not necessarily beadjusted to the particular role of each neuron in the net-work.

Output variability across animals and levels in a motor system

Rhythmic behaviors vary across individuals. We investigated the sources of this output variability across a motor system, from the central pattern generator (CPG) to the motor plant. In the bilaterally symmetric leech heartbeat system, the CPG orchestrates two coordinations in the bilateral hearts with different intersegmental phase relations (Δϕ) and periodic side-to-side switches. Population variability is large. We show that the system is precise within a coordination, that differences in repetitions of a coordination contribute little to population output variability, but that differences between bilaterally homologous cells may contribute to some of this variability. Nevertheless, much output variability is likely associated with genetic and life history differences among individuals. Variability of Δϕ were coordination-specific: similar at all levels in one, but significantly lower for the motor pattern than the CPG pattern in the other. Mechanisms that transform CPG output to motor neurons may limit output variability in the motor pattern.