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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

Stimulus features associated with bursts containing n spikes. Event-triggered averages for the MC (A1) and IFB (B1) models, for different n-values (see color key at top center). Time = 0 ms marks the first spike in the burst. The values of several stimulus features are averaged and plotted as a function of n(A2–A7, B2–B7), including pre-onset hyperpolarizing stimulus charge (N.int) (2), post-onset depolarizing stimulus charge (P.int) (3), stimulus minimum prior to onset (4), stimulus amplitude (5), slope (6), and stimulus phase (7). Amplitude, slope and phase are all calculated at burst onset. Error bars represent ±1SE of the mean. Mean phase and the corresponding error bars are calculated with circular statistics.
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Figure 3: Stimulus features associated with bursts containing n spikes. Event-triggered averages for the MC (A1) and IFB (B1) models, for different n-values (see color key at top center). Time = 0 ms marks the first spike in the burst. The values of several stimulus features are averaged and plotted as a function of n(A2–A7, B2–B7), including pre-onset hyperpolarizing stimulus charge (N.int) (2), post-onset depolarizing stimulus charge (P.int) (3), stimulus minimum prior to onset (4), stimulus amplitude (5), slope (6), and stimulus phase (7). Amplitude, slope and phase are all calculated at burst onset. Error bars represent ±1SE of the mean. Mean phase and the corresponding error bars are calculated with circular statistics.

Mentions: To determine the stimulus features encoded by bursts of different length, we used reverse correlation methods (Rieke, 1997; Chichilnisky, 2001; Samengo et al., 2013b). We stimulated both thalamic models with an OU stimulus current, and we identified the events ranging from single spikes to 6-spike bursts. Sections of the stimulus surrounding each of these events (event-triggered stimuli) were then collected. In all cases, the number of event-triggered stimuli for each n-spike event exceeded 1 × 104. Figures 3A1,B1 shows n-spike event-triggered stimulus averages (n-ETAs) for different burst sizes from the MC (A1) and IFB (B1) models (see Section Materials and Methods). For both models, all n-ETAs contain a pre-onset hyperpolarization followed by a post-onset depolarization (Time = 0 ms indicates burst onset). The double-peak structure observed for positive times in both models closely resembles the results obtained by Samengo et al. (2013b) for quadratic bursters. The amplitude of the pre and the post-onset features grows with increasing n. While both the MC and IFB bursts have qualitatively similar stimulus preference, the hyperpolarizing trajectory of the stimulus prior to burst onset is longer in the IFB model. Longer IFB bursts are evoked by hyperpolarizations of increased duration, whereas longer MC bursts are evoked by hyperpolarization of increased (negative) amplitude.


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

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

Stimulus features associated with bursts containing n spikes. Event-triggered averages for the MC (A1) and IFB (B1) models, for different n-values (see color key at top center). Time = 0 ms marks the first spike in the burst. The values of several stimulus features are averaged and plotted as a function of n(A2–A7, B2–B7), including pre-onset hyperpolarizing stimulus charge (N.int) (2), post-onset depolarizing stimulus charge (P.int) (3), stimulus minimum prior to onset (4), stimulus amplitude (5), slope (6), and stimulus phase (7). Amplitude, slope and phase are all calculated at burst onset. Error bars represent ±1SE of the mean. Mean phase and the corresponding error bars are calculated with circular statistics.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4585143&req=5

Figure 3: Stimulus features associated with bursts containing n spikes. Event-triggered averages for the MC (A1) and IFB (B1) models, for different n-values (see color key at top center). Time = 0 ms marks the first spike in the burst. The values of several stimulus features are averaged and plotted as a function of n(A2–A7, B2–B7), including pre-onset hyperpolarizing stimulus charge (N.int) (2), post-onset depolarizing stimulus charge (P.int) (3), stimulus minimum prior to onset (4), stimulus amplitude (5), slope (6), and stimulus phase (7). Amplitude, slope and phase are all calculated at burst onset. Error bars represent ±1SE of the mean. Mean phase and the corresponding error bars are calculated with circular statistics.
Mentions: To determine the stimulus features encoded by bursts of different length, we used reverse correlation methods (Rieke, 1997; Chichilnisky, 2001; Samengo et al., 2013b). We stimulated both thalamic models with an OU stimulus current, and we identified the events ranging from single spikes to 6-spike bursts. Sections of the stimulus surrounding each of these events (event-triggered stimuli) were then collected. In all cases, the number of event-triggered stimuli for each n-spike event exceeded 1 × 104. Figures 3A1,B1 shows n-spike event-triggered stimulus averages (n-ETAs) for different burst sizes from the MC (A1) and IFB (B1) models (see Section Materials and Methods). For both models, all n-ETAs contain a pre-onset hyperpolarization followed by a post-onset depolarization (Time = 0 ms indicates burst onset). The double-peak structure observed for positive times in both models closely resembles the results obtained by Samengo et al. (2013b) for quadratic bursters. The amplitude of the pre and the post-onset features grows with increasing n. While both the MC and IFB bursts have qualitatively similar stimulus preference, the hyperpolarizing trajectory of the stimulus prior to burst onset is longer in the IFB model. Longer IFB bursts are evoked by hyperpolarizations of increased duration, whereas longer MC bursts are evoked by hyperpolarization of increased (negative) amplitude.

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