How does the mammalian retina detect motion? This classic problem in visual neuroscience has remained unsolved for 50 years. In search of clues, we reconstructed Off-type starburst amacrine cells (SACs) and bipolar cells (BCs) in serial electron microscopic images with help from EyeWire, an online community of “citizen neuroscientists.” Based on quantitative analyses of contact area and branch depth in the retina, we found evidence that one BC type prefers to wire with a SAC dendrite near the SAC soma, while another BC type prefers to wire far from the soma. The near type is known to lag the far type in time of visual response. A mathematical model shows how such “space-time wiring specificity” could endow SAC dendrites with receptive fields that are oriented in space-time and therefore respond selectively to stimuli that move in the outward direction from the soma.

In pursuit of the goal to understand and eventually reproduce the diverse functions of the brain, a key challenge lies in reverse engineering the peculiar biology-based “technology” that underlies the brain’s remarkable ability to process and store information. The basic building block of the nervous system is the nerve cell, or “neuron,” yet after more than 100 years of neurophysiological study and 60 years of modeling, the information processing functions of individual neurons, and the parameters that allow them to engage in so many different types of computation (sensory, motor, mnemonic, executive, etc.) remain poorly understood. In this paper, we review both historical and recent findings that have led to our current understanding of the analog spatial processing capabilities of dendrites, the major input structures of neurons, with a focus on the principal cell type of the neocortex and hippocampus, the pyramidal neuron (PN). We encapsulate our current understanding of PN dendritic integration in an abstract layered model whose spatially sensitive branch-subunits compute multidimensional sigmoidal functions. Unlike the 1-D sigmoids found in conventional neural network models, multidimensional sigmoids allow the cell to implement a rich spectrum of nonlinear modulation effects directly within their dendritic trees.

Neocortical pyramidal neurons (PNs) receive thousands of excitatory synaptic contacts on their basal dendrites. Some act as classical driver inputs while others are thought to modulate PN responses based on sensory or behavioral context, but the biophysical mechanisms that mediate classical-contextual interactions in these dendrites remain poorly understood. We hypothesized that if two excitatory pathways bias their synaptic projections towards proximal vs. distal ends of the basal branches, the very different local spike thresholds and attenuation factors for inputs near and far from the soma might provide the basis for a classical-contextual functional asymmetry. Supporting this possibility, we found both in compartmental models and electrophysiological recordings in brain slices that the responses of basal dendrites to spatially separated inputs are indeed strongly asymmetric. Distal excitation lowers the local spike threshold for more proximal inputs, while having little effect on peak responses at the soma. In contrast, proximal excitation lowers the threshold, but also substantially increases the gain of distally-driven responses. Our findings support the view that PN basal dendrites possess significant analog computing capabilities, and suggest that the diverse forms of nonlinear response modulation seen in the neocortex, including uni-modal, cross-modal, and attentional effects, could depend in part on pathway-specific biases in the spatial distribution of excitatory synaptic contacts onto PN basal dendritic arbors.

Pyramidal neuron (PN) dendrites compartmentalize voltage signals and can generate local spikes, which has led to the proposal that their dendrites act as independent computational subunits within a multilayered processing scheme. However, when a PN is strongly activated, back-propagating action potentials (bAPs) sweeping outward from the soma synchronize dendritic membrane potentials many times per second. How PN dendrites maintain the independence of their voltage-dependent computations, despite these repeated voltage resets, remains unknown. Using a detailed compartmental model of a layer 5 PN, and an improved method for quantifying subunit independence that incorporates a more accurate model of dendritic integration, we first established that the output of each dendrite can be almost perfectly predicted by the intensity and spatial configuration of its own synaptic inputs, and is nearly invariant to the rate of bAP-mediated "cross-talk" from other dendrites over a 100-fold range. Then, through an analysis of conductance, voltage, and current waveforms within the model cell, we identify three biophysical mechanisms that together help make independent dendritic computation possible in a firing neuron, suggesting that a major subtype of neocortical neuron has been optimized for layered, compartmentalized processing under in-vivo-like spiking conditions.O n the path to understanding the diverse information processing functions of the brain, it is essential to develop simplified models of individual neurons. The classical view that a neuron collects excitatory and inhibitory influences from across its dendritic arbor and passively funnels them to a single spikegenerating zone near the soma has been challenged by the finding that dendrites generate local spikes (1-15) and can support a variety of compartmentalized computations (16-28). We previously showed that the input-output (i/o) behavior of a dendritic subtree, whose branches emanate from a main trunk or soma, can be described by a two-layer model (2LM), where the first layer consists of multiple independent dendritic "subunits" with stereotyped nonlinear i/o functions. In the second layer, corresponding to the soma, the dendritic outputs are summed and fed through the cell's somatic firing rate-current (f-I) curve (8,21,22,29) (Fig. 1A). The core assumption of the 2LM is that each dendrite's output depends only on its synaptic inputs, and is independent of the activity of other dendrites or the cell as a whole. The 2LM was previously tested in both experimental and modeling studies, outperforming passive dendrite (one-layer) models in predicting pyramidal neuron (PN) responses both to brief inputs leading to subthreshold somatic responses (8,29) and to high-frequency stimulation that produced output trains lasting hundreds of milliseconds (21,22). Notwithstanding the improved description of PN responses provided by two-layer models, a substantial fraction of the response variance remained unexplained by 2LM predictions in those previous studies (21, 22), leav...