Input-dependent switching of inhibitory configurations in neural networks
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1
New York University , United States
The responses of neurons during a stimulus depend on the summed excitatory (E) and inhibitory (I) synaptic inputs. Shifts in the E-I balance cause both qualitative and quantitative changes in the neuronal firing patterns. How the E and I inputs scale with stimuli is yet unclear. An important determinant is the network architecture, broadly classified either as the lateral inhibitory network (LIN) where the I inputs to a neuron are more broadly tuned than the E inputs, or the co-tuned network (CON) where the E and I inputs co-vary for the entire stimulus range. E and I scale with input differently in each configuration to produce complementary sets of responses, suggesting that both may be needed to account for the diverse stimulus-evoked firing behavior. Here I show that a single E-I network transitions seamlessly between LIN and CON when the amplitude and spatial extent of the input to the network changes. Simulations were performed with a 2D network (10000 E cells; 2000 I cells) of Adaptive exponential integrate and fire neurons adjusted to reproduce the firing of pyramidal cells, and fast spiking and low threshold spiking interneurons. The patterns of connections between E and I cells were based on experimental data obtained from paired recordings in an in vitro slice preparation. Synaptic barrages (200 ms duration) were delivered to each cell; the number of barrages was adjusted so that the mean input was Gaussian distributed (parameterized by amplitude A and standard deviation S) in space. When the input was narrow (small S), the network was configured as LIN. E cells tended to fire tonically, and exhibited side-band inhibition. As the input broadened, the network switched to CON. The neurons tended to fire phasically and exhibited no sideband inhibition. To characterize quantitatively the variation of E-I balance and firing in the A-S space, mean field techniques were used to calculate the population activity of the neurons in the network. This yielded relations that describe how the widths and amplitude of excitatory and inhibitory inputs and the associated firing patterns change with the magnitude and spatial distribution of the input. Physiologically, the Gaussian input may represent tuning to stimuli such as tone frequency in the auditory system or disc location in the visual system; A may vary with stimulus intensity while S with bandwidth or disc diameter. The changes in firing that occur with increasing the bandwidth/diameter of auditory/visual stimulus are reproduced by tracing appropriate trajectories in the A and S space. That the LIN and CON configurations are not hardwired into the network has important implications. The ability to switch between configurations provides potentially a mechanism for modulating the response of the network to a variety of inputs and behavioral states. Additionally, many of the heterogeneous firing and receptive field properties that had been postulated to arise from different network configurations may in fact be due to transitions within a single network that are triggered perhaps by changes in the stimulus characteristics or state of the animal.
Conference:
Computational and Systems Neuroscience 2010, Salt Lake City, UT, United States, 25 Feb - 2 Mar, 2010.
Presentation Type:
Oral Presentation
Topic:
Oral presentations
Citation:
Reyes
AD
(2010). Input-dependent switching of inhibitory configurations in neural networks.
Front. Neurosci.
Conference Abstract:
Computational and Systems Neuroscience 2010.
doi: 10.3389/conf.fnins.2010.03.00003
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Received:
17 Feb 2010;
Published Online:
17 Feb 2010.
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Correspondence:
Alex D Reyes, New York University, New York, United States, ar65@nyu.edu