T-type calcium channels cause bursts of spikes in motor but not sensory thalamic neurons during mimicry of natural patterns of synaptic input

Differential recruitment of ventral pallidal e-types by behaviorally salient stimuli during Pavlovian conditioning

The ventral pallidum (VP) is interfacing striatopallidal and limbic circuits, conveying information about salience and valence crucial to adjusting behavior. However, how VP neuron populations with distinct electrophysiological properties (e-types) represent these variables is not fully understood. Therefore, we trained mice on probabilistic Pavlovian conditioning while recording the activity of VP neurons. Many VP neurons responded to punishment (54%), reward (48%), and outcome-predicting auditory stimuli (32%), increasingly differentiating distinct outcome probabilities through learning. We identified e-types based on the presence of bursts or fast rhythmic discharges and found that non-bursting, non-rhythmic neurons were the most sensitive to reward and punishment. Some neurons exhibited distinct responses of their bursts and single spikes, suggesting a multiplexed coding scheme in the VP. Finally, we demonstrate synchronously firing neuron assemblies, particularly responsive to reinforcing stimuli. These results suggest that electrophysiologically defined e-types of the VP differentially participate in transmitting reinforcement signals during learning.

Thalamic regulation of switching between cortical representations enables cognitive flexibility

Interactions between the prefrontal cortex (PFC) and mediodorsal thalamus (MD) are critical for cognitive flexibility, yet the underlying computations are unknown. To investigate fronto-thalamic substrates of cognitive flexibility, we developed a behavioral task, where mice switched between different sets of learned cues that guided attention towards either visual or auditory targets. We found that PFC responses reflected both the individual cues and their meaning as task rules, indicating a hierarchical cue-to-rule transformation. Conversely, MD responses reflected the statistical regularity of cue presentation, and were required for switching between such experimentally-specified cueing contexts. A subset of these thalamic responses sustained context-relevant PFC representations, while another suppressed the context-irrelevant ones. Through modeling and experimental validation, we find that thalamic-mediated suppression may not only reduce PFC representational interference but could also preserve unused cortical traces for future use. Overall, our study provides a computational foundation for thalamic engagement in cognitive flexibility.

Theory of optimal balance predicts and explains the amplitude and decay time of synaptic inhibition

Synaptic inhibition counterbalances excitation, but it is not known what constitutes optimal inhibition. We previously proposed that perfect balance is achieved when the peak of an excitatory postsynaptic potential (EPSP) is exactly at spike threshold, so that the slightest variation in excitation determines whether a spike is generated. Using simulations, we show that the optimal inhibitory postsynaptic conductance (IPSG) increases in amplitude and decay rate as synaptic excitation increases from 1 to 800 Hz. As further proposed by theory, we show that optimal IPSG parameters can be learned through anti-Hebbian rules. Finally, we compare our theoretical optima to published experimental data from 21 types of neurons, in which rates of synaptic excitation and IPSG decay times vary by factors of about 100 (5–600 Hz) and 50 (1–50 ms), respectively. From an infinite range of possible decay times, theory predicted experimental decay times within less than a factor of 2. Across a distinct set of 15 types of neuron recorded in vivo, theory predicted the amplitude of synaptic inhibition within a factor of 1.7. Thus, the theory can explain biophysical quantities from first principles.

Inhibition and excitation are counterbalanced at synapses, but the conditions that constitute optimal balance are not known. Here the authors show through modelling that the properties of synaptic inhibition are fine-tuned to maintain an optimal balance in which peak excitation reaches precisely to spike threshold.