Faculty Opinions recommendation of Neural ensembles in CA3 transiently encode paths forward of the animal at a decision point.

2007 ◽  
Author(s):  
Edvard I Moser
Keyword(s):  
Decision Point ◽  
Neural Ensembles

Perspectives on Ethics of AI

2020 ◽  
pp. 419-441
Author(s):  
Benjamin Kuipers
Keyword(s):  
Autonomous Vehicles ◽  
Intelligent Systems ◽  
Feedback Mechanism ◽  
Computational Approach ◽  
Human Society ◽  
Decision Point ◽  
Free Riders ◽  
Ethical Knowledge ◽  
Distributed Intelligent Systems ◽  
Lesser Evil

This chapter describes a computational view of the function of ethics in human society and discusses its application to three diverse examples. First, autonomous vehicles are individually embodied intelligent systems that act as members of society. The ethical knowledge needed by such an agent is not how to choose the lesser evil when confronted by a Deadly Dilemma, but how to recognize the upstream decision point that makes it possible to avoid the Deadly Dilemma entirely. Second, disembodied distributed intelligent systems like Google and Facebook provide valuable services while collecting, aggregating, and correlating vast amounts of information about individual users. With inadequate controls, these corporate systems can invade privacy and do substantial damage through either correct or incorrect inferences. Third, acceptance of the legitimacy of the society by its individual members depends on a general perception of fairness. Rage about unfairness can be directed at individual free-riders or at systematic inequality across the society. Ultimately, the promise of a computational approach to ethical knowledge is not simply ethics for computational devices such as robots. It also promises to help people understand the pragmatic value of ethics as a feedback mechanism that helps intelligent creatures, human and nonhuman, live together in thriving societies.

Falsification of Hybrid Systems Using Adaptive Probabilistic Search

2021 ◽  
Vol 31 (3) ◽  
pp. 1-22
Author(s):  
Gidon Ernst ◽  
Sean Sedwards ◽  
Zhenya Zhang ◽  
Ichiro Hasuo
Keyword(s):  
Hybrid Systems ◽  
Input Signal ◽  
Tree Search ◽  
Single Trial ◽  
Decision Point ◽  
Directed Tree ◽  
Probabilistic Choice ◽  
Original Algorithm ◽  
Fine Grained ◽  
Probabilistic Search

We present and analyse an algorithm that quickly finds falsifying inputs for hybrid systems. Our method is based on a probabilistically directed tree search, whose distribution adapts to consider an increasingly fine-grained discretization of the input space. In experiments with standard benchmarks, our algorithm shows comparable or better performance to existing techniques, yet it does not build an explicit model of a system. Instead, at each decision point within a single trial, it makes an uninformed probabilistic choice between simple strategies to extend the input signal by means of exploration or exploitation. Key to our approach is the way input signal space is decomposed into levels, such that coarse segments are more probable than fine segments. We perform experiments to demonstrate how and why our approach works, finding that a fully randomized exploration strategy performs as well as our original algorithm that exploits robustness. We propose this strategy as a new baseline for falsification and conclude that more discriminative benchmarks are required.

Dynamic and heterogeneous neural ensembles contribute to a memory engram

2021 ◽  
Vol 67 ◽  
pp. 199-206
Author(s):  
Brian M Sweis ◽  
William Mau ◽  
Sima Rabinowitz ◽  
Denise J Cai
Keyword(s):  
Neural Ensembles ◽  
Memory Engram

FosGFP expression does not capture a sensory learning-related engram in superficial layers of mouse barrel cortex

2021 ◽  
Vol 118 (52) ◽  
pp. e2112212118
Author(s):  
Jiseok Lee ◽  
Joanna Urban-Ciecko ◽  
Eunsol Park ◽  
Mo Zhu ◽  
Stephanie E. Myal ◽  
...  
Keyword(s):  
Pyramidal Neurons ◽  
Barrel Cortex ◽  
Sensory Information ◽  
Learning Task ◽  
Early Gene ◽  
Sensory Stimuli ◽  
Association Learning ◽  
Neural Ensembles ◽  
Dependent Learning

Immediate-early gene (IEG) expression has been used to identify small neural ensembles linked to a particular experience, based on the principle that a selective subset of activated neurons will encode specific memories or behavioral responses. The majority of these studies have focused on “engrams” in higher-order brain areas where more abstract or convergent sensory information is represented, such as the hippocampus, prefrontal cortex, or amygdala. In primary sensory cortex, IEG expression can label neurons that are responsive to specific sensory stimuli, but experience-dependent shaping of neural ensembles marked by IEG expression has not been demonstrated. Here, we use a fosGFP transgenic mouse to longitudinally monitor in vivo expression of the activity-dependent gene c-fos in superficial layers (L2/3) of primary somatosensory cortex (S1) during a whisker-dependent learning task. We find that sensory association training does not detectably alter fosGFP expression in L2/3 neurons. Although training broadly enhances thalamocortical synaptic strength in pyramidal neurons, we find that synapses onto fosGFP+ neurons are not selectively increased by training; rather, synaptic strengthening is concentrated in fosGFP− neurons. Taken together, these data indicate that expression of the IEG reporter fosGFP does not facilitate identification of a learning-specific engram in L2/3 in barrel cortex during whisker-dependent sensory association learning.

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