4,355 research outputs found
Computational techniques to interpret the neural code underlying complex cognitive processes
Advances in large-scale neural recording technology have significantly improved the
capacity to further elucidate the neural code underlying complex cognitive processes.
This thesis aimed to investigate two research questions in rodent models. First, what
is the role of the hippocampus in memory and specifically what is the underlying
neural code that contributes to spatial memory and navigational decision-making.
Second, how is social cognition represented in the medial prefrontal cortex at the
level of individual neurons. To start, the thesis begins by investigating memory and
social cognition in the context of healthy and diseased states that use non-invasive
methods (i.e. fMRI and animal behavioural studies). The main body of the thesis
then shifts to developing our fundamental understanding of the neural mechanisms
underpinning these cognitive processes by applying computational techniques to ana lyse stable large-scale neural recordings. To achieve this, tailored calcium imaging
and behaviour preprocessing computational pipelines were developed and optimised
for use in social interaction and spatial navigation experimental analysis. In parallel,
a review was conducted on methods for multivariate/neural population analysis. A
comparison of multiple neural manifold learning (NML) algorithms identified that non linear algorithms such as UMAP are more adaptable across datasets of varying noise
and behavioural complexity. Furthermore, the review visualises how NML can be
applied to disease states in the brain and introduces the secondary analyses that
can be used to enhance or characterise a neural manifold. Lastly, the preprocessing
and analytical pipelines were combined to investigate the neural mechanisms in volved in social cognition and spatial memory. The social cognition study explored
how neural firing in the medial Prefrontal cortex changed as a function of the social
dominance paradigm, the "Tube Test". The univariate analysis identified an ensemble
of behavioural-tuned neurons that fire preferentially during specific behaviours such
as "pushing" or "retreating" for the animal’s own behaviour and/or the competitor’s
behaviour. Furthermore, in dominant animals, the neural population exhibited greater
average firing than that of subordinate animals. Next, to investigate spatial memory,
a spatial recency task was used, where rats learnt to navigate towards one of three
reward locations and then recall the rewarded location of the session. During the
task, over 1000 neurons were recorded from the hippocampal CA1 region for five rats
over multiple sessions. Multivariate analysis revealed that the sequence of neurons encoding an animal’s spatial position leading up to a rewarded location was also active
in the decision period before the animal navigates to the rewarded location. The result
posits that prospective replay of neural sequences in the hippocampal CA1 region
could provide a mechanism by which decision-making is supported
A Political Theory of Engineered Systems and A Study of Engineering and Justice Workshops
Since there are good reasons to think that some engineered systems are socially undesirable—for example, internal combustion engines that cause climate change, algorithms that are racist, and nuclear weapons that can destroy all life—there is a well-established literature that attempts to identify best practices for designing and regulating engineered systems in order to prevent harm and promote justice. Most of this literature, especially the design theory and engineering justice literature meant to help guide engineers, focuses on environmental, physical, social, and mental harms such as ecosystem and bodily poisoning, racial and gender discrimination, and urban alienation. However, the literature that focuses on how engineered systems can produce political harms—harms to how we shape the way we live in community together—is not well established. The first part of this thesis contributes to identifying how particular types of engineered systems can harm a democratic politics. Building on democratic theory, philosophy of collective harms, and design theory, it argues that engineered systems that extend in space and time beyond a certain threshold subvert the knowledge and empowerment necessary for a democratic politics. For example, the systems of global shipping and the internet that fundamentally shape our lives are so large that people cannot attain the knowledge necessary to regulate them well nor the empowerment necessary to shape them.
The second part of this thesis is an empirical study of a workshop designed to encourage engineering undergraduates to understand how engineered systems can subvert a democratic politics, with the ultimate goal of supporting students in incorporating that understanding into their work. 32 Dartmouth undergraduate engineering students participated in the study. Half were assigned to participate in a workshop group, half to a control group. The workshop group participants took a pretest; then participated in a 3-hour, semi-structured workshop with 4 participants per session (as well as a discussion leader and note-taker) over lunch or dinner; and then took a posttest. The control group participants took the same pre- and post- tests, but had no suggested activity in the intervening 3 hours. We find that the students who participated in workshops had a statistically significant test-score improvement as compared to the control group (Brunner-Munzel test, p \u3c .001). Using thematic analysis methods, we show the data is consistent with the hypothesis that workshops produced a score improvement because of certain structure (small size, long duration, discussion-based, over homemade food) and content (theoretically rich, challenging). Thematic analysis also reveals workshop failures and areas for improvement (too much content for the duration, not well enough organized).
The thesis concludes with a discussion of limitations and suggestions for future theoretical, empirical, and pedagogical research
Towards Neuromorphic Gradient Descent: Exact Gradients and Low-Variance Online Estimates for Spiking Neural Networks
Spiking Neural Networks (SNNs) are biologically-plausible models that can run on low-powered non-Von Neumann neuromorphic hardware, positioning them as promising alternatives to conventional Deep Neural Networks (DNNs) for energy-efficient edge computing and robotics. Over the past few years, the Gradient Descent (GD) and Error Backpropagation (BP) algorithms used in DNNs have inspired various training methods for SNNs. However, the non-local and the reverse nature of BP, combined with the inherent non-differentiability of spikes, represent fundamental obstacles to computing gradients with SNNs directly on neuromorphic hardware. Therefore, novel approaches are required to overcome the limitations of GD and BP and enable online gradient computation on neuromorphic hardware.
