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Thalamic neuron models encode stimulus information by burst-size modulation.

Elijah DH, Samengo I, Montemurro MA - Front Comput Neurosci (2015)

Bottom Line: We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc.Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code.Thus, our results suggest the n-spike burst code is a general property of thalamic neurons.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Life Sciences, The University of Manchester Manchester, UK.

ABSTRACT
Thalamic neurons have been long assumed to fire in tonic mode during perceptive states, and in burst mode during sleep and unconsciousness. However, recent evidence suggests that bursts may also be relevant in the encoding of sensory information. Here, we explore the neural code of such thalamic bursts. In order to assess whether the burst code is generic or whether it depends on the detailed properties of each bursting neuron, we analyzed two neuron models incorporating different levels of biological detail. One of the models contained no information of the biophysical processes entailed in spike generation, and described neuron activity at a phenomenological level. The second model represented the evolution of the individual ionic conductances involved in spiking and bursting, and required a large number of parameters. We analyzed the models' input selectivity using reverse correlation methods and information theory. We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc. The stimulus feature that is most efficiently encoded by bursts, however, need not coincide with one of such classical features. We therefore searched for the optimal feature among all those that could be expressed as a linear transformation of the time-dependent input current. We found that bursting neurons transmitted 6 times more information about such more general features. The relevant events in the stimulus were located in a time window spanning ~100 ms before and ~20 ms after burst onset. Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code. Thus, our results suggest the n-spike burst code is a general property of thalamic neurons.

No MeSH data available.


Related in: MedlinePlus

Probability density functions of stimulus features triggering bursts of n spikes and associated information, for MC (A) and IFB (B) models. Instantaneous stimulus features (A1–A6, B1–B6), with the same color code as in Figure 3 (see key). For better visualization, distributions are plotted with a resolution of M = 256 bins. Information values (displayed in A7, B7) were calculated with a coarser binning M = 32 to reduce estimation bias.
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Figure 5: Probability density functions of stimulus features triggering bursts of n spikes and associated information, for MC (A) and IFB (B) models. Instantaneous stimulus features (A1–A6, B1–B6), with the same color code as in Figure 3 (see key). For better visualization, distributions are plotted with a resolution of M = 256 bins. Information values (displayed in A7, B7) were calculated with a coarser binning M = 32 to reduce estimation bias.

Mentions: In Figures 3A2–A7,B2–B7, we showed that burst size varied mostly monotonically with the mean value of several instantaneous stimulus features. However, an analysis of mean values does not suffice to assess the quality of the encoding: Variances and higher moments also matter. We therefore analyze the whole distribution of stimulus parameters eliciting bursts of a given size. In Figures 5A1–A6,B1–B6 we plot the normalized histograms of instantaneous stimulus features evoking bursts of different duration. Intuitively, if the distributions associated to bursts containing different number of spikes are not separable, burst duration does not encode information about the tested feature. For a quantitative assessment, we estimated the mutual information between n and stimulus feature F (see Section Materials and Methods). The results are displayed in Figures 5A7,B7.


Thalamic neuron models encode stimulus information by burst-size modulation.

Elijah DH, Samengo I, Montemurro MA - Front Comput Neurosci (2015)

Probability density functions of stimulus features triggering bursts of n spikes and associated information, for MC (A) and IFB (B) models. Instantaneous stimulus features (A1–A6, B1–B6), with the same color code as in Figure 3 (see key). For better visualization, distributions are plotted with a resolution of M = 256 bins. Information values (displayed in A7, B7) were calculated with a coarser binning M = 32 to reduce estimation bias.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4585143&req=5

Figure 5: Probability density functions of stimulus features triggering bursts of n spikes and associated information, for MC (A) and IFB (B) models. Instantaneous stimulus features (A1–A6, B1–B6), with the same color code as in Figure 3 (see key). For better visualization, distributions are plotted with a resolution of M = 256 bins. Information values (displayed in A7, B7) were calculated with a coarser binning M = 32 to reduce estimation bias.
Mentions: In Figures 3A2–A7,B2–B7, we showed that burst size varied mostly monotonically with the mean value of several instantaneous stimulus features. However, an analysis of mean values does not suffice to assess the quality of the encoding: Variances and higher moments also matter. We therefore analyze the whole distribution of stimulus parameters eliciting bursts of a given size. In Figures 5A1–A6,B1–B6 we plot the normalized histograms of instantaneous stimulus features evoking bursts of different duration. Intuitively, if the distributions associated to bursts containing different number of spikes are not separable, burst duration does not encode information about the tested feature. For a quantitative assessment, we estimated the mutual information between n and stimulus feature F (see Section Materials and Methods). The results are displayed in Figures 5A7,B7.

Bottom Line: We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc.Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code.Thus, our results suggest the n-spike burst code is a general property of thalamic neurons.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Life Sciences, The University of Manchester Manchester, UK.

ABSTRACT
Thalamic neurons have been long assumed to fire in tonic mode during perceptive states, and in burst mode during sleep and unconsciousness. However, recent evidence suggests that bursts may also be relevant in the encoding of sensory information. Here, we explore the neural code of such thalamic bursts. In order to assess whether the burst code is generic or whether it depends on the detailed properties of each bursting neuron, we analyzed two neuron models incorporating different levels of biological detail. One of the models contained no information of the biophysical processes entailed in spike generation, and described neuron activity at a phenomenological level. The second model represented the evolution of the individual ionic conductances involved in spiking and bursting, and required a large number of parameters. We analyzed the models' input selectivity using reverse correlation methods and information theory. We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc. The stimulus feature that is most efficiently encoded by bursts, however, need not coincide with one of such classical features. We therefore searched for the optimal feature among all those that could be expressed as a linear transformation of the time-dependent input current. We found that bursting neurons transmitted 6 times more information about such more general features. The relevant events in the stimulus were located in a time window spanning ~100 ms before and ~20 ms after burst onset. Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code. Thus, our results suggest the n-spike burst code is a general property of thalamic neurons.

No MeSH data available.


Related in: MedlinePlus