All right, let's dive into neuromorphic programming.
Emerging directions for brain-inspired hardware.
You know, it's all about figuring out how to program
those brain-inspired computers.
Yeah, those brain-inspired computers
everyone's talking about really are fascinating.
They're designed to process information more
like our brains do.
Yeah, potentially revolutionizing computing.
But as this paper points out,
figuring out how to actually program them
is a huge challenge.
Right, it's a huge E challenge.
Because we can't just rely on the traditional methods we use.
The traditional methods for programming regular computers
just won't cut it.
Okay, so why is that?
Well, the paper highlights five key differences.
Five key differences between traditional
and brain-inspired computers.
Yeah.
But I think for our deep dive today,
we should focus on the two that are most mind-boggling
and really impact how we have to think about programming.
Sounds good to me.
So, let's start with plasticity.
Okay.
In traditional computers, the hardware stays the same
no matter what program is running.
But brain-inspired computers are different.
Different how?
They actually physically change during operation.
Whoa!
Imagine a computer that rewires itself as it learns.
That's wild.
How do we even begin to control that?
That's one of the big questions researchers
are grappling with.
Yeah.
It leads us to the second key difference, stochasticity.
Stochasticity.
In traditional computers,
we want everything to be predictable.
Yeah, you give an input,
you get a specific output.
Right.
But brain-inspired systems are inherently noisy.
Noisy.
There's randomness built in.
So, it's like trying to program a computer
that has a mind of its own.
Well, kind of.
Yeah.
Making decisions based on probability rather than strict rules.
Exactly.
Debugging that must be a nightmare.
You can say that.
So, these two differences, plasticity and stochasticity,
really force us to rethink
what it even means to program a computer.
Exactly.
Our usual approach is to give the computer
a step-by-step list of instructions.
Yeah, that's the traditional way.
But if the hardware's changing
and there's randomness involved,
those instructions might not have the intended effect.
Right.
This paper argues that we need to move away
from that traditional command and control style of programming
and embrace new approaches that are more aligned
with how brains work.
So, what are some of those approaches?
Well, the paper mentions a whole bunch of them.
It does.
Before we dive into specific methods,
I think it's helpful to understand this overarching idea
the author's present.
Computing with physical systems.
It suggests that we need to consider
the physical properties of the hardware
as an integral part of the computation.
So, we can't just treat the brain-inspired computer
as a black box.
We can't just feed it instructions
and expect predictable outcomes.
Right.
We need to understand how its physical structure
and dynamics are shaping the computation itself.
It's like the difference between playing a piano
and listening to a recording.
Yes.
With a recording, the music is fixed.
But with a piano, the sound you produce
depends on how you interact with the physical instrument.
Exactly.
It's a great analogy.
So, in neuromorphic computing,
the program isn't just a set of instructions.
It's more like a description of how the computation
will emerge from the physical interactions
within the system.
And that leads to a whole new way
of thinking about programming languages.
Right.
Because in traditional programming,
we use languages to write those step-by-step instructions.
Exactly.
But if we're not giving explicit instructions anymore,
then what role do programming languages play?
Yeah.
What role do they play in this new framework?
Well, the authors use the term programming language
in a much broader sense.
Broader how?
It could be any formal language that allows us
to communicate with the physical system
and shape how the computation unfolds.
So, instead of thinking of programming languages,
it's just ways to write instructions.
We need to consider them as ways to describe
the computation that will emerge from the physical system.
Precisely.
And this leads us to a discussion of programming paradigms.
Programming paradigm.
The different approaches we can take to programming
based on certain principles or theories.
Okay.
So, let's unpack some of those paradigms.
Right.
The paper talks about a whole bunch of them,
from the familiar to the downright futuristic.
It does.
It covers traditional paradigms like imperative programming.
Imperative programming.
Where you give the computer a sequence of commands.
Right.
Declarative programming.
Where you describe the desired outcome.
Okay.
Rather than the specific steps.
Yeah.
But then it gets really interesting when it dives
into automated programming.
Automated programming.
Yeah.
That sounds like something straight out of a sci-fi movie.
It kind of is.
Are we talking about computers that program themselves?
In a way, yes.
Automated programming involves using algorithms
to generate programs.
Okay.
Often based on a set of specifications or examples.
So, the computer is doing the programming.
In a sense, yes.
That's wild.
The paper highlights techniques like metaprogramming,
program synthesis, constraint programming,
and inductive programming.
So, it's a way to let the computer take on more
of the heavy lifting in the programming process.
Exactly.
