Abstract
Understanding of how neurons transform fluctuations of membrane potential, reflecting input activity, into spike responses, which communicate the ultimate results of single-neuron computation, is one of the central challenges for cellular and computational neuroscience. To study this transformation under controlled conditions, previous work has used a signal immersed in noise paradigm where neurons are injected with a current consisting of fluctuating noise that mimics on-going synaptic activity and a systematic signal whose transmission is studied. One limitation of this established paradigm is that it is designed to examine the encoding of only one signal under a specific, repeated condition. As a result, characterizing how encoding depends on neuronal properties, signal parameters, and the interaction of multiple inputs is cumbersome. Here we introduce a novel fully-defined signal mixture paradigm, which allows us to overcome these problems. In this paradigm, current for injection is synthetized as a sum of artificial postsynaptic currents (PSCs) resulting from the activity of a large population of model presynaptic neurons. PSCs from any presynaptic neuron(s) can be now considered as “signal”, while the sum of all other inputs is considered as “noise”. This allows us to study the encoding of a large number of different signals in a single experiment, thus dramatically increasing the throughput of data acquisition. Using this novel paradigm, we characterize the detection of excitatory and inhibitory PSCs from neuronal spike responses over a wide range of amplitudes and firing-rates. We show, that for moderately-sized neuronal populations the detectability of individual inputs is higher for excitatory than for inhibitory inputs during the 2–5 ms following PSC onset, but becomes comparable after 7–8 ms. This transient imbalance of sensitivity in favor of excitation may enhance propagation of balanced signals through neuronal networks. Finally, we discuss several open questions that this novel high-throughput paradigm may address.
Highlights
Developing a mechanistic understanding of how neurons transform fluctuations in membrane potential, driven by synaptic inputs, into spike responses, which communicate the ultimate results of single-neuron computation, is one of the central challenges for cellular and computational neuroscience
Our results reveal that changes of the firing rate of moderate-sized neuronal populations are more sensitive to excitatory than to inhibitory inputs during the 2–5 ms following postsynaptic currents (PSCs) onset
An established paradigm for studying population encoding using intracellular recording in slices is to inject in a cell a current in which a ‘‘signal’’, such as an artificial postsynaptic potential (aPSC) or sine-wave modulated current, is immersed in fluctuating ‘‘noise’’
Summary
Developing a mechanistic understanding of how neurons transform fluctuations in membrane potential, driven by synaptic inputs, into spike responses, which communicate the ultimate results of single-neuron computation, is one of the central challenges for cellular and computational neuroscience. To study this transformation under controlled conditions, a signal immersed in noise paradigm has been introduced [1,2]. Action potentials generated in response to repeated current injection can provide a precise measure of the average output of the neuron in response to a specific input signal. The signal immersed in noise paradigm has been successfully applied to study signal propagation in feedforward networks [1,3], the speed of population responses to steplike changes of the input [2,4,5,6] and characterization of the frequency transfer function of neuronal population responses [7,8,9,4,5]
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