Using this data, we create a detailed single cell model and simulate synaptic input .

We then summarize the results of the simulations using a simple abstracted model, that ultimately describes the computation layer 5 pyramidal neurons perform on synaptic input .

We modify the published Hay and colleagues [17] L5b pyramidal neuron multi-compartmen-tal model to further probe the interaction of synaptic inputs and intrinsic membrane nonline-arities.

To quantify the relationship between synaptic inputs , Ca2+ spiking, and bAPs, we adapt a detailed biophysical multi-compartmental model able to emulate and recreate the physiological properties of mouse V1 L5 pyramids [17].

In our modeling work, we postulate two groups of excitatory, glutamatergic synaptic input .

In our modeling work, a decrease of synaptic input led to a loss of the coincident detection mechanism (Fig.

The model is then used to explore the biophysical computation that single neurons perform on spatio-temporal patterns of synaptic input , which can be precisely controlled in silico alongside details of the intrinsic biophysics.

Synaptic inputs in the multicompartmental model

We use the multi-compartmental pyramidal cell model [17] to further explore the relationship between the different nonlinearities found in our experiments and their role in the transformation between synaptic input and action potential output.

Unlike our experiments, we have complete control over every aspect of the simulation (including synaptic input ), and can explicitly study the role of specific conductances (e.g.

By analyzing the firing of neurons in response to these artificial inputs, we ask 1) How does functional connectivity inferred from spikes relate to simulated synaptic input ?

We find that individual current-based synaptic inputs are detectable over a broad range of amplitudes and conditions.

These results illustrate the possibilities and outline the limits of inferring synaptic input from spikes.

We then analyzed the postsynaptic responses aiming 1) to examine whether input can be detected based on spikes alone, 2) to quantify how much data is necessary to detect a synaptic input of a given strength, 3) to quantify how much data is necessary to detect changes in input strength, and 4) to determine how accurately such pairwise models describe and predict spiking of the postsynaptic neuron.

Traditionally, the effects of synaptic input on postsynaptic spiking are assessed using descriptive, cross-correlation methods (Fig.

Consistent with results of cross-corre-lation and area-under-the-curve analysis, the model with coupling provides a better fit to the data than the spike-history alone model when the amplitude of added synaptic input is 0.5 a or larger.

Here we provide empirical tests of statistical tools for such analysis using in vitro current injection where the true synaptic input is known.

Sparse sampling of neurons and large electrode spacing may contribute somewhat to the difficulty in interpreting the results of functional connectivity analyses of cortical circuits, but it is also unclear what information these inference methods can provide about actual synaptic inputs and what limitations there are to the use of these methods in general.

We ask how well synaptic inputs of different amplitudes can be detected, how much data is necessary to reconstruct the amplitudes of excitatory and inhibitory synaptic inputs , and how precisely synaptic weights can be estimated from spikes alone.

Here we examine the relationship between simulated synaptic input and functional connections estimated from spikes using in vitro current injection experiments.

To test whether distributed synaptic inputs might influence the shape of the PRC, we delivered random synaptic inputs to the dendrites of the model neuron.

We found that, even when the instantaneous rates of inhibitory and excitatory inputs were relatively stable, synaptic inputs greatly affected the shape of the PRC (S3 Fig, panel A).

By capturing the relationship between the evoked phase shift Ag) and the phase (p at which the input pulse occurred, the PRC predicts how, upon receiving weak synaptic inputs , neurons transiently delay or accelerate AP firing, contribute to network-wide AP synchrony, integrate external inputs or detect their temporal coincidences.

In the case of the model Without synaptic inputs we used the PMlO model [40], set the temperature of the simulation to 28°C and injected a somatic current of varying amplitude to span a Wide range of firing rates.

In the case of the model With synaptic inputs , we used the PM9 model With synapses distributed on the dendritic tree as described in [41], set the temperature to 37°C and fixed the presynaptic excitatory and inhibitory firing rates to 35 and 2 HZ, respectively.