Okay.
So, that's automated programming.
Right.
What other paradigm shifting approaches
does this paper explore?
Well, one that's particularly relevant to neuromorphic
computing is machine learning.
Machine learning.
Everyone's talking about it these days.
It seems like it.
How does it fit into this brain-inspired world?
Well, machine learning can be seen
as a type of automated programming.
Okay.
Instead of explicitly programming every step,
we provide the system with data.
And let it learn the desired behavior.
So, in the context of neuromorphic computing,
we're essentially training the brain-inspired computer
to perform the desired computation.
Needs.
Much like we train our own brains to learn new skills.
That's a good way to put it.
So, we're teaching the computer instead of programming it.
Yes.
And the paper talks about various machine learning
paradigms that are relevant here, including supervised
learning, reinforcement learning, and, of course, deep learning.
Deep learning.
The rock star of the AI world.
Right.
But adapting deep learning for neuromorphic systems
presents unique challenges.
Because of those key differences we talked about earlier.
Exactly.
Plasticity and stochasticity.
So, we can't just take the deep learning algorithms that
work so well in traditional computing.
And apply them directly to neuromorphic systems.
Right.
Not quite.
There's got to be some adaptation.
There's a lot of research going on
to figure out how to adapt deep learning for these brain-inspired
computers.
So, we're just scratching the surface of understanding
how to harness the power of deep learning
in the neuromorphic world.
Yeah, I'd say so.
What other approaches are being explored?
The paper also discusses some non-digital programming
methods that are really interesting, especially
for the types of hardware often used in neuromorphic systems.
OK.
These methods borrow ideas from fields
like control engineering and signal processing.
So, we're looking beyond the traditional world of computer
science to tackle the challenges of neuromorphic programming.
It's a truly interdisciplinary field.
It requires drawing on a wide range of expertise.
This is all so fascinating.
It feels like we're standing on the precipice
of a whole new era of computing.
It does, doesn't it?
But we're also just beginning to understand
how to program these incredible machines.
That's the beauty of it.
There's so much left to explore.
There is.
In the next part of our deep dive,
we'll delve into some specific approaches
to neuromorphic programming that the paper highlights
as particularly promising.
I can't wait.
Welcome back to our deep dive into neuromorphic programming.
I'm ready to get into the nitty gritty.
Last time we explored why programming,
these brain-inspired computers, is so different.
Right.
It's a whole new world.
Now, let's look at some specific approaches.
OK.
Let's get concrete.
Researchers are exploring these to tackle this challenge.
Oh, where do we start?
Let's begin with neuromorphic co-design.
Co-design.
It's not strictly a programming paradigm,
but rather a fundamental approach to designing these systems.
So what exactly does that involve?
It's all about closely integrating
the development of both the hardware and the software.
Together.
Ensuring they work together seamlessly
for a specific application.
OK.
Remember these brain-inspired computers?
They use their physical properties for computation?
So the hardware design is crucial to how we program them.
That's right.
It's like an architect designing a building.
Yeah, like an architect.
I can't just draw up a fancy blueprint.
Right.
They need to consider the materials, the location.
And how everything will fit together.
That's a great analogy.
So co-design seems perfect for specialized applications.
It has been very successful in developing
specialized neuromorphic chips.
Chips that excel at tasks like recognizing patterns?
Yes.
Or processing sensory data?
Almost like artificial senses.
Exactly.
OK, so co-design seems perfect for specialized applications.
But what about creating software that can adapt?
It's a different neuromorphic system.
To tackle a wider range of problems.
That's where machine learning methods come in.
Machine learning.
Remember how we talk about machine learning?
Yeah, letting the computer learn from data.
Instead of giving it explicit instruction.
It's like teaching a computer to ride a bike
by letting it practice.
Exactly.
Instead of giving it a physics lecture.
This approach is becoming increasingly popular
in neuromorphic computing.
Especially with what?
Especially with the rise of spiking neural networks or SNNs.
SNNs.
Right.
We touched on those before.
Yes, they are those brain-like networks.
They're the ones that communicate using
spikes of electrical activity.
Mimicking how real neurons work.
OK.
SNNs have the potential to be much more energy efficient.
More efficient than what?
Then the artificial neural networks
used in traditional deep learning.
Interesting.
And they're much better at processing information
that changes over time.
So SNNs are like the next generation of neural networks.
Taking inspiration directly from the brain's own communication
system.
OK, I see.
Researchers are exploring different machine learning
methods that work well with these spiking networks.
Going beyond just adapting traditional deep learning
techniques.
