Identifying and tracking simulated synaptic inputs from neuronal firing: insights from in vitro experiments

• Published in: PLoS computational biology Vol. 11; no. 3; p. e1004167
• Main Authors: Volgushev, Maxim, Ilin, Vladimir, Stevenson, Ian H
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01-03-2015; Public Library of Science (PLoS)
• Subjects: Action Potentials - physiology; Anatomy & physiology; Animals; Experiments; Identification and classification; Information processing; Models, Neurological; Neural circuitry; Neurons; Neurons - physiology; Physiological aspects; Rats; Rats, Wistar; Synapses - physiology; Synaptic transmission; Visual Cortex - cytology; Visual Cortex - physiology
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1004167

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.