Nikolaus Kriegeskorte – CCN 2017

Five questions answered by Birte Forstmann

Nikolaus Kriegeskorte — Sat, 29 Jul 2017 12:01:08 +0000

Photo by Jeroen Oerlemans

(1) How can we find out how the brain works?

I believe that formal models that make simultaneous predictions about different modalities such as behavior and the brain are powerful tools. Such tools could help to gain a better mechanistic understanding of brain function.

(2) What will your talk at CCN 2017 be about?

In my talk I will focus on the human subcortex. I will show how different scales can be combined including information from the cellular level by means of immunohistochemistry to neurocognitive modeling and how they can be combined in joint models. Finally, I will show how this knowledge can directly translate to the bedside.

(3) How can cognitive science, computational neuroscience, and artificial intelligence best work together?

Organizing exciting new meetings such as CCN is an excellent start. In addition, hands-on workshops are an interesting format to get people familiarized with the tools and data each discipline has to offer. In the lab, it is fun to have people with backgrounds from the cognitive sciences, basic/cognitive neurosciences, and artificial intelligence work together and see how their curiosity drives them to learn with and from each other.

(4) What current developments are you most excited about?

I am excited about technologies that give us noninvasive access to small structures that lie deep in the brain including ultra-high field MRI and Connectom scanners. My hope is that this technology will deliver better brain data, which in turn is essential to developing more precise neurocognitive models and may ultimately translate to the bedside.

(5) What do you hope to learn at CCN 2017?

A lot.

Five questions answered by Michael Shadlen

Nikolaus Kriegeskorte — Wed, 26 Jul 2017 17:28:40 +0000

(1) How can we find out how the brain works?

By testing neurobiological hypotheses that address the “how” question at a variety of levels. To me, “how the brain works” is a biological problem because I am less interested in mimicking the brain with a machine than I am in assessing what goes wrong when the brain doesn’t work, and how we might remedy the fault. To this end, functional equivalence (like airplanes to birds), which might interest the engineer, is not enough and possibly detrimental—a misguided diversion. To make progress on the “how” of cognitive function, my approach is to focus less on the representation of information and more on what the organism does with the information. To put it crudely, start at the motor system and work backwards. Another practical guide is the time signature of neural processing. Propositions, beliefs, plans and decisions transpire on time scales that necessitate persistence—that are free from the immediacy of sensory processing and online motor control. We need big (new) ideas, not big data acquired under the presumption that all the brain has to work with is connectionism and Hebbian plasticity.

(2) What will your talk at CCN 2017 be about?

I haven’t decided yet. Yes, I will actually talk about that.

(3) How can cognitive science, computational neuroscience, and artificial intelligence best work together?

By drawing on and attempting to influence the missing element from this list: experimental neurobiology. Abandon functionalism (think airplane/bird neural network/brain), magical incantations (add fairy dust du jour and the gorilla is now consciously perceived, where dust equals oscillation or synchrony or ignition), parlor tricks (e.g., chips that [appear to] control limbs or algorithms that read minds), and obfuscation (e.g., appeals to high dimensional representations without a testable/plausible notion of a biological mechanism for read out—unless we count the statistician with a computer as a biological mechanism).

(4) What current developments are you most excited about?

The emerging intersection of circuit- and systems-level approaches in behavior. The ability to record from neurons based on their connectivity to other neurons in nonhuman primates—still just a promise, but it will open a new era in neurophysiology.

(5) What do you hope to learn at CCN 2017?

Why 90% of what I just wrote is wrong. And/or that there are a few sisters and brothers out there who share similar prejudices. And/or whether my kevlar vest works as advertised (just in case, I’m A positive).

Five questions answered by Yoshua Bengio

Nikolaus Kriegeskorte — Thu, 13 Jul 2017 17:47:40 +0000

1 How can we find out how the brain works?

If there is a compact description of the computational principles which explain how the brain manages to provide us with our intelligence, this is something I would consider the core explanation for how the brain works – a little bit like the laws of physics for our physical world. Note that this is very different from the structured observation of our world in all its encyclopedic detail, which provides a useful map of our world, but not a principled explanation. Just replace ‘world’ by ‘brain’. My thesis is that those principles would also allow us to build intelligent machines and that at the heart of our intelligence is our ability to learn and make sense of the world, by observing it and interacting with it. That is why I believe in the importance of a continuous discussion between the brain researchers and AI researchers, especially those in machine learning – particularly deep learning and neural networks. This is likely to benefit AI research as well, as it has in the past.

2 What will your talk at CCN 2017 be about?

I will start by discussing the recent progress in deep learning research, focusing on the inspiration from neuroscience and cognition. I will also talk about novel work, which aims at bridging the gap between back-propagation, the workhorse of deep learning, and neuroscience, as well as about unsupervised reinforcement learning approaches on the cognitive side, which may help to formalize in a machine learning framework how a learner could discover the notions of attributes and objects (as independently controllable aspects of the environment).

3 How can cognitive science, computational neuroscience, and artificial intelligence best work together?

By learning about each other’s developments, collaborating, as usual!

4 What current developments are you most excited about?

It is becoming clear to me that it is very plausible that the brain developed a learning strategy analogous to back-propagation in order to estimate gradients of a training objective, thus addressing the very central question of credit assignment, which is also at the heart of the success of deep learning, in which many areas (layers in a deep neural network) are jointly trained in a coordinated way towards a common objective. Many questions remain open in the quest to bridge this gap between backprop and neuroscience, and we are approaching the time when the questions will be sufficiently precise to be investigated by experimentalists.
I am also very excited about recent developments in machine learning that connect back to older questions raised by classical AI and cognitive science regarding higher-level cognitive notions such as objects, agents, reasoning, memory and knowledge representation, bringing together the expertise in deep learning and in reinforcement learning. There is here again an opportunity for fruitful multi-disciplinary investigations, which could lead us towards a better understanding of cognition – going beyond the current success of deep learning for perception tasks.

5 What do you hope to learn at CCN 2017?

I am hoping to learn about new developments and views in cognitive computational neuroscience, since I do not follow that literature, and as I wrote above, I believe that the potential for positive interactions is quite high.