Precisely.
So we can't just copy and paste from the traditional world
of AI.
We need to develop methods tailored
to these unique SNNs.
I don't.
And one particularly fascinating area is online learning.
Online learning.
What does that mean?
It's all about allowing brain inspired computers
to learn and adapt continuously.
Continuous.
As they interact with the world.
So no more training offline with massive data sets.
Exactly.
So instead of teaching a network, everything up front.
We let it learn on the fly.
Kind of like how our own brains constantly adapt
to new experiences.
Exactly.
And one promising way to implement this.
Just through plasticity.
Plasticity, right?
Remember how we talked about brain inspired computers
physically changing during operation?
Yeah, they're rewiring themselves.
Well, plasticity refers to their ability
to modify their own structure and connection.
Their connections.
In response to new information.
Almost like rewiring their circuits as they learn.
Exactly.
So we're not just training the network.
We're letting it re-architect itself as it learns.
That's mind blowing.
The challenge, of course, is guiding that rewiring process.
Oh, way back.
To make sure the system learns what we want it to learn.
Right.
Researchers are using techniques like evolutionary algorithm.
Evolutionary algorithm.
And meta learning.
OK.
To figure out the best plasticity rules.
Plasticity rules.
That govern how the system modifies itself.
OK, so online learning through plasticity is one path.
What other approaches are out there?
Another fascinating avenue is the use of evolutionary methods.
Evolutionary methods.
You know how natural selection has
shaped the incredible diversity and complexity of life
on Earth?
Right.
Survival of the fittest and all that.
Exactly.
So instead of designing a solution ourselves.
We're setting up a sort of digital ecosystem
where solutions can evolve organically.
So a survival of the fittest but for neural networks.
That's a great way to put it.
And over many generations.
The algorithm discovers incredibly efficient and effective
network architectures.
Exactly.
And this approach doesn't require the network
to be differentiable.
Meaning it can be applied to a broader range of SNN models.
OK.
Including some that are difficult to train
with traditional deep learning techniques.
This is all incredibly fascinating.
But I have to admit it's a lot to keep up with.
I know.
There are so many approaches.
Podesign, machine learning, online learning.
Plasticity, evolutionary methods.
It feels like we need a whole new dictionary just to talk
about neuromorphic programming.
It's a rapidly evolving field with its own unique concepts
and terminology.
Right.
But the key takeaway is that we're moving away
from the old way of programming.
The old command and control way.
Where we dictate every step to the computer.
Instead, we're embracing approaches
that allow the computation to emerge.
From the dynamic interactions.
From the interactions within these brain inspired systems.
It's a paradigm shift in how we think about computation
itself.
It really is.
And this shift is leading to some truly ingenious approaches
to programming.
Let's let's next.
In the next part of our deep dive,
we'll explore even more of these approaches.
Really?
Something called spiking neuromorphic algorithms.
OK.
And the concept of higher abstractions.
Stay tuned, listeners.
Because we're just scratching the surface
of this exciting journey into the future of computing.
Welcome back, listeners, for the final part of our deep dive
into the fascinating world of neuromorphic programming.
We've explored why programming these brain inspired
computers is such a unique challenge.
And we've looked at some high level
approaches for tackling it.
Right.
Now let's get even more concrete and see how some of these ideas
are being put into practice.
OK.
Let's do it.
We'll explore spiking neuromorphic algorithms.
Spiking neuromorphic algorithms.
And the concept of higher abstractions.
Higher abstractions.
Both of which are key to unlocking
the true potential of these systems.
OK.
Let's start with these spiking neuromorphic algorithms,
or SNAs.
OK.
What makes them so special?
SNAs are algorithms specifically designed
to work with the unique way spiking neural networks
compute.
So they're tailored for SNNs.
Yes.
Remember SNNs use spikes of electrical activity
to communicate.
Just like real neurons in the brain.
Well, SNAs capitalize on these spikes
to perform computations in a way that traditional algorithms
simply can't.
So SNAs are like a custom design toolkit
for speaking the language of the brain.
Yeah, you could say that.
Taking advantage of those precise spikes of activity.
Exactly.
They leverage the timing and dynamics of these spikes.
OK.
To do things that are either very difficult or impossible
for traditional algorithms.
Can you give us some examples of what these SNAs can do?
What kind of problems can they solve?
The paper mentioned some really fascinating applications.
For instance, researchers have developed SNAs
that can perform calculations on sets of numbers.
Nibbulate graphs, solve constraint satisfaction
problems, and even model physical phenomena
like the spread of heat.
