ArticleFrontiers in computational neuroscience2015
Thalamic neuron models encode stimulus information by burst-size modulation.
Article in Frontiers in computational neuroscience, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- The dorsal lateral geniculate nucleus and the pulvinar as essential partners for visual cortical functions.Frontiers in neuroscience · 2023Review
- Corticothalamic Pathways From Layer 5: Emerging Roles in Computation and Pathology.Frontiers in neural circuits · 2021Review
- The Augmentation of Retinogeniculate Communication during Thalamic Burst Mode.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2019Article
- Temporal pattern separation in hippocampal neurons through multiplexed neural codes.PLoS computational biology · 2019Article
- Prefrontal cortex modulates firing pattern in the nucleus reuniens of the midline thalamus via distinct corticothalamic pathways.The European journal of neuroscience · 2018Article
- Information-Theoretical Analysis of the Neural Code in the Rodent Temporal Lobe.Entropy (Basel, Switzerland) · 2018Article
- Spike and burst coding in thalamocortical relay cells.PLoS computational biology · 2018Article
- Neural Coding With Bursts-Current State and Future Perspectives.Frontiers in computational neuroscience · 2018Review
- Multiplexed Spike Coding and Adaptation in the Thalamus.Cell reports · 2017Article
- Neuroelectric Tuning of Cortical Oscillations by Apical Dendrites in Loop Circuits.Frontiers in systems neuroscience · 2017Article
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3 authors.
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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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