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. 2015 Dec 22:9:80.
doi: 10.3389/fncir.2015.00080. eCollection 2015.

Corticothalamic Synaptic Noise as a Mechanism for Selective Attention in Thalamic Neurons

Sébastien Béhuret  1 , Charlotte Deleuze  2 , Thierry Bal  1
Affiliations

Corticothalamic Synaptic Noise as a Mechanism for Selective Attention in Thalamic Neurons

Sébastien Béhuret et al. Front Neural Circuits. .

Abstract

A reason why the thalamus is more than a passive gateway for sensory signals is that two-third of the synapses of thalamocortical neurons are directly or indirectly related to the activity of corticothalamic axons. While the responses of thalamocortical neurons evoked by sensory stimuli are well characterized, with ON- and OFF-center receptive field structures, the prevalence of synaptic noise resulting from neocortical feedback in intracellularly recorded thalamocortical neurons in vivo has attracted little attention. However, in vitro and modeling experiments point to its critical role for the integration of sensory signals. Here we combine our recent findings in a unified framework suggesting the hypothesis that corticothalamic synaptic activity is adapted to modulate the transfer efficiency of thalamocortical neurons during selective attention at three different levels: First, on ionic channels by interacting with intrinsic membrane properties, second at the neuron level by impacting on the input-output gain, and third even more effectively at the cell assembly level by boosting the information transfer of sensory features encoded in thalamic subnetworks. This top-down population control is achieved by tuning the correlations in subthreshold membrane potential fluctuations and is adapted to modulate the transfer of sensory features encoded by assemblies of thalamocortical relay neurons. We thus propose that cortically-controlled (de-)correlation of subthreshold noise is an efficient and swift dynamic mechanism for selective attention in the thalamus.

Keywords: activity decorrelation; corticothalamic feedback; gain control; selective attention; sensory transfer; synaptic noise; thalamic gateway; thalamocortical system.

