Thalamic neuron models encode stimulus information by burst-size modulation

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.

Multiplexed Spike Coding and Adaptation in the Thalamus

“…3D). Previous studies of model bursting neurons suggested that burst size may encode a variety of stimulus features (Elijah et al, 2015, Kepecs and Lisman, 2003, Kepecs et al, 2002) but this possibility had not been tested experimentally on at the single neuron level. Our approach recalls the theoretical work of (Kepecs and Lisman, 2003) using covariance analysis.…”

“…This greatly increases the information content of thalamic bursts beyond “AP-count” codes where stimulus encoding is limited to the size of the burst (Lesica and Stanley, 2004, Gaudry and Reinagel, 2008, Elijah et al, 2015, Butts et al, 2010). Existing analyses of precise AP times within visually evoked thalamic bursts have not explored the role of specific stimulus selectivity within bursts, demonstrating rather that shorter initial ISIs in a burst are correlated with larger burst AP count (Gaudry and Reinagel, 2008) or duration (Butts et al, 2010), reflecting the fact that larger LTSs drive more APs separated by shorter intervals.…”

The transcription factor Shox2 shapes thalamocortical neuron firing and synaptic properties

“…The functions of the burst and tonic firing properties of thalamocortical neurons are still under investigation. Tonic spike firing mode is thought to contribute to reliable information transfer during perceptive states that conveys sensory information to cortex 89,90 . Burst firing mode may allow lack of responsiveness to sensory input during sleep and unconsciousness such as during an absence seizure [91][92][93][94] .…”

Phase analysis method for burst onset prediction

“…. , [17,21] Hz, thus obtaining the synaptic input currents I (t). This first step is necessary to characterize the chosen neuron model through input signals with frequency contents typical for LFPs.…”

“…A recent interesting modeling study [16] considers this locking and suggests that the way the LFP phase is converted into firing is through input-phase modulation of burst onset. The ability of bursts to encode the input phase is moreover proved to be qualitatively robust to the specific choice of bursting model [17].…”