biological foundations for deep learning: towards decision networks

Nathan R. Wilson, Ph.D.CSIG Speaker SeriesJune 23, 2016

Biological Foundations for Deep Learning: Towards Decision Networks

Neural Networks and Neuroscience

• Benefits of Cross-Pollination

• New Learning Rules and Emerging Analogies

• “Recommendations and Decision Support” as an

Enriched Domain for Both

Today

The Interface of Neuroscience +

Computer Science

Benefits of Cross-Pollination

Overcoming Stereotypes

Neuroscientist- Focused on insignificant details

- Loves chemicals, ice, rodents

- Has trouble seeing the big picture

Reality: has many of the same

goals as DL researchers

Deep Learning Researcher- Doesn’t pay any dues

- Disembodied from traditional fields

- Works out of coffee shops

Reality: establishing one of the most

important disciplines of 21st century

Neural Networks and Neuroscience >

Neural networks and neuroscience

share the same core orientation

• Same Goal: overarching

understanding of a general algorithm

• Same Structure:

• Connectionist not Von Neumann

• Pathways not rules

• Connection weights are the key

• Structure is function

• Same Puzzles:

• What is the optimal transfer function?

• Is the code distributed or localized?

• How are sequences learned?



So why study nature? Why biological neural networks?

A Lesson from the 20th Century: Aviation


Otto Lilienthal, Foundations of Modern Aviation

AI is to the brain as airplanes

are to birds. The details are

different, but the underlying

principles are the same.

-- Yann LeCun, 2015

The Wright Brothers spent a great

deal of time observing birds in flight.


Common retort: “Of course, modern aircraft look

nothing like birds”

Stabilizing Similar Ideas Through Cross-Linking


Neuroscience:Has spent a century on how to

map connectionist frameworks

directly to cognitive,

psychological, and social

principles => where AI is headed.

Neural Networks:Are emerging as the dominant

framework for machine learning, and

will inherit / reconcile mappings from

other adjacent fields of AI.


Why Now?


Neuroscience Has:New tools to interact with cells at

the “network” level (Zhang et al.,

2010, Wilson et al. 2013, Nature

Protocols) and uncover insights.

Neural Networks Has:Rigorous frameworks and data sets

for evaluating network intelligence.

Software for identifying and optimizing

key parameters of learning.


A difference of terminology, but not concepts.

What is the goal?

Neuroscience: “maximize reward”

Neural Networks: “minimize loss”

What is the system doing?

Neuroscience: “learning from local micro-successes (Hebb)”

Neural networks: “globally optimizing a function (backprop)”

What are additional parameters for?

Neuroscience: Stabilizing firing rates

Neural networks: Regularization

Learning Rules and Analogies


“Feedforward” transmission is electrical and easy to measure in the brain

“Retrograde” signals are more “invisible”, but candidates exist

Hebbian or STDP learning could provide mechanics for gradient descent

(Markram et al., 1997; Bi and Poo, 1998; Xie and Seung, 2003; Bengio et al., 2016)

A mismatch between pairs of neurons could be construed by the cells as a local

error signal which could then propagate further.

Methods are emerging that will explain how/if backpropagation happens

Synaptic plasticity and backpropagation

• Signals like: NGF, BDNF, cannabinoids, NO

• Some are released in proportion to synapse

strength

• Can travel back through vesicular uptake,

cytoplasmic transport

Neural Networks and Neuroscience > Learning Rules and Analogies >

Resisting “out of range” connections

Synaptic Plasticity and Regularization

Neurons will “auto-tune” at different scales:

• Inter-synaptic competition (Fonseca, 2002)

• Single neuron within network (Murthy, 2003)

• Trade strength for more partners (Wilson, 2007)

• Whole network scaling (Turrigiano 1998, 2008)

Neurons also undergo forms of “dropout”

• Sparse coding and decorrelation (Olshausen

2004)

• Stochastic firing; stochastic synapses (Zador

1999, Abbott 2004)


Inhibitory connections: more than meets the eye

Negative weights in networks

Denève et al.,

Nature Neuroscience 2016

Cells seem to “want”

an excitatory /

inhibitory balance

(Liu, Nature

Neuroscience, 2004)

Inhibition is the basis for

network gain control

(Wilson et al., Nature

2012)

Carandini et al.,

Nature Rev. Neuro 2012


Neuroscience can inform us on how networks learn in practice.

Nowhere is this more true than in…

Interesting aspects of recommendations / decision support:

• As with games, it connects perception to cognition and action

• Remains an original commercial justification of machine learning

• Highly structured data sets and goals, rigorous arenas for success.

Recommendations / Decision Support:

an Interesting Network Learning Problem

Games / Which Move to Make Recommendations /

Decision Support

Multi-level learning problems:

Our networks learn to “match” contexts to decisions:

• Travel: which spots should I visit when my plane lands?

• Entertainment: which movie is right for me and my friends?

• Medicine: which available doctor is right for this patient?

• Crime: which events could be related to this incident?

• Fraud: which recent behavior doesn’t match the others?

• Supply Chain: which component is exhibiting fault-predictive traits?

Recommendations / Decision Support:

An Interesting Network Learning Problem

Evaluate many criteria, and “match” a recommended decision.

Brain-like algorithms can construct networks of knowledge

In any domain to power real-time recommendations and decisions.

Elucidating Pathways for Recommendations Through Network Learning

Elucidating Pathways for Recommendations Through Network Learning

Learning Representations Using

“Perceptual” vs. “Cognitive” Structures


“Mommy’s hair

is melting”

Challenge 1: Recommendations need to cold start and

generalize to new things, but then also to hyper-optimize

• Bottom-up representations via sparse,

“one shot” learning => like PageRank

• Works in cold-start conditions

• Can trace back reasons for answers

Deep learning techniques can then further

optimize recommendations, when historical data

is available to support them => see paper on this

appearing soon.

Challenge 2: the # of nuanced features gathered is pivotal

to your recommendation success, across all algorithms

Liam Neeson, 1990s:

Liam Neeson, 2010s:

Andrew Ng – Importance of Data

Plot Concept

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Challenge 3: Effective learning stacks are multi-

component and must be kept organized

III. Data Organization Engine• Structured, unstructured• Cross-source, un-harmonized• Feature engineering pathways

II. Learning Platform• Unsupervised• Supervised

I. Interfaces and APIs• Recommendations• Profiles / Analytics

Special Thanks:

• Observations around deep learning at Nara Logics:

• General-purpose recommendations pushed us to create a general

implementation that worked across many domains and contexts.

• Deep learning is compatible with our neuroscience-inspired

association networks, and we continue to work on this convergence.

• Analytics and interfaces into these networks are as important as the

learning itself, for maintaining and extending performance.

Deep Learning for Recommendations Is Now an

Important Part of Our General Process

Sahil Zubair Denise Ichinco Raymond Plante Jana Eggers

Neuroscience and deep learning research offer

complementary insights that can be utilized in practice.

For galvanizing this work, “recommendations” offers a

particularly rich and well-structured domain for

exploring the relationship between data and decisions.

Summary: Today

Closing Quote; Thanks

Good luck with what you may build, and

never forget where you came from.

Nathan R. Wilson, Ph.D.CSIG Speaker SeriesJune 23, 2016

Biological Foundations for Deep Learning: Towards Decision Networks

biological foundations for deep learning: towards decision networks

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