| Comparison of model-based predictions and real neuronal responses | In the simulated neuronal population, synchronized neurons had a mean minimum latency of 10.8 ms, while non-synchronized neurons had a mean minimum latency of 16.6 ms (Wilcoxon rank sum test, P < 1.4 x 1089). |
| Comparison of model-based predictions and real neuronal responses | We observed that mixed neurons had a mean minimum latency ( simulated neurons : 8.0 ms, real neurons: 16.2 ms) not significantly different from synchronized neurons (Wilcoxon rank sum test, P = 0.053 (simulated), P = 0.30 (real)) and significantly different from non-synchronized neurons (Wilcoxon rank sum test, P<3.1x10'75 (simulated), P<1.1x10'5 (real)). |
| Comparison of model-based predictions and real neuronal responses | In the simulated neuronal population, synchronized neurons had a mean onset/ sustained ratio of 0.69, while non-synchronized neurons had a mean onset/ sustained ratio of 0.18 (Wilcoxon rank sum test, P < 6.1 x 10124). |
| Methods). | We observed that for the simulated neuronal population, mixed neurons had a significantly higher discharge rate to pure tones than either non-synchronized and synchronized neurons (Fig. |
| Methods). | One potential reason for this is a higher percentage of simulated neurons receiving very strong inhibition than in our real neuronal population. |
| Model parameters underlying rate and temporal representations | While both of these examples of simulated neurons differ in the robustness of their temporal representation, they closely match the general properties of real synchronized neurons (Fig. |
| Model parameters underlying rate and temporal representations | While both of these examples of simulated neurons differ in the dynamic range of their rate representation, they closely match the general properties of real non-synchronized neurons (Fig. |
| Model parameters underlying rate and temporal representations | For example, we allowed simulated neurons to have pure tone evoked discharge rates in the range of 1—50 spk/s, reflecting the range of discharge rates observed in our real data. |
| Responses to pulse trains in real and simulated cortical neurons | Next we compared the rate and temporal representations generated by real and simulated neurons . |
| Responses to pulse trains in real and simulated cortical neurons | 2c), although simulated neurons had a substantially better temporal fidelity. |
| Comparing current and conductance-based inputs with simulated neurons | Comparing current and conductance-based inputs with simulated neurons |
| Supporting Information | Spike statistics in observed and simulated neurons . |
| Supporting Information | Inter-spike interval distributions and cross-correlograms for synaptic inputs of different strength in a neuron recorded in a slice (“Observed”) along with results from two adaptive-exponential integrate-and-fire simulated neurons , receiving inputs either Via current-based or conductance-based synapses. |
| Supporting Information | Parameters and model accuracy for simulated neurons . |
| DKL<p(x|r = 0) p(x)) is the information (per spike) carried by silences, and | The simulated neuron had Bernoulli (i.e., binary) spiking, where the probability of a spike increased linearly from 0 to 1 as evaried from —rr/2 to 17/2, that is: p(spikel0) = 0/rr+1/2. |
| Equivalence of MID and maximum-likelihood LNP | 3 illustrates this point using data from a simulated neuron With a single filter in a two-dimensional stimulus space. |
| Introduction | We illustrate this shortcoming by showing that MID can fail to find information-maximizing filters for simulated neurons with binary or other non-Poisson spike count distributions. |