Benjamin Ehret

The prefrontal cortex (PFC) plays a central role in the selection and expression of diverse social behaviors. A balance of excitation and inhibition is necessary for normal social functioning, but it remains unclear how inhibition sculpts pyramidal activity to promote specific social behaviors. To address this question, we developed a novel decision-making task in which female mice chose between interaction with a male (sociosexual stimulus) or an appetitive non-social option (milk solution). To explore the role of a task-relevant inhibitory subpopulation, we targeted neurons in the medial PFC (mPFC) expressing oxytocin receptors (OXTR neurons). Combining optogenetic inhibition of OXTR neurons with population calcium imaging of pyramidal neural activity, we found that OXTR neurons normally promote interaction with a male compared to a non-social alternative. OXTR neurons also regulate pyramidal activity, which enables a specific ensemble to represent the male option during decision-making. Computational modeling reproduced these findings through a pyramidal competition mechanism, in which regulation by OXTR neurons allows a male-representing pyramidal ensemble to effectively compete against the remaining pyramidal population to drive male choice. These results provide a candidate mechanism by which inhibition enables mPFC pyramidal activity to select for specific social behaviors and may help explain why excitation/inhibition balance is so important for social functioning.

The medial prefrontal cortex (mPFC) has been proposed to link sensory inputs and behavioral outputs to mediate the execution of learned behaviors. However, how such a link is implemented has remained unclear. To measure prefrontal neural correlates of sensory stimuli and learned behaviors, we performed population calcium imaging during a new tone-signaled active avoidance paradigm in mice. We developed an analysis approach based on dimensionality reduction and decoding that allowed us to identify interpretable task-related population activity patterns. While a large fraction of tone-evoked activity was not informative about behavior execution, we identified an activity pattern that was predictive of tone-induced avoidance actions and did not occur for spontaneous actions with similar motion kinematics. Moreover, this avoidance-specific activity differed between distinct avoidance actions learned in two consecutive tasks. Overall, our results are consistent with a model in which mPFC contributes to the selection of goal-directed actions by transforming sensory inputs into specific behavioral outputs through distributed population-level computations.

Neurons integrate from thousands of synapses whose strengths span an order of magnitude. Intriguingly, in mouse neocortex, the few ‘strong’ synapses are formed between similarly tuned cells, suggesting they determine spiking output. This raises the question of how other computational primitives, including ‘background’ activity from the many ‘weak’ synapses, short-term plasticity, and temporal factors contribute to spiking. We used paired recordings and extracellular stimulation experiments to map excitatory postsynaptic potential (EPSP) amplitudes and paired-pulse ratios of synaptic connections formed between pyramidal neurons in layer 2/3 (L2/3) of barrel cortex. While net short-term plasticity was weak, strong synaptic connections were exclusively depressing. Importantly, we found no evidence for clustering of synaptic properties on individual neurons. Instead, EPSPs and paired-pulse ratios of connections converging onto the same cells spanned the full range observed across L2/3, which critically constrains theoretical models of cortical filtering. To investigate how different computational primitives of synaptic information processing interact to shape spiking, we developed a computational model of a pyramidal neuron in the excitatory L2/3 circuitry, which was constrained by our experiments and published in vivo data. We found that strong synapses were substantially depressed during ongoing activation and their ability to evoke correlated spiking primarily depended on their high temporal synchrony and high firing rates observed in vivo. However, despite this depression, their larger EPSP amplitudes strongly amplified information transfer and responsiveness. Thus, our results contribute to a nuanced framework of how cortical neurons exploit synergies between temporal coding, synaptic properties, and noise to transform synaptic inputs into spikes.

The medial prefrontal cortex (mPFC) has been proposed to link sensory inputs and behavioral outputs to mediate the execution of learned behaviors. However, how such a link is implemented has remained unclear. To measure prefrontal neural correlates of sensory stimuli and learned behaviors, we performed population calcium imaging during a novel tone-signaled active avoidance paradigm in mice. We developed a novel analysis approach based on dimensionality reduction and decoding that allowed us to identify and isolate population activity patterns related the tone stimulus, learned avoidance actions and general motion. While tone-related activity was not informative about behavior, avoidance-related activity was predictive of upcoming avoidance actions. Moreover, avoidance-related activity distinguished between two different learned avoidance actions, consistent with a model in which mPFC contributes to the selection between different goal-directed actions. Overall, our results suggest that mPFC circuit dynamics transform sensory inputs into specific behavioral outputs through distributed population-level computations.

Neurons integrate from thousands of synapses whose strengths span an order of magnitude. Intriguingly, in mouse neocortex, the few ‘strong’ synapses are formed between similarly tuned cells, suggesting they determine neuronal spiking output. This raises the question of how other computational primitives, including ‘background’ activity from the many ‘weak’ synapses, short-term plasticity, and temporal factors contribute to spiking. We combined extracellular stimulation and whole-cell recordings in mouse barrel cortex to map excitatory postsynaptic potential (EPSP) amplitudes and paired-pulse ratios of excitatory synaptic connections converging onto individual layer 2/3 (L2/3) neurons. While net short-term plasticity was weak, connections with EPSPs > 2 mV were exclusively depressing. There was no evidence for clustering of synaptic properties on individual neurons. Instead, EPSPs and paired-pulse ratios of connections converging onto the same cells spanned the full range observed across L2/3, which critically constrains theoretical models of cortical filtering. To investigate how different computational primitives of synaptic information processing interact to shape spiking, we developed a computational model of a pyramidal neuron in the rodent L2/3 circuitry, which was constrained by our own experiments and published in vivo data. We found that the ability of strong inputs to evoke spiking depended on their high temporal synchrony and high firing rates observed in vivo and on synaptic background activity – and not primarily on synaptic strength, which further amplified information transfer. Our results provide a framework of how cortical neurons exploit complex synergies between temporal coding, synaptic properties, and noise to transform synaptic inputs into output firing.