Neural circuits for evidence accumulation and decision making in larval zebrafish

December 2, 2019

Bahl A, Engert F

To make appropriate decisions, animals need to accumulate sensory evidence. Simple integrator models can explain many aspects of such behavior, but how the underlying computations are mechanistically implemented in the brain remains poorly understood. Here we approach this problem by adapting the random-dot motion discrimination paradigm, classically used in primate studies, to larval zebrafish. Using their innate optomotor response as a measure of decision making, we find that larval zebrafish accumulate and remember motion evidence over many seconds and that the behavior is in close agreement with a bounded leaky integrator model. Through the use of brain-wide functional imaging, we identify three neuronal clusters in the anterior hindbrain that are well suited to execute the underlying computations. By relating the dynamics within these structures to individual behavioral choices, we propose a biophysically plausible circuit arrangement in which an evidence integrator competes against a dynamic decision threshold to activate a downstream motor command.

Nat Neurosci

Single-Cell Profiles of Retinal Ganglion Cells Differing in Resilience to Injury Reveal Neuroprotective Genes

November 26, 2019

Nicholas M.Tran, Karthik Shekhar, Irene E.Whitney, Anne Jacobi, Inbal Benhar, Guosong Hong, Wenjun Yan, Xian Adiconis, McKinzie E. Arnold, Jung Min Lee, Joshua Z. Levin, Dingchang Lin, Chen Wang, Charles M. Lieber, Aviv Regev, Zhigang He, Joshua R. Sanes 

Neuronal injury is characterized by the selective death of specific types of neurons, but the reasons are poorly understood. In particular, Joshua Sanes, Zhigang He, and colleagues earlier found that different retinal ganglion cell (RGC) types differ in their robustness to axonal damage. Now, by sequencing the genes expressed in tens of thousands of individual RGCs, Sanes and colleagues (Tran et al., Neuron 2019) used the correlation of differential gene expression with injury response to systematically identify neuroprotective genes. By manipulating some of these molecular targets, they point to potential therapeutic targets.


Learning optimal decisions with confidence

November 15, 2019

Drugowitsch J, Mendonça AG, Mainen ZF, Pouget A

Diffusion decision models (DDMs) are immensely successful models for decision making under uncertainty and time pressure. In the context of perceptual decision making, these models typically start with two input units, organized in a neuron-antineuron pair. In contrast, in the brain, sensory inputs are encoded through the activity of large neuronal populations. Moreover, while DDMs are wired by hand, the nervous system must learn the weights of the network through trial and error. There is currently no normative theory of learning in DDMs and therefore no theory of how decision makers could learn to make optimal decisions in this context. Here, we derive such a rule for learning a near-optimal linear combination of DDM inputs based on trial-by-trial feedback. The rule is Bayesian in the sense that it learns not only the mean of the weights but also the uncertainty around this mean in the form of a covariance matrix. In this rule, the rate of learning is proportional (respectively, inversely proportional) to confidence for incorrect (respectively, correct) decisions. Furthermore, we show that, in volatile environments, the rule predicts a bias toward repeating the same choice after correct decisions, with a bias strength that is modulated by the previous choice's difficulty. Finally, we extend our learning rule to cases for which one of the choices is more likely a priori, which provides insights into how such biases modulate the mechanisms leading to optimal decisions in diffusion models.

Veil-of-ignorance reasoning favors the greater good

November 12, 2019

Huang K, Greene JD, Bazerman M

The "veil of ignorance" is a moral reasoning device designed to promote impartial decision making by denying decision makers access to potentially biasing information about who will benefit most or least from the available options. Veil-of-ignorance reasoning was originally applied by philosophers and economists to foundational questions concerning the overall organization of society. Here, we apply veil-of-ignorance reasoning in a more focused way to specific moral dilemmas, all of which involve a tension between the greater good and competing moral concerns. Across 7 experiments (n = 6,261), 4 preregistered, we find that veil-of-ignorance reasoning favors the greater good. Participants first engaged in veil-of-ignorance reasoning about a specific dilemma, asking themselves what they would want if they did not know who among those affected they would be. Participants then responded to a more conventional version of the same dilemma with a moral judgment, a policy preference, or an economic choice. Participants who first engaged in veil-of-ignorance reasoning subsequently made more utilitarian choices in response to a classic philosophical dilemma, a medical dilemma, a real donation decision between a more vs. less effective charity, and a policy decision concerning the social dilemma of autonomous vehicles. These effects depend on the impartial thinking induced by veil-of-ignorance reasoning and cannot be explained by anchoring, probabilistic reasoning, or generic perspective taking. These studies indicate that veil-of-ignorance reasoning may be a useful tool for decision makers who wish to make more impartial and/or socially beneficial choices.

Joshua Sanes Wins Cowan Award at 2019 SfN Meeting

October 29, 2019

The W. Maxwell Cowan Awards was established by John Wiley and Sons in 2004 to honor two past Editors-in-Chief of The Journal of Comparative Neurology (JCN). The Award is given by a jury, consisting of the Editors of the Journal of Comparative Neurology and the Officers of the Cajal Club, every other year at the Cajal Club Annual Meeting at the Society for Neuroscience convention.

Previous winners have included:

2005 Carla J. Shatz
2007 Andres Lumsden
2009 Thomas M. Jessell
2011 Marc Tessier-Lavigne
2011 Special Cowan Lifetime Achievement Award: Edward G. (Ted) Jones
2013 Pasko Rakic
2015 Mary Beth Hatten
2017 Fred H. Gage


Dr Sanes is interested in the molecular mechanisms and structural features that regulate synapse formation, addressing how Information processing in the brain occurs at synapses, and how abnormal synapse formation results in neurological and psychiatric disorders. He has developed an interdisciplinary approach that combines molecular biology, chemistry, genetics, engineering, and psychology to investigate systems-level questions in neuroscience with a focus on the assembly and function of neural circuits in the retina and on synapse formation, maturation, and remodeling at the skeletal neuromuscular junction. To understand how these circuits form, Dr Sanes uses transgenic methods to identify retinal cell types and map their connections, as well as genetic methods to manipulate molecules that mediate the connectivity of these neurons, to assess the consequences of modifying the molecular determinants of these neuronal network at the structural and functional levels. Finally, Dr. Sanes is also studying the retina to explore the issue of neuronal classification and nomenclature. This remains a complicated problem for neuroscience that is far from being resolved. He uses high-throughput single-cell RNA sequencing to profile tens of thousands of retinal cells, with bioinformatics methods to categorize them. This led to the emergence of a mouse “retinome”, which can serve as a framework for neuronal classification, not solely in the mouse but extending to other species, such as primates, whose retinal structure differs significantly from that of rodents, and consequently to the analysis of gene expression in animal models of blindness and retinopathies with a cellular level of specificity. The approaches developed by Dr. Sanes and his collaborators have been highly influential as they enabled studies of development, functional plasticity, and vulnerability to disease states of larger complex structures like the cerebral cortex, as well as less accessible brain regions.