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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

Diagrams of the MC (A) and IFB (B) thalamic models. The net synaptic input is represented by an Ornstein-Uhlenbeck (OU) process (bottom), here termed the stimulus. The models have no spatial structure. The conductances governing the evolution of the membrane potential (top traces) are marked.
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Figure 1: Diagrams of the MC (A) and IFB (B) thalamic models. The net synaptic input is represented by an Ornstein-Uhlenbeck (OU) process (bottom), here termed the stimulus. The models have no spatial structure. The conductances governing the evolution of the membrane potential (top traces) are marked.

Mentions: To simulate thalamic neuron responses we employ two single compartment models: the multi-conductance (MC) and the integrate and fire or burst (IFB) models. The two models contain different levels of biological detail. Figure 1 shows a diagram of the MC (A) and the IFB (B) models, each driven by an Ornstein-Uhlenbeck (OU) stimulus, and producing membrane voltage responses.


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

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

Diagrams of the MC (A) and IFB (B) thalamic models. The net synaptic input is represented by an Ornstein-Uhlenbeck (OU) process (bottom), here termed the stimulus. The models have no spatial structure. The conductances governing the evolution of the membrane potential (top traces) are marked.
© Copyright Policy
Related In: Results  -  Collection

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

Figure 1: Diagrams of the MC (A) and IFB (B) thalamic models. The net synaptic input is represented by an Ornstein-Uhlenbeck (OU) process (bottom), here termed the stimulus. The models have no spatial structure. The conductances governing the evolution of the membrane potential (top traces) are marked.
Mentions: To simulate thalamic neuron responses we employ two single compartment models: the multi-conductance (MC) and the integrate and fire or burst (IFB) models. The two models contain different levels of biological detail. Figure 1 shows a diagram of the MC (A) and the IFB (B) models, each driven by an Ornstein-Uhlenbeck (OU) stimulus, and producing membrane voltage responses.

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