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Building better models in cognitive neuroscience. Part 1: Theory
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Describes a rigorous, proven method to create quantitate neuroscience theories.
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The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
Enterprise Knowledge
Digital Global Overview Report 2024 Slides presentation for Event presented in 2024 after compilation of data around last year.
[2024]Digital Global Overview Report 2024 Meltwater.pdf
[2024]Digital Global Overview Report 2024 Meltwater.pdf
hans926745
I've been in the field of "Cyber Security" in its many incarnations for about 25 years. In that time I've learned some lessons, some the hard way. Here are my slides presented at BSides New Orleans in April 2024.
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
Rafal Los
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Enterprise Knowledge’s Urmi Majumder, Principal Data Architecture Consultant, and Fernando Aguilar Islas, Senior Data Science Consultant, presented "Driving Behavioral Change for Information Management through Data-Driven Green Strategy" on March 27, 2024 at Enterprise Data World (EDW) in Orlando, Florida. In this presentation, Urmi and Fernando discussed a case study describing how the information management division in a large supply chain organization drove user behavior change through awareness of the carbon footprint of their duplicated and near-duplicated content, identified via advanced data analytics. Check out their presentation to gain valuable perspectives on utilizing data-driven strategies to influence positive behavioral shifts and support sustainability initiatives within your organization. In this session, participants gained answers to the following questions: - What is a Green Information Management (IM) Strategy, and why should you have one? - How can Artificial Intelligence (AI) and Machine Learning (ML) support your Green IM Strategy through content deduplication? - How can an organization use insights into their data to influence employee behavior for IM? - How can you reap additional benefits from content reduction that go beyond Green IM?
Driving Behavioral Change for Information Management through Data-Driven Gree...
Driving Behavioral Change for Information Management through Data-Driven Gree...
Enterprise Knowledge
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Explore the leading Large Language Models (LLMs) and their capabilities with a comprehensive evaluation. Dive into their performance, architecture, and applications to gain insights into the state-of-the-art in natural language processing. Discover which LLM best suits your needs and stay ahead in the world of AI-driven language understanding.
Evaluating the top large language models.pdf
Evaluating the top large language models.pdf
ChristopherTHyatt
This presentations targets students or working professionals. You may know Google for search, YouTube, Android, Chrome, and Gmail, but did you know Google has many developer tools, platforms & APIs? This comprehensive yet still high-level overview outlines the most impactful tools for where to run your code, store & analyze your data. It will also inspire you as to what's possible. This talk is 50 minutes in length.
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
wesley chun
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
Axa Assurance Maroc - Insurer Innovation Award 2024
The Digital Insurer
Stay safe, grab a drink and join us virtually for our upcoming "GenAI Risks & Security" Meetup to hear about how to uncover critical GenAI risks and vulnerabilities, AI security considerations in every company, and how a CISO should navigate through GenAI Risks.
GenAI Risks & Security Meetup 01052024.pdf
GenAI Risks & Security Meetup 01052024.pdf
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My presentation at the Lehigh Carbon Community College (LCCC) NSA GenCyber Cyber Security Day event that is intended to foster an interest in the cyber security field amongst college students.
GenCyber Cyber Security Day Presentation
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[2024]Digital Global Overview Report 2024 Meltwater.pdf
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Evaluating the top large language models.pdf
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Building better models in cognitive neuroscience. Part 1: Theory
1.
Dr. Brian J.
Spiering Building Better Models in Cognitive Neuroscience: THEORY
2.
3.
4.
5.
Better models help solve better
problems
6.
How to Build
Better A) Why? B) Ideals C) Modeling Cognitive Neuroscience Models
7.
8.
Increased Constraints
9.
Find Unexpected Relationships
10.
Make Novel Predictions
11.
Better Model Testing
12.
IDEALS
13.
Neuroscience
14.
1) Be consistent
only known connections 2) Excitatory or Inhibitory Connections: Pick One 3) Predicted behavior consistent with known behavior 4) Predicted learning consistent with known learning
15.
Simplicity
16.
Set-in-Stone
17.
Input Black Box Output
18.
Goodness-of-Fit
19.
Biological Simplicity vs. Scalability
20.
Units Connections Learning Behavior
21.
Units
22.
MODELING UNITS
23.
Leaky Integrate-and-fire Model
24.
Leaky Integrate-and-fire Model
25.
Leaky Integrate-and-fire Model
26.
Activation
27.
Set spiking threshold,Vpeak, onVB(t)
28.
29.
Leaky Integrate-and-fire Model with
quadratic polynomial
30.
31.
Izhikevich model
32.
See Figure 3
in Paper
33.
Units Connections
34.
Axon & Synaptic
Delays
35.
Break
36.
L E A R N I N G
37.
Units Connections Learning
38.
Long-Term Potentiation (LTP) Long lasting
increase in the efficacy of a synapse
39.
Long-Term Depression (LTD) Long lasting
decrease in the efficacy of a synapse
40.
Dopamine Important function but depends
on location
41.
42.
43.
Discrete-Time vs. Continuous-Time
44.
Discrete Time &
Slow DA Reuptake
45.
Discrete Time &
Slow DA Reuptake
46.
Discrete Time &
Fast DA Reuptake
47.
Modeling Dopamine Release
48.
Input Black Box Output
49.
Predicted Reward - Reward Prediction Error (RPE) Obtained Reward =
50.
Obtained Reward (Simple) +1
if correct -1 if error
51.
Predicted Reward (Simple)
52.
Dopamine & Reward Prediction
Error
53.
Continuous-time models of
learning
54.
Learning Rules: Local vs. Global
55.
56.
57.
+
58.
BEHAVIOR
59.
Units Connections Learning Behavior
60.
1) Which brain
regions controls behavior?
61.
Input Black Box Output
62.
2) What is
the neural activity function that drives the decision?
63.
1)Integrated neural activity 2)Spiking
behavior 3)Integrated output alpha function
64.
Integrated Output Alpha
Function
65.
3) How is
response competition resolved?
66.
Two Choice Tasks 1)Diffusion
Model 2)Accumulator/Race Model with Lateral Inhibition
67.
>Two Choice Tasks •
Set criterion at each decision unit • 1st unit that crosses threshold “wins” • Add lateral inhibition
68.
OVERCONNECTING
69.
Have a Happy Thanksgiving!
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