Visual social information use in collective foraging.

Collective dynamics emerge from individual-level decisions, yet we still poorly understand the link between individual-level decision-making processes and collective outcomes in realistic physical systems. Using collective foraging to study the key trade-off between personal and social information use, we present a mechanistic, spatially-explicit agent-based model that combines individual-level evidence accumulation of personal and (visual) social cues with particle-based movement. Under idealized conditions without physical constraints, our mechanistic framework reproduces findings from established probabilistic models, but explains how individual-level decision processes generate collective outcomes in a bottom-up way. In clustered environments, groups performed best if agents reacted strongly to social information, while in uniform environments, individualistic search was most beneficial. Incorporating different real-world physical and perceptual constraints profoundly shaped collective performance, and could even buffer maladaptive herding by facilitating self-organized exploration. Our study uncovers the mechanisms linking individual cognition to collective outcomes in human and animal foraging and paves the way for decentralized robotic applications.

Collective incentives reduce over-exploitation of social information in unconstrained human groups.

Collective dynamics emerge from countless individual decisions. Yet, we poorly understand the processes governing dynamically-interacting individuals in human collectives under realistic conditions. We present a naturalistic immersive-reality experiment where groups of participants searched for rewards in different environments, studying how individuals weigh personal and social information and how this shapes individual and collective outcomes. Capturing high-resolution visual-spatial data, behavioral analyses revealed individual-level gains-but group-level losses-of high social information use and spatial proximity in environments with concentrated (vs. distributed) resources. Incentivizing participants at the group (vs. individual) level facilitated adaptation to concentrated environments, buffering apparently excessive scrounging. To infer discrete choices from unconstrained interactions and uncover the underlying decision mechanisms, we developed an unsupervised Social Hidden Markov Decision model. Computational results showed that participants were more sensitive to social information in concentrated environments frequently switching to a social relocation state where they approach successful group members. Group-level incentives reduced participants' overall responsiveness to social information and promoted higher selectivity over time. Finally, mapping group-level spatio-temporal dynamics through time-lagged regressions revealed a collective exploration-exploitation trade-off across different timescales. Our study unravels the processes linking individual-level strategies to emerging collective dynamics, and provides tools to investigate decision-making in freely-interacting collectives.

Functional Synaptic Architecture of Callosal Inputs in Mouse Primary Visual Cortex.

Callosal projections are thought to play a critical role in coordinating neural activity between the cerebral hemispheres in placental mammals, but the rules that govern the arrangement of callosal synapses on the dendrites of their target neurons remain poorly understood. Here we describe a high-throughput method to map the functional organization of callosal connectivity by combining in vivo 3D random-access two-photon calcium imaging of the dendritic spines of single V1 neurons with optogenetic stimulation of the presynaptic neural population in the contralateral hemisphere. We find that callosal-recipient spines are more likely to cluster with non-callosal-recipient spines with similar orientation preference. These observations, based on optogenetic stimulation, were confirmed by direct anatomical visualization of callosal synaptic connections using post hoc expansion microscopy. Our results demonstrate, for the first time, that functional synaptic clustering in a short dendritic segment could play a role in integrating distinct neuronal circuits.

[Fast three-dimensional two-photon scanning methods for studying neuronal physiology on cellular and network level]. / Háromdimenziós, gyors, kétfoton-pásztázó eljárások sejt- és hálózatszintu idegsejtvizsgálatokhoz.

Erratum to the article published on December 27th 2015 in Issue 52 of Orvosi Hetilap [Orv. Hetil., 2015, 156(52), 2120-2126, DOI: 10.1556/650.2015.30329]. The name of Dávid Mezey was not correctly typed. The corresponding author asked for the following correction to be published.

[Fast three-dimensional two-photon scanning methods for studying neuronal physiology on cellular and network level]. / Háromdimenziós, gyors, kétfoton-pásztázó eljárások sejt- és hálózatszintu idegsejtvizsgálatokhoz.

INTRODUCTION: Two-photon microscopy is the ideal tool to study how signals are processed in the functional brain tissue. However, early raster scanning strategies were inadequate to record fast 3D events like action potentials. AIM: The aim of the authors was to record various neuronal activity patterns with high signal-to-noise ratio in an optical manner. METHOD: Authors developed new data acquisition methods and microscope hardware. RESULTS: Multiple Line Scanning enables the experimenter to select multiple regions of interests, doing this not just increases repetition speed, but also the signal-to-noise ratio of the fluorescence transients. On the same principle, an acousto-optical deflector based 3D scanning microscope has been developed with a sub-millisecond temporal resolution and a millimeter z-scanning range. Its usability is demonstrated by obtaining 3D optical recordings of action potential backpropagation in several hundred micrometers long neuronal processes of single neurons and by 3D random-access scanning of Ca(2+) transients in hundreds of neurons in the mouse visual cortex. CONCLUSIONS: Region of interest scanning enables high signal-to-noise ratio and repetition speed, while keeping good depth penetration of the two-photon microscopes.