Overcoming the space clamp effect: Reliable recovery of local and effective synaptic conductances of neurons

Abstract

Neurons process information by integrating thousands of synaptic inputs along their dendrites. Understanding the computational principles underlying neuronal information processing requires a reliable measure of synaptic conductance dynamics that accurately represents the input sources before signal integration and processing. Prevailing approaches to measuring synaptic conductances typically employ a voltage clamp at the soma of a neuron and assume the neuron as an isopotential point when processing electrical signals. However, owing to the presence of the well-known space clamp effect, the measurement of synaptic conductances through these methods often leads to significant errors, impeding the elucidation of dendritic signal features and subsequent signal integration processes. To address this issue, here we first develop a two-step clamp method at the soma that separately recovers the mean and time constant information of local synaptic conductance on the dendrite with high accuracy when a neuron receives a single synaptic input. Furthermore, under in vivo conditions of multiple synaptic inputs, we propose an intercept method to extract effective net excitatory and inhibitory synaptic conductances from measurements of synaptic currents at the soma. Both methods are grounded in mathematical perturbation analyses of a conductance-based passive cable model and are validated across multiple biologically detailed multicompartment neuron models with active channels, including Purkinje neuron, pyramidal neuron, and fast-spiking interneuron. Results demonstrate that our methods effectively circumvent the space clamp effect, offering reliable means to assess the role of measured conductances and synaptic activity in neuronal information processing.