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Figures

Figure 1
Figure 1
Synaptic noise tunes the transfer function of thalamocortical cells recorded in vitro. (A) Illustration of a thalamic slice where a TC cell is recorded using a patch pipette. (B) The dynamic-clamp technique is used to stimulate TC cells with artificial sensory inputs and synaptic conductances, thus mimicking the impact of the corticothalamic feedback during sensory integration. (C) Voltage during injection of discrete retinal-like input conductance (quiescent) and with additional inhibitory plus excitatory background conductance that were either non-fluctuating (static) or stochastically fluctuating (noise). Combined inhibitory and excitatory conductances reduced the input resistance to ~50% (insets in quiescent and static). (D) Probabilities of input conductance strengths to evoke at least one spike within a 20 ms delay, fitted to sigmoid functions. In the noise condition (but not static; not shown, see in Wolfart et al., 2005), a multiplicative gain is induced, corresponding to a slope change of the response curve, and characterized by an increased sensitivity to small inputs and a decreased sensitivity to large inputs. Decreasing the variance of noise conductance values from high voltage variance noise (high std noise; 3.65 mV; n = 24) to low noise (low std noise; 2.6 mV; n = 5) changes the input-output slope and the sensitivity to small inputs. (E) Changing the ratio of excitatory/inhibitory conductances (1/1, 1/2, 1/3, and 1/4) induces an additive gain that shifts the dynamic input sensitivity range of the transfer function toward smaller inputs (leftward green shift) for higher ratios and toward larger inputs for lower ratios (rightward red shift). Modified from Wolfart et al. (2005).
Figure 2
Figure 2
Noise linearizes the transfer function of thalamocortical neurons through increased burstiness. (A) Typical burst response in the quiescent hyperpolarized state. (B) Bursts also occurs in response to inputs when the neuron is depolarized by background synaptic noise. (C) During resting condition, plotting the average total number of spikes per burst response against the input shows that noise linearized the staircase-like transfer function across the whole input range. Modified from Wolfart et al. (2005).
Figure 3
Figure 3
T-current tunes the transfer function of TC neurons and contributes to single-spike and burst firing at depolarized membrane potentials. (A) Left panel. The rebound low-threshold spike evoked in a TC neuron following a hyperpolarizing current step (control condition) is blocked by TTA-P2 and restored by dynamic clamp injection of gT. Middle panel. Voltage traces of a TC neuron injected with a sequence of AMPA conductances (gAMPA) of different amplitudes in control condition and in the presence of TTA-P2. The neuron received the same fluctuating excitatory and inhibitory conductance noise in each condition and displayed a mean membrane potential of −58 mV. The smallest AMPA conductances failed to evoke a spike when the T-current was blocked (red) and the spike probability was restored upon artificial gT injection (light blue). Right panel. Zoom on the firing activity in response to gAMPA of increasing amplitudes from the recording shown on the middle panel. Both single-spike and burst responses were conditioned by the presence of the T-current. (B) Transfer functions of the neuron presented in (A) show that the T-current block shifted the input-output curve toward larger AMPA conductances [same color code as in (A)]. Recovery was obtained with injection of gT. (C) Histograms present the probability of single-spike (gray area) and burst (black curve) generation as a function of the gAMPA amplitude in each condition. In the absence of T-current, the single-spike probability curve was shifted toward larger gAMPA and the burst probability was drastically reduced. The single-spike probability was fitted to a Gaussian function (colored line) to estimate the gAMPA conductance leading to the maximal probability (dashed line). Modified from Deleuze et al. (2012).
Figure 4
Figure 4
T-current provides robustness to the response of TC neurons across a large range of membrane potentials. (A) Spike raster plots of a TC neuron injected with gAMPA of fixed amplitude following a Poisson-distribution (mean frequency: 10 Hz), while the mean membrane potential was successively maintained at −60, −65, and −70 mV. (B) Intracellular activities recorded during the time windows indicated by the black line in (A). In control condition the firing of the neuron remained almost invariant across the entire voltage range, but strongly decreased upon hyperpolarization when T-current was blocked. (C) Firing frequencies calculated in each neuron successively maintained at a membrane potential between −60/−55 mV and between −72/−67 mV while being submitted to the same gAMPA/noise sequences. Hyperpolarization induced either a decrease or an increase in firing frequency in control condition (CTR; n = 13) but a systematic decrease in the presence of TTA-P2 (TTA; n = 14). (D) In each neuron, the firing frequency at hyperpolarized potentials was normalized to the one at −60/−55 mV. Comparison of the mean values obtained with (CTR) and without T-current (TTA; **p < 0.01; independent t-test) suggests that at the level of the TC neuronal population, T-channels rescued the voltage dependent decrease in firing induced by hyperpolarization. (E) Transfer functions were quasi-invariant in the presence of the T-current but drastically shifted toward larger gAMPA values upon hyperpolarization when the T-current was blocked. (F) Similar voltage dependence of the transfer functions was observed in TC neurons recorded in Cav3.1-/- knock-out mice devoid of T-current. (E,F) are from Deleuze et al. (2012).
Figure 5
Figure 5
Thalamocortical convergent circuit. Biological or model TC cells network synaptically converge to a model recipient cortical neuron. The thalamic population receives a model retinal input in addition to a corticothalamic input mimicked through the injection of stochastically fluctuating mixed excitatory and inhibitory conductances. Details on the implementation of this circuit are available in Béhuret et al. (2013).
Figure 6
Figure 6
(A) Effect of the cortical excitatory and inhibitory mean input conductances on the sensory transfer efficiency. Mean conductances were normalized relative to the resting conductance of the thalamic cells. Arrows indicate specific operating regimes illustrated in (C). The normalized conductance standard deviations were normalized relative to their respective means, and set to 0.2 for both excitation and inhibition. (B) Similar to (A) for the standard deviation of the excitatory and inhibitory conductances. The normalized conductance means were set to 1.5 for the excitation and 1.0 for the inhibition. (C) Membrane voltage traces for the four specific regimes denoted by the arrows in (A,B). The thalamic spike synchrony was measured with cortical spike-triggered average. The number of thalamic spikes evoked in the corresponding regimes was averaged using a bin size of 1 ms and was then normalized to the total number of TC cells. Grayed areas represent the standard deviation of the counts across all cortical spikes (n > 103 in every bin). Modified from Béhuret et al. (2013).
Figure 7
Figure 7
Effect of synaptic noise correlation across TC cells on sensory information transfer. (A) Normalized transfer efficiency in model (gray curve) and biological networks (colored curves) for increasing levels of synaptic noise correlation across thalamic neurons. (B) Average transfer efficiency reduction (± SEM) across all biological networks shown in (A) (***p < 3.10−4; t-test; n = 15). (C) Illustration of voltage traces for a biological network receiving uncorrelated synaptic bombardment. (D) Same biological network as in (C) receiving correlated synaptic bombardment. Retinal spikes that were detected by the recipient cortical neuron in (C) but not detected in (D) are indicated by blue arrows. (E) Zoomed sections of membrane potential fluctuations underlined in (C) (sections 1–4; uncorrelated synaptic bombardment) and (D) (sections 1'–4'; correlated synaptic bombardment). Modified from Béhuret et al. (2013).
Figure 8
Figure 8
Effect of thalamic oscillations on sensory information transfer. (A) Transfer efficiency for synchronized oscillations. (B) Same as (A) for desynchronized oscillations. (C) Thalamic membrane potential traces for oscillation amplitudes of 0.1 nA [arrow 1 in (A), degraded transfer] and 0.4 nA [arrow 2 in (B), permissive transfer]. Modified from Béhuret et al. (2013).
Figure 9
Figure 9
Speculative role of synaptic bombardment decorrelation and thalamic oscillations in selective attention. (A) Visual stimulation composed of bars of various orientation. Focusing attention on a single bar (for instance vertical) will slowly segregate all other bars of same orientation from the context made of other bars of dissimilar orientation. Vertical bars are colored in brown for illustration purposes only. (B) Presumed functional steps involved when focusing attention on a vertical bar (see text for details). Bars shown on each neuron illustrate the orientation preference. Columnar organization of V1 circuits is not illustrated although each cortical neuron shown in this schema belongs to a different orientation column. Modified from Béhuret et al. (2013).
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