Active dendrites enable strong but sparse inputs to determine orientation selectivity

The dendrites of neocortical pyramidal neurons are excitable. However, it is unknown how synaptic inputs engage nonlinear dendritic mechanisms during sensory processing in vivo, and how they in turn influence action potential output. Here, we provide a quantitative account of the relationship between synaptic inputs, nonlinear dendritic events, and action potential output. We developed a detailed pyramidal neuron model constrained by in vivo dendritic recordings. We drive this model with realistic input patterns constrained by sensory responses measured in vivo and connectivity measured in vitro. We show mechanistically that under realistic conditions, dendritic Na⁺ and NMDA spikes are the major determinants of neuronal output in vivo. We demonstrate that these dendritic spikes can be triggered by a surprisingly small number of strong synaptic inputs, in some cases even by single synapses. We predict that dendritic excitability allows the 1% strongest synaptic inputs of a neuron to control the tuning of its output. Active dendrites therefore allow smaller subcircuits consisting of only a few strongly connected neurons to achieve selectivity for specific sensory features.

There is longstanding evidence from in vitro experiments that dendrites of mammalian neurons are electrically excitable (1, 2), and theoretical work has demonstrated that these active properties can be exploited for computations so that single neurons can perform functions that could otherwise only be performed by a network (3–7). Recently, technical breakthroughs have enabled dendritic integration to be studied in vivo using both imaging and electrophysiological techniques (8, 9). These experiments have revealed that the integration of synaptic events in vivo can be highly nonlinear and that this process influences the response properties of single neurons and neuronal populations in vivo (10–16). For example, patch-clamp recordings from dendrites in mouse primary visual cortex (V1) have demonstrated that dendritic spikes are triggered by visual input and that they may contribute to the orientation selectivity of somatic membrane potential (17). However, important mechanistic questions are still unanswered. How many synaptic inputs must be locally coactive on a dendrite to recruit dendritic spikes? What is the contribution of individual dendritic spikes to somatic action potential (AP) output and its orientation selectivity? Moreover, we do not understand how the answers to these questions depend on the type of dendritic spike. Finally, how do active dendrites, by supporting dendritic spikes, influence which synaptic inputs control AP output and its tuning?These issues are extremely challenging to address experimentally. We have therefore taken a modeling approach, constrained by in vitro and in vivo experimental data, in order to provide a quantitative understanding of the relationship between synaptic input, dendritic spikes, and AP output during sensory processing in V1. We constructed a detailed active model of a layer (L) 2/3 pyramidal neuron in mouse V1 and combined this with a model of the presynaptic inputs it receives during visual stimulation with drifting gratings in vivo (17–21).Our model reproduces key features of the experimental data on dendritic and somatic responses to visual stimulation as observed in vivo and allows us to identify the synaptic inputs that trigger dendritic Na⁺ spikes and NMDA spikes. We also provide a quantitative explanation for how these dendritic spikes determine neuronal output in vivo. Our results show that dendritic spikes can be triggered by a surprisingly small number of synaptic inputs—in some cases even by single synapses. We also find that during sensory processing, already few dendritic spikes are effective at driving somatic output. Overall, this strategy allows a remarkably small number of strong synaptic inputs to dominate neural output, which may reduce the number of neurons required to represent a given sensory feature.