Highlights d Some neurons in V1 reliably respond to specific stimulus parameters and some do not d Functional networks containing these tuned and untuned neurons are stimulus specific d Tuned and untuned neurons have different positions in networks d Functional networks comprised of all neurons and connections improve decoding

No abstract

A promising alternative to directly measuring the anatomical connections in a neuronal population is inferring the connections from the activity. We employ simulated spiking neuronal networks to compare and contrast commonly used inference methods that identify likely excitatory synaptic connections using statistical regularities in spike timing. We find that simple adjustments to standard algorithms improve inference accuracy: A signing procedure improves the power of unsigned mutual-information-based approaches and a correction that accounts for differences in mean and variance of background timing relationships, such as those expected to be induced by heterogeneous firing rates, increases the sensitivity of frequency-based methods. We also find that different inference methods reveal distinct subsets of the synaptic network and each method exhibits different biases in the accurate detection of reciprocity and local clustering. To correct for errors and biases specific to single inference algorithms, we combine methods into an ensemble. Ensemble predictions, generated as a linear combination of multiple inference algorithms, are more sensitive than the best individual measures alone, and are more faithful to ground-truth statistics of connectivity, mitigating biases specific to single inference methods. These weightings generalize across simulated datasets, emphasizing the potential for the broad utility of ensemble-based approaches.

Spike trains in cortical neuronal populations vary in number and timing trial-to-trial, rendering a viable single trial coding scheme for sensory information elusive. Correlations between pairs of neocortical neurons can be segmented into either sensory or noise according to their stimulus specificity. Here we show that pairs of spikes, corresponding to reliable sensory correlations in imaged populations in layer 2/3 of mouse visual cortex are particularly informative of visual stimuli. This set of spikes is sparse and exhibits comparable levels of trial-to-trial variance relative to the full spike train. Despite this, correspondence of pairs of spikes to a specific set of sensory correlations identifies spikes that carry more information per spike about the visual stimulus than the full set or any other matched set of spikes. Moreover, this sparse subset is more accurately decoded, regardless of the decoding algorithm employed. Our findings suggest that consistent pairwise correlations between neurons, rather than first-order statistical features of spike trains, may be an organizational principle of a single trial sensory coding scheme.

Understanding how circuits self-assemble starting from neuronal stem cells is a fundamental question in developmental biology. Here, we addressed how neurons from different lineages wire with each other to form a specific circuit motif. To do so, we combined developmental genetics—Twin spot MARCM, Multi-color Flip Out, permanent labeling—with circuit analysis—calcium imaging, connectomics, and network science analyses. We find many lineages are organized into temporal cohorts, which are sets of lineage-related neurons born within a tight time window, and that temporal cohort boundaries have sharp transitions in patterns of input connectivity. We identify a feed-forward circuit motif that encodes the onset of vibration stimuli. This feed-forward circuit motif is assembled by preferential connectivity between temporal cohorts from different neuronal stem cell lineages. Further, connectivity does not follow the often-cited early-to-early, late-to-late model. Instead, the feed-forward motif is formed by sequential addition of temporal cohorts, with circuit output neurons born before circuit input neurons. Further, we generate multiple new tools for the fly community. Ultimately, our data suggest that sequential addition of neurons (with outputs neurons being oldest and input neurons being youngest) could be a fundamental strategy for assembling feed-forward circuits.