Subthreshold repertoire and threshold dynamics of midbrain dopamine neuron firingin vivo

Abstract

The firing pattern of ventral midbrain dopamine neurons is controlled by afferent and intrinsic activity to generate prediction error signals that are essential for reward-based learning. Given the absence of intracellular in vivo recordings in the last three decades, the subthreshold membrane potential events that cause changes in dopamine neuron firing patterns remain unknown. By establishing stable in vivo whole-cell recordings of >100 spontaneously active midbrain dopamine neurons in anaesthetized mice, we identified the repertoire of subthreshold membrane potential signatures associated with distinct in vivo firing patterns. We demonstrate that in vivo activity of dopamine neurons deviates from a single spike pacemaker pattern by eliciting transient increases in firing rate generated by at least two diametrically opposing biophysical mechanisms: a transient depolarization resulting in high frequency plateau bursts associated with a reactive depolarizing shift in action potential threshold, and a prolonged hyperpolarization preceding slower rebound bursts characterized by a predictive hyperpolarizing shift in action potential threshold. Our findings therefore illustrate a framework for the biophysical implementation of prediction error coding in dopamine neurons by tuning action potential threshold dynamics.

Distributional Reinforcement Learning in the Brain

“…One way to create such a gradient would be through inhomogeneous projections of inputs generating excitatory and inhibitory responses in dopamine neurons, as is the case for input from RMTg [43,44]. There is additional topographic variability in the intrinsic membrane properties of dopamine neurons, particularly in their response to hyperpolarizing current, that is hypothesized to render them differentially sensitive to positive and negative RPEs [45], adding yet another layer of diversity that could support distributional RL.…”

A General LSTM-based Deep Learning Method for Estimating Neuronal Models and Inferring Neural Circuitry

“…Subthreshold dynamics are considered as a representation of background and input activity statistics(Clay & Shrier, 1999; Hillenbrand, 2002). Subthreshold dynamics have been pointed out to be important for the neural system in terms of firing pattern association(Otomo et al, 2020), information integration(Ratté et al, 2015), plasticity(Latorre et al, 2016), and they also play an important role in neural networks and neural circuits(Ness et al, 2016; Wright & Wessel, 2017). The discoveries based on GNE also showed that subthreshold dynamics are extremely important in identifying neuronal models, which might open a door for further investigation about the functional role of subthreshold dynamics for single neurons or neural circuits.…”

“…Subthreshold dynamics are considered as a representation of background and input activity statistics (Clay & Shrier, 1999;Hillenbrand, 2002). Subthreshold dynamics have been pointed out to be important for the neural system in terms of firing pattern association (Otomo et al, 2020), information integration (Ratté et al, 2015),…”

Inactivation mode of sodium channels defines the different maximal firing rates of conventional versus atypical midbrain dopamine neurons

“…DA VTA neurons projecting to the medial shell of nucleus accumbens displayed atypical features (small sag, long rebound delay) while DA VTA/SNc neurons projecting to the lateral shell of nucleus accumbens possessed conventional features (large sag, short delay), both in accordance with our previous data [ 10 ]. However, using more physiological low calcium buffering patch-pipette solutions (0.1 mM EGTA), which we recently also evaluated in vivo [ 47 ] for both atypical and atypical DA neurons, we realized that spontaneous pacemaker frequencies were lower in atypical DA neurons (about 2 Hz), while their other signature properties such as low sag amplitude, long rebound delay and–most importantly in the context of this study–dynamic range of firing upon current injection were preserved. In contrast, pacemaker frequencies of conventional DA neurons were in the same range compared to our previous studies.…”

“…In vivo pacemaking is preserved in most identified dopamine neurons under isoflurane anesthesia [ 47 ] as well as in awake behaving animals [ 55 ]. Dopamine neurons are not simply leaky integrators processing their synaptic inputs to determine threshold crossings; instead, the intrinsic properties of dopamine neurons critically shape their patterns of electrical activity in vivo .…”