Abstract
SummaryDendrites integrate inputs nonlinearly, but it is unclear how these nonlinearities contribute to the overall input-output transformation of single neurons. We developed statistically principled methods using a hierarchical cascade of linear-nonlinear subunits (hLN) to model the dynamically evolving somatic response of neurons receiving complex, in vivo-like spatiotemporal synaptic input patterns. We used the hLN to predict the somatic membrane potential of an in vivo-validated detailed biophysical model of a L2/3 pyramidal cell. Linear input integration with a single global dendritic nonlinearity achieved above 90% prediction accuracy. A novel hLN motif, input multiplexing into parallel processing channels, could improve predictions as much as conventionally used additional layers of local nonlinearities. We obtained similar results in two other cell types. This approach provides a data-driven characterization of a key component of cortical circuit computations: the input-output transformation of neurons during in vivo-like conditions.
Highlights
Cortical neurons receive and integrate thousands of synaptic inputs within their dendritic tree to produce action potential output
Responses to Simple Stimuli Do Not Predict Responses to Complex Stimulation Patterns To illustrate the potential shortcomings of the most common approach for characterizing dendritic integration (Polsky et al, 2004; Losonczy and Magee, 2006; Branco et al, 2010; Abrahamsson et al, 2012; Makara and Magee, 2013), we used a previously validated multicompartmental biophysical model of a L2/3 cortical pyramidal cell (Smith et al, 2013) and recorded the somatic membrane potential response while stimulating the cell with inputs that were either similar to those typically used in in vitro experiments or resembled naturalistic patterns expected to emerge in vivo
As for the previous two cell types, we found that > 90% variance of the somatic response of the CA1 cell was captured by a hierarchical cascade of linear-nonlinear subunits (hLN) model including a single subunit with a global nonlinearity, though a 2-layer (5-subunit) hLN model significantly outperformed the 1-layer model, achieving above 95% accuracy
Summary
Cortical neurons receive and integrate thousands of synaptic inputs within their dendritic tree to produce action potential output. Recent experimental work has demonstrated that active dendritic conductances can substantially contribute to neuronal output in vivo (Xu et al, 2012; Lavzin et al, 2012; Palmer et al, 2014; Bittner et al, 2015; Takahashi et al, 2016), but it remains unclear how these active conductances change the neuronal inputoutput transformation In principle they could produce a qualitative change (e.g., from linear to supralinear; Poirazi et al, 2003b; Polsky et al, 2004; Branco and Ha€usser 2011; Makara and Magee 2013), or they could change quantitatively the relative contributions of different synapses (Cash and Yuste, 1998; Magee, 2000; Ha€usser, 2001), leaving the neuron’s global computation unaffected. Understanding the role of dendritic integration mechanisms in single-neuron computations requires both technical advances that allow experimental measurements of the spatiotemporal dynamics of synaptic activation across entire dendritic trees in vivo (Jia et al, 2010; Scholl et al, 2017) and new analysis methods for describing and quantifying dendritic and single-neuron computations
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