Comparison of model-based predictions and real neuronal responses | 6a), while balanced excitation and inhibition in non-synchronized neurons led to weaker net excitation that was spread out over a longer time duration (Fig. |
Discussion | Conversely, non-synchronized responses were generated when excitation and inhibition were concurrent and balanced, which resulted in weak net excitation. |
Discussion | Based on the relationship between excitation and inhibition used to generate temporal and rate representations in our computational model, we were able to make several testable predictions including differences in the temporal fidelity, discharge rates and temporal dynamics of evoked responses, which were subsequently confirmed in our real neuronal population. |
Discussion | Because non-synchronized responses are generated by weak net-excitation, usually by concurrent excitation and inhibition , any anesthesia related decrease in excitation or increase in inhibition would further decrease the neuron’s net excitation, potentially silencing non-synchronized responses [52—54]. |
Impact of spontaneous rate on computational model | To generate a spontaneous rate, we added Gaussian noise to the excitatory and inhibitory conductances of the neuron. |
Introduction | This difference is reflected in the organization of each cell’s receptive f1eld- excitation and inhibition are spatially segregated in simple cells, but spatially overlapping in complex cells. |
Introduction | We reasoned that synchronized and non-synchronized responses in auditory cortex could be generated by a similar relationship between excitation and inhibition , with the degree of segregation between these two inputs varying in the time domain, rather than the spatial domain. |
Introduction | To investigate this, we simulated an auditory cortical neuron using an integrate-and-f1re computational neuronal model [23—24] , and measured how changing the relative timing between excitatory and inhibitory inputs affected a neuron’s representation of temporal information. |
Model parameters underlying rate and temporal representations | In contrast to this, non-synchronized neurons were more common when the net excitation was weak, which occurred for I/E ratios close to one (balanced excitation and inhibition ) or low I/E ratios in combination with a weak excitatory input (Fig. |
Results | We tested our model With acoustic pulse trains spanning the perceptual range of flutter/ fusion perception, with interpulse intervals (IPIs) ranging between 3—75 ms. Each acoustic pulse was modeled as a change in the excitatory and inhibitory conductance, governed by an alpha function with a 5 ms time constant (Fig. |
Current-based vs conductance-based synaptic input | As with the experimental data, we find that, in both the current-based and conductance-based models, higher amplitude synapses are easier to detect and there is an asymmetry between excitatory and inhibitory inputs with inhibitory inputs being more difficult to detect than excitatory inputs of the same magnitude. |
Detection of connectivity in fully-defined input setting | We used a presynaptic population consisting of the equal number of excitatory and inhibitory neurons, with log-normal distribution of synaptic amplitudes (same distribution, positive weights for excitatory, negative weights for inhibitory), and PSC kernels consisted of the same difference of two exponentials with time constants of 0.5ms and 5ms. |
Detection of connectivity in fully-defined input setting | This paradigm for injection of fully-defined current allows us to examine the detectability of excitatory and inhibitory inputs of multiple amplitudes using the same recording [40]. |
Electrophysiology and current injection | 2) Fully-defined current was produced by the firing of a large population of simulated presynaptic excitatory and inhibitory neurons whose postsynaptic currents (PSCs) sum to mimic fluctuating, naturalistic input. |
Experiment 2. Fully-defined input produced by a population of spiking neurons | Excitatory and inhibitory inputs are assumed to have the same distribution, differing only by the sign. |
Introduction | The fully-defined input is composed of excitatory and inhibitory postsynaptic currents produced by firing of large number of simulated presyn-aptic neurons. |
Introduction | 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. |
Prediction of spikes | Because spike times of all N = 1024 excitatory and inhibitory presynaptic neurons composing the total input to the postsynaptic neuron (Fig. |
input experiments. | The coupling coefficients accurately reconstruct both excitatory and inhibitory input amplitudes over a broad range, and this reconstruction becomes more accurate with higher postsynaptic firing rates (Fig. |
mined by the exponential nonlinearitygLATexp< ), and the adaptation variable has its own | To modify the synaptic inputs for changing conductances we split the inputs into excitatory and inhibitory contributions with two separate reversal potentials ry and inhibitory contributions. |
mined by the exponential nonlinearitygLATexp< ), and the adaptation variable has its own | For the conductance-based models, since we want the average PSC for each presynaptic input to match the original inputs, we introduce an additional constraint that the excitatory and inhibitory input be balanced: 611 < V(t) — VI > = — < V(t) — VE > aE. |
Abstract | We also show that by considering excitatory and inhibitory STDP at lateral connections, the circuit can acquire a lateral structure optimal for signal detection. |
Discussion | We also investigated the functional roles of STDP at lateral excitatory and inhibitory connections to demonstrate that |
Excitatory and inhibitory STDP cooperatively shape structured lateral connections | Excitatory and inhibitory STDP cooperatively shape structured lateral connections |
Excitatory and inhibitory STDP cooperatively shape structured lateral connections | The synaptic weight dynamics of lateral excitatory and inhibitory connections are approximately given as |
Introduction | We also found that excitatory and inhibitory STDP cooperatively shapes lateral circuit structure, making it suitable for signal detection. |
Model | Where ngE and ngI are excitatory and inhibitory conductances, respectively, and 1‘5 and tks are the spike timings of input neuron i and lateral neuron k. Similarly, for inhibitory neurons in the lateral layer, |