Identifying and Tracking Simulated Synaptic Inputs from Neuronal Firing: Insights from In Vitro Experiments

Accurately describing synaptic interactions between neurons and how interactions change over time are key challenges for systems neuroscience. Although intracellular electrophysiology is a powerful tool for studying synaptic integration and plasticity, it is limited by the small number of neurons that can be recorded simultaneously in vitro and by the technical difficulty of intracellular recording in vivo. One way around these difficulties may be to use large-scale extracellular recording of spike trains and apply statistical methods to model and infer functional connections between neurons. These techniques have the potential to reveal large-scale connectivity structure based on the spike timing alone. However, the interpretation of functional connectivity is often approximate, since only a small fraction of presynaptic inputs are typically observed. Here we use in vitro current injection in layer 2/3 pyramidal neurons to validate methods for inferring functional connectivity in a setting where input to the neuron is controlled. In experiments with partially-defined input, we inject a single simulated input with known amplitude on a background of fluctuating noise. In a fully-defined input paradigm, we then control the synaptic weights and timing of many simulated presynaptic neurons. By analyzing the firing of neurons in response to these artificial inputs, we ask 1) How does functional connectivity inferred from spikes relate to simulated synaptic input? and 2) What are the limitations of connectivity inference? We find that individual current-based synaptic inputs are detectable over a broad range of amplitudes and conditions. Detectability depends on input amplitude and output firing rate, and excitatory inputs are detected more readily than inhibitory. Moreover, as we model increasing numbers of presynaptic inputs, we are able to estimate connection strengths more accurately and detect the presence of connections more quickly. These results illustrate the possibilities and outline the limits of inferring synaptic input from spikes.Author Summary: Synapses play a central role in neural information processing – weighting individual inputs in different ways allows neurons to perform a range of computations, and the changing of synaptic weights over time allows learning and recovery from injury. Intracellular recordings provide the most detailed view of the properties and dynamics of individual synapses, but studying many synapses simultaneously during natural behavior is not feasible with current methods. In contrast, extracellular recordings allow many neurons to be observed simultaneously, but the details of their synaptic interactions have to be inferred from spiking alone. By modeling how spikes from one neuron, statistically, affect the spiking of another neuron, statistical inference methods can reveal “functional” connections between neurons. Here we examine these methods using neuronal spiking evoked by intracellular injection of a defined artificial current that simulates input from a single presynaptic neuron or a large population of presynaptic neurons. We study how well functional connectivity methods are able to reconstruct the simulated inputs, and assess the validity and limitations of functional connectivity inference. We find that, with a sufficient amount of data, accurate inference is often possible, and can become more accurate as more of the presynaptic inputs are observed.