Other phase relationships between neurons can be obtained by choosing appropriate groups of PNs from the 2D ordering of excitatory neurons shown in Figure 5 and Figure 7B (top panels). More complex phase relationships can be generated by using a larger number of colors and multiple colorings of the network. This simple example illustrates that knowing the coloring structure of the inhibitory network, we can predict the dynamics of the excitatory principal cells despite the complex and seemingly random synaptic structure AZD8055 cost between excitatory

and inhibitory neurons. The ultimate goal of exploring sensory network dynamics is to understand the spatiotemporal activity of excitatory principal neurons since this activity is what typically drives the responses of neurons at downstream levels of processing. In many circuits where information processing is based on the detection of coincidence between spikes (for example, between insect the AL and MB), a property important for understanding information flow is synchrony between excitatory neurons. In this study we showed a relationship between the connectivity structure of the inhibitory subnetwork

and synchronization properties of excitatory neurons. Furthermore, we used the coloring of the inhibitory subnetwork as a tool to construct a space in which the distance between excitatory neurons is defined not by the length of the synaptic path connecting those neurons, but by the similarity of the inhibitory input they receive. This description

optimally matches the perspective of the downstream neurons looking for synchrony in ensembles of presynaptic cells and, therefore, allows a low-dimensional selleck products description of seemingly complex high-dimensional network activity. Individual PNs and LNs were modeled by a single compartment that included voltage- and Ca2+-dependent currents described by Hodgkin-Huxley kinetics (Hodgkin and Huxley, 1990). Since the biophysical makeup of insects’ olfactory neurons has not yet been completely characterized, we used parameters drawn from well-described cell types while following two guiding principles: (1) minimize the number of currents and their complexity in each too cell type; (2) generate realistic (though simplified) firing profiles. Our LN model includes a transient Ca2+ current (Laurent et al., 1993), a calcium-dependent potassium current (Sloper and Powell, 1979), a fast potassium current (Traub and Miles, 1991), and a potassium leak current, thus producing profiles devoid of Na+ action potentials but capable of Ca2+-dependent active responses, as observed experimentally (Laurent and Davidowitz, 1994). Our PN model includes a fast sodium current (Traub and Miles, 1991), a fast potassium current (Traub and Miles, 1991), a transient K+ A-current (Huguenard et al., 1991) and a potassium leak current IKL. Equations for all intrinsic currents in locust LNs and PNs can be found in Bazhenov et al., 2001a and Bazhenov et al., 2001b.