15), LGN excitatory to L4 inhibitory (P = 0.0619), and TRN inhibitory to LGN excitatory (P = 0.3). The number of neurons in each area is shown in Table 2. The model contained a total of 46 926 neurons and approximately 43 million synapses. Simple and extended versions of the Izhikevich model were used to govern the dynamics of the spiking neurons in this simulation. beta-catenin inhibitor The computational efficiency of these point neurons (single compartment) makes them ideal for large-scale simulations. Izhikevich neurons are also highly realistic and are able to reproduce at least 20 different firing modes seen in the brain, which

include: spiking, bursting, rebound spikes and bursts, subthreshold oscillations, resonance, spike frequency adaptation, spike threshold variability, and bistability of resting and spiking states (Izhikevich, 2004). Inhibitory and excitatory neurons in the cortex were modeled using the simple Izhikevich model, which are described by the following equations Raf inhibitor (Izhikevich, 2003): (2) where v is the membrane potential, u is the recovery variable, I is the input current, and a, b, c and d are parameters chosen based on the neuron type. For regular spiking, excitatory neurons, we set a = 0.01, b = 0.2, c = −65.0 and d = 8.0 (see Fig. 4). For fast-spiking, inhibitory neurons, we set a = 0.1, b = 0.2, c = −65.0 and d = 2.0 (Fig. 4). GABAergic and cholinergic neurons in the BF were modeled as simple

Izhikevich inhibitory and excitatory neurons, respectively. LGN and TRN neurons were modeled using the extended version of the Izhikevich neuron model to account for the bursting and tonic modes of activity, which these neurons have been shown to exhibit (Izhikevich & Edelman, 2008). The equations governing these neurons are given as: (5) The equations for this extended model are similar to the previous model, except they include additional parameters, such as: membrane capacitance (C), resting potential (vr) and instantaneous

threshold potential (vt). For LGN neurons, parameters were set to: a = 0.1, c = −60, d = 10, C = 200, vr = −60 and vt = −50. For TRN neurons, parameters were set to: a = 0.015, c = −55, d = 50, C = 40, vr = −65 and vt = −45 (Izhikevich & Edelman, 2008). To simulate PDK4 the switch between bursting and tonic mode, the b parameter, which is related to the excitability of the cell, was changed depending upon membrane potential, v. Specifically, if v < −65, b was set to 70 and the neuron would be in bursting mode (Fig. 4; bottom, right). If v > −65, b was set to 0 and the neuron would be in tonic mode (Fig. 4; bottom, left). The synaptic input, I, driving each neuron was dictated by simulated AMPA, NMDA, GABAA and GABAB conductances (Izhikevich & Edelman, 2008; Richert et al., 2011). The conductance equations used are well established and have been described in Dayan & Abbott (2001) and Izhikevich et al. (2004). The total synaptic input seen by each neuron was given by: (7) where v is the membrane potential and g is the conductance.