That's a pretty diverse set of applications.
It is.
And as we develop more sophisticated SNAs,
we'll unlock even more of the computational power
of these brain inspired systems.
It sounds like SNAs have the potential
to revolutionize many different fields.
They absolutely do.
OK.
So SNAs sound incredibly powerful and versatile.
Yeah.
But I imagine they're also pretty complex
to design and implement.
Well, they can be.
Is there any way to make programming with SNNs more
accessible?
More accessible.
Especially for those of us who aren't neuroscientists
or computer engineering wizards.
That's a great question.
And it brings us to the idea of higher abstractions.
Higher abstractions.
Think about it like this.
When you write a program in a high level language,
like Python.
Python.
OK.
You don't have to worry about the low level details
of how the computer's processor actually
executes each instruction.
Right.
The language provides you with a higher level of abstraction.
A higher level of abstraction.
Allowing you to focus on the logic of your program.
All right.
Rather than the nitty gritty of machine code.
It's like using a map to navigate a city.
Yes.
Instead of memorizing every single street in Alleyway.
And in the world of neuromorphic programming.
Oh, OK.
Researchers are developing tools and frameworks
that provide similar high level abstractions
for working with SNNs.
So these tools would allow us to program
brain inspired computers without needing
to understand all the intricate details of how
neurons communicate and interact.
That's the goal.
That would be a game changer.
These higher abstractions aim to make neuromorphic programming
more user friendly.
User friendly.
And accessible.
Opening it up to a wider range of developers.
The paper mentions a couple of specific examples
of these high level tools.
Can you tell us about them?
Of course, one example is the neural engineering framework
or Nengo.
Nengo.
Nengo allows programmers to describe the computations
they want to perform at a higher level.
A higher level.
Using concepts from dynamical systems theory.
OK.
Then it automatically translates those descriptions
into networks of spiking neurons.
So you don't need to be a neuroscience expert to use Nengo?
Not necessarily.
You can describe what you want to achieve
in a more abstract way.
Right.
And Nengo takes care of all the messy details of implementing
it with those spiking neurons.
Exactly.
That's incredible.
It makes designing complex brain inspired systems much more
accessible and intuitive.
What's the other example of the paper highlights?
The other example is Lava, an open source framework designed
specifically for neuromorphic computing.
So Lava is like a toolbox full of pre-built components
and utilities for working with brain inspired computers.
That's a great analogy.
Does it have those SNAs we were talking about?
It does.
Lava includes a growing library of neuromorphic algorithms
and tools.
Including support for those SNAs we talked about.
It also provides higher level abstractions
for describing computations.
OK.
And managing the complexities of SNNs.
It sounds like Lava is trying to build an entire ecosystem
for neuromorphic programming.
Kind of is.
Giving developers everything they
need to build sophisticated applications
for these new types of computers.
And the best part is that it's open source.
Open source.
So anyone can contribute.
So anyone can contribute to its development
and help push the field forward.
This is all so incredibly exciting.
It is.
It feels like we're truly on the verge of a revolution
in computing.
I agree.
But as we've discussed throughout this deep dive,
it's still early days.
Very early.
What are some of the key challenges that lie ahead?
One of the biggest challenges is developing
a unified theory of neuromorphic computation.
A unified theory.
A framework that can bridge the gap
between the physical dynamics of these systems
and the abstract concepts we use to describe computation.
We need a way to connect the language of the brain
with the language of computer science.
Exactly.
Without a common framework.
It's hard to make progress.
It's difficult to compare different approaches,
share knowledge.
Right.
And build upon each other's work.
It sounds like a daunting challenge,
but also an incredibly exciting one.
It absolutely is.
There's so much we still don't understand
about how the brain computes.
And those mysteries hold the key to unlocking
the true potential of neuromorphic systems.
We're like explorers charting a new frontier.
And every discovery brings us closer
to understanding the very nature of intelligence itself.
That's beautifully put.
And it sounds like the journey is just beginning.
It really is.
As we delve deeper into this world of neuromorphic programming,
we'll not only create more powerful and efficient computers,
but we'll also gain a deeper understanding of our own minds.
That's a great point.
It's been a pleasure exploring this fascinating topic
with you.
Likewise.
And to our listeners, I encourage you to keep your minds
open and your curiosity peaked.
Curiosity peaked.
Who knows?
Maybe you will be the one to discover the next breakthrough
in neuromorphic computing.
That's a great note to end on.
Thanks.
Thank you for joining us on this incredible journey
into the future of computing.