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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.

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Related in: MedlinePlus

Analysis of spike-train correlations. Mean and variance of the spike count in a 20 ms window from 100 trials, for the full responses (A1, B1), tonic model responses (A2, B2), and full responses with bursts removed (A3, B3). The fraction of information contained in stimulus-modulated correlations (ΔI∕I) is shown for MC (A4) and IFB (B4) models, for the full response (F), tonic response (T), and the full response with bursts removed (BR). Response window size L = 40 ms.
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Figure 2: Analysis of spike-train correlations. Mean and variance of the spike count in a 20 ms window from 100 trials, for the full responses (A1, B1), tonic model responses (A2, B2), and full responses with bursts removed (A3, B3). The fraction of information contained in stimulus-modulated correlations (ΔI∕I) is shown for MC (A4) and IFB (B4) models, for the full response (F), tonic response (T), and the full response with bursts removed (BR). Response window size L = 40 ms.

Mentions: The variability of neuron responses to repeated presentations of a stimulus is usually quantified by means of the Fano factor, that is, by the ratio between the spike count variance and the spike count mean, both measured in a given time window. The Fano factor of a neuron governed by a Poisson firing mechanism is equal to one. Systematic deviations from unity indicate the presence of correlations between spikes (Berry and Meister, 1998; Panzeri et al., 1999; Quian Quiroga and Panzeri, 2013). To quantify how bursts affect the variability of spike responses in the neuron models, we computed the mean M(t) and variance V(t) of the spike count in 20 ms non-overlapping windows across 100 trials (see Section Materials and Methods), as shown in Figures 2A1–A3,B1–B3. Both models contained windows with sub-Poissonian (V(t)∕M(t) < 1) and with super-Poissonian (V(t)∕M(t)>1) behavior, see Figures 2A1,B1. The points below the diagonal are a direct consequence of negative correlations induced by the refractory period (Berry and Meister, 1998; Montemurro et al., 2007a). To test whether the points above the diagonal were a consequence of bursting, we ran a modified version of the models (termed the IFB-T and MC-T models) that were incapable of producing intrinsic bursting (see Section Materials and Methods). As shown in Figures 2A2,B2, their responses produced no super-Poissonian data. Additionally, we also tested the effect of removing all bursts from the responses of the original (bursting) models and found that super-Poissonian regions were drastically reduced, as seen in Figures 2A3,B3. Both tests indicate that super-Poissonian responses (V(t)∕M(t)>1) are indeed due to bursting rather than other causes like, for instance, fluctuations due to insufficient sampling.


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

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

Analysis of spike-train correlations. Mean and variance of the spike count in a 20 ms window from 100 trials, for the full responses (A1, B1), tonic model responses (A2, B2), and full responses with bursts removed (A3, B3). The fraction of information contained in stimulus-modulated correlations (ΔI∕I) is shown for MC (A4) and IFB (B4) models, for the full response (F), tonic response (T), and the full response with bursts removed (BR). Response window size L = 40 ms.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 2: Analysis of spike-train correlations. Mean and variance of the spike count in a 20 ms window from 100 trials, for the full responses (A1, B1), tonic model responses (A2, B2), and full responses with bursts removed (A3, B3). The fraction of information contained in stimulus-modulated correlations (ΔI∕I) is shown for MC (A4) and IFB (B4) models, for the full response (F), tonic response (T), and the full response with bursts removed (BR). Response window size L = 40 ms.
Mentions: The variability of neuron responses to repeated presentations of a stimulus is usually quantified by means of the Fano factor, that is, by the ratio between the spike count variance and the spike count mean, both measured in a given time window. The Fano factor of a neuron governed by a Poisson firing mechanism is equal to one. Systematic deviations from unity indicate the presence of correlations between spikes (Berry and Meister, 1998; Panzeri et al., 1999; Quian Quiroga and Panzeri, 2013). To quantify how bursts affect the variability of spike responses in the neuron models, we computed the mean M(t) and variance V(t) of the spike count in 20 ms non-overlapping windows across 100 trials (see Section Materials and Methods), as shown in Figures 2A1–A3,B1–B3. Both models contained windows with sub-Poissonian (V(t)∕M(t) < 1) and with super-Poissonian (V(t)∕M(t)>1) behavior, see Figures 2A1,B1. The points below the diagonal are a direct consequence of negative correlations induced by the refractory period (Berry and Meister, 1998; Montemurro et al., 2007a). To test whether the points above the diagonal were a consequence of bursting, we ran a modified version of the models (termed the IFB-T and MC-T models) that were incapable of producing intrinsic bursting (see Section Materials and Methods). As shown in Figures 2A2,B2, their responses produced no super-Poissonian data. Additionally, we also tested the effect of removing all bursts from the responses of the original (bursting) models and found that super-Poissonian regions were drastically reduced, as seen in Figures 2A3,B3. Both tests indicate that super-Poissonian responses (V(t)∕M(t)>1) are indeed due to bursting rather than other causes like, for instance, fluctuations due to insufficient sampling.

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