In this thesis, I address the limitations of GD and BP with SNNs by proposing three algorithms. First, I extend a recent method that computes exact gradients with temporally-coded SNNs by relaxing the firing constraint of temporal coding and allowing multiple spikes per neuron. My proposed method generalizes the computation of exact gradients with SNNs and enhances the tradeoffs between performance and various other aspects of spiking neurons. Next, I introduce a novel alternative to BP that computes low-variance gradient estimates in a local and online manner. Compared to other alternatives to BP, the proposed method demonstrates an improved convergence rate and increased performance with DNNs. Finally, I combine these two methods and propose an algorithm that estimates gradients with SNNs in a manner that is compatible with the constraints of neuromorphic hardware. My empirical results demonstrate the effectiveness of the resulting algorithm in training SNNs without performing BP
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
Bandit Social Learning: Exploration under Myopic Behavior
We study social learning dynamics where the agents collectively follow a
simple multi-armed bandit protocol. Agents arrive sequentially, choose arms and
receive associated rewards. Each agent observes the full history (arms and
rewards) of the previous agents, and there are no private signals. While
collectively the agents face exploration-exploitation tradeoff, each agent acts
myopically, without regards to exploration. Motivating scenarios concern
reviews and ratings on online platforms.
We allow a wide range of myopic behaviors that are consistent with
(parameterized) confidence intervals, including the "unbiased" behavior as well
as various behaviorial biases. While extreme versions of these behaviors
correspond to well-known bandit algorithms, we prove that more moderate
versions lead to stark exploration failures, and consequently to regret rates
that are linear in the number of agents. We provide matching upper bounds on
regret by analyzing "moderately optimistic" agents.
As a special case of independent interest, we obtain a general result on
failure of the greedy algorithm in multi-armed bandits. This is the first such
result in the literature, to the best of our knowledg
Witnessing environment dimension through temporal correlations
We introduce a framework to compute upper bounds for temporal correlations
achievable in open quantum system dynamics, obtained by repeated measurements
on the system. As these correlations arise by virtue of the environment acting
as a memory resource, such bounds are witnesses for the minimal dimension of an
effective environment compatible with the observed statistics. These witnesses
are derived from a hierarchy of semidefinite programs with guaranteed
asymptotic convergence. We compute non-trivial bounds for various sequences
involving a qubit system and a qubit environment, and compare the results to
the best known quantum strategies producing the same outcome sequences. Our
results provide a numerically tractable method to determine bounds on
multi-time probability distributions in open quantum system dynamics and allow
for the witnessing of effective environment dimensions through probing of the
system alone.Comment: 24 pages, 7 figure
Blind Beamforming for Intelligent Reflecting Surface in Fading Channels without CSI
This paper discusses how to optimize the phase shifts of intelligent
reflecting surface (IRS) to combat channel fading without any channel state
information (CSI), namely blind beamforming. Differing from most previous works
based on a two-stage paradigm of first estimating channels and then optimizing
phase shifts, our approach is completely data-driven, only requiring a dataset
of the received signal power at the user terminal. Thus, our method does not
incur extra overhead costs for channel estimation, and does not entail
collaboration from service provider, either. The main idea is to choose phase
shifts at random and use the corresponding conditional sample mean of the
received signal power to extract the main features of the wireless environment.
This blind beamforming approach guarantees an boost of signal-to-noise
ratio (SNR), where is the number of reflective elements (REs) of IRS,
regardless of whether the direct channel is line-of-sight (LoS) or not.
Moreover, blind beamforming is extended to a double-IRS system with provable
performance. Finally, prototype tests show that the proposed blind beamforming
method can be readily incorporated into the existing communication systems in
the real world; simulation tests further show that it works for a variety of
fading channel models.Comment: 14 pages, 14 figure
A First Course in Causal Inference
I developed the lecture notes based on my ``Causal Inference'' course at the
University of California Berkeley over the past seven years. Since half of the
students were undergraduates, my lecture notes only require basic knowledge of
probability theory, statistical inference, and linear and logistic regressions
Investigating the learning potential of the Second Quantum Revolution: development of an approach for secondary school students
In recent years we have witnessed important changes: the Second Quantum Revolution is in the spotlight of many countries, and it is creating a new generation of technologies.
To unlock the potential of the Second Quantum Revolution, several countries have launched strategic plans and research programs that finance and set the pace of research and development of these new technologies (like the Quantum Flagship, the National Quantum Initiative Act and so on).
The increasing pace of technological changes is also challenging science education and institutional systems, requiring them to help to prepare new generations of experts.
This work is placed within physics education research and contributes to the challenge by developing an approach and a course about the Second Quantum Revolution. The aims are to promote quantum literacy and, in particular, to value from a cultural and educational perspective the Second Revolution.
The dissertation is articulated in two parts. In the first, we unpack the Second Quantum Revolution from a cultural perspective and shed light on the main revolutionary aspects that are elevated to the rank of principles implemented in the design of a course for secondary school students, prospective and in-service teachers. The design process and the educational reconstruction of the activities are presented as well as the results of a pilot study conducted to investigate the impact of the approach on students' understanding and to gather feedback to refine and improve the instructional materials.
The second part consists of the exploration of the Second Quantum Revolution as a context to introduce some basic concepts of quantum physics. We present the results of an implementation with secondary school students to investigate if and to what extent external representations could play any role to promote students’ understanding and acceptance of quantum physics as a personal reliable description of the world
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