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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

Information encoded by several instantaneous stimulus features, registered at varying time with respect to burst onset, for the MC (A) and IFB (B) models. Information transmitted by the discrimination of stimulus amplitude (blue), phase (red) and slope (green) at varying times, for different n-values. Digitization M = 32, information estimates are shuffle-corrected (see Section Materials and Methods).
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Figure 6: Information encoded by several instantaneous stimulus features, registered at varying time with respect to burst onset, for the MC (A) and IFB (B) models. Information transmitted by the discrimination of stimulus amplitude (blue), phase (red) and slope (green) at varying times, for different n-values. Digitization M = 32, information estimates are shuffle-corrected (see Section Materials and Methods).

Mentions: The values of the stimulus amplitude, slope, and phase used to calculate the mutual informations reported in Figures 5A7,B7 were calculated at the time of burst onset. However, nothing forbids the size of a given burst to encode the value of a stimulus feature at some other time. The analysis was therefore repeated as a function of the relative timing between burst onset and stimulus feature. The results are shown in Figure 6 with panels A and B corresponding to the MC and IFB models, respectively. We observe an overall tendency of the information to decay for times away from onset. The maximum information, however, is not exactly burst onset but, depending on the feature, it can be slightly before or after. The local maxima for encoded information about stimulus amplitude by MC bursts occur at −21 ms before and 4 ms after onset while IFB bursts produced peak information at −202, −80, −40, −8 before, and 15 ms after burst onset (see blue curves in Figures 6A,B). Interestingly, the information values for amplitude and phase decay over a time range that is significantly longer than the stimulus correlation time or the passive membrane time constant. The only other dynamical candidates for keeping track of the stimulus amplitude for prolonged times are the slow bursting currents. In the MC model, two slow currents exist: IT and ISag. The ISag current slowly rectifies prolonged (lasting >400 ms) hyperpolarizing membrane voltage deflections and has little effect on the timescales of burst stimulus selectivity. The IT current has a strong influence on burst stimulus preference. The timescale of this current is controlled by a voltage-dependent variable τh which has an average value of 42 ± 18 ms. For the IFB model, the timescale of the IT current is given as τ+h = 100 ms. The timescales of bursting currents therefore closely match the timescales of amplitude discrimination in the IFB and MC models.


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

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

Information encoded by several instantaneous stimulus features, registered at varying time with respect to burst onset, for the MC (A) and IFB (B) models. Information transmitted by the discrimination of stimulus amplitude (blue), phase (red) and slope (green) at varying times, for different n-values. Digitization M = 32, information estimates are shuffle-corrected (see Section Materials and Methods).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4585143&req=5

Figure 6: Information encoded by several instantaneous stimulus features, registered at varying time with respect to burst onset, for the MC (A) and IFB (B) models. Information transmitted by the discrimination of stimulus amplitude (blue), phase (red) and slope (green) at varying times, for different n-values. Digitization M = 32, information estimates are shuffle-corrected (see Section Materials and Methods).
Mentions: The values of the stimulus amplitude, slope, and phase used to calculate the mutual informations reported in Figures 5A7,B7 were calculated at the time of burst onset. However, nothing forbids the size of a given burst to encode the value of a stimulus feature at some other time. The analysis was therefore repeated as a function of the relative timing between burst onset and stimulus feature. The results are shown in Figure 6 with panels A and B corresponding to the MC and IFB models, respectively. We observe an overall tendency of the information to decay for times away from onset. The maximum information, however, is not exactly burst onset but, depending on the feature, it can be slightly before or after. The local maxima for encoded information about stimulus amplitude by MC bursts occur at −21 ms before and 4 ms after onset while IFB bursts produced peak information at −202, −80, −40, −8 before, and 15 ms after burst onset (see blue curves in Figures 6A,B). Interestingly, the information values for amplitude and phase decay over a time range that is significantly longer than the stimulus correlation time or the passive membrane time constant. The only other dynamical candidates for keeping track of the stimulus amplitude for prolonged times are the slow bursting currents. In the MC model, two slow currents exist: IT and ISag. The ISag current slowly rectifies prolonged (lasting >400 ms) hyperpolarizing membrane voltage deflections and has little effect on the timescales of burst stimulus selectivity. The IT current has a strong influence on burst stimulus preference. The timescale of this current is controlled by a voltage-dependent variable τh which has an average value of 42 ± 18 ms. For the IFB model, the timescale of the IT current is given as τ+h = 100 ms. The timescales of bursting currents therefore closely match the timescales of amplitude discrimination in the IFB and MC models.

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