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.

Conversely, non-synchronized responses were generated when excitation and inhibition were concurrent and balanced, which resulted in weak net excitation.

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.

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].

To generate a spontaneous rate, we added Gaussian noise to the excitatory and inhibitory conductances of the neuron.

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.

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.

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.

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.

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.

We also show that by considering excitatory and inhibitory STDP at lateral connections, the circuit can acquire a lateral structure optimal for signal detection.

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

The synaptic weight dynamics of lateral excitatory and inhibitory connections are approximately given as

We also found that excitatory and inhibitory STDP cooperatively shapes lateral circuit structure, making it suitable for signal detection.

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,