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

There is a continued need for computational frameworks that interplay with experimental design and analysis at multiple levels (e.g., neurons, circuits, cognition). I have been intrigued by how neural systems represent and learn about stimuli in the natural environment, resulting in complex inferences and behavior. My main focus has been building computational neural models that push towards a more principled understanding for natural stimuli such as visual scenes. With advances in machine learning and in understanding the statistics of natural stimuli, I believe there is potential for progress in designing and interpreting experiments with naturalistic environments and tasks.

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

I am giving a tutorial on computational neuroscience. This is a broad field, spanning many levels and computational tools. I’ll expect to give examples from my focus area of vision. I will emphasize aspects that I think cross the boundaries to artificial intelligence and cognitive science – such as normative (why) models of neurons learned from statistical properties of scenes or task goals. I will also talk about descriptive (what) neural models from high dimensional biological data, and make some links to mechanistic (how) models.

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

Through collaboration and interdisciplinary meetings such as CCN! I think that models and computational approaches developed across these three areas can enrich one another. In addition, models developed in artificial intelligence can facilitate new experiments in neuroscience and cognitive science, as we have seen in recent years with deep neural networks. As in computational neuroscience and cognitive science, experiments can highlight model failures and limitations, and directions for improvement.

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

I am excited about advances in deep learning and combining these directions with models developed in computational neuroscience. I think there are also opportunities to utilize current developments in generating naturalistic stimuli across different layers of a hierarchy, for neuroscience and cognitive science.

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

New developments and interaction between these areas of computational neuroscience, cognitive science and artificial intelligence.

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.

(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).

1 How can we find out how the brain works?

As a cognitive scientist I normally think about this question from the perspective of Marr’s levels of analysis. Understanding how the brain works is a question at what Marr called the “implementation” level, but I think a lot of insight can be gained by asking why the brain does what it does — a question at Marr’s “computational” level. Between those levels of analysis is the “algorithmic” level, which looks at the particular cognitive processes that are involved in solving a problem. Over the last few years a lot of progress has been made at both the implementation level and the computational level, but I think the algorithmic level gets neglected when we think about the brain. Understanding the algorithms that brains execute — and how brains learn to execute those algorithms — is going to be a critical part of finding out how the brain works.

2 What will your talk at CCN 2017 be about?

I will talk about a formal framework for linking these different levels of analysis. Computational-level explanations focus on the structure of abstract problems and their ideal solutions, explaining behavior in terms of optimal solutions to the problems that we face in everyday life. However, finding those optimal solutions can be extremely computationally expensive. So, my collaborators and I have started to look at the consequences of introducing resource constraints: if you know you have to solve a problem using a particular computational architecture, with associated computational costs, how should you solve the problem? This “bounded optimality” approach pushes the principle of optimization down to the algorithmic level, and gives us a new set of tools for exploring some classic questions about human cognition.

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

I think these three approaches need to work together to give explanations of how the mind works all levels of analysis. Artificial intelligence can help us find good solutions to the kinds of problems that intelligence organisms face, providing the elements we need for constructing computational-level theories. Cognitive science puts these theories together with behavior to really understand the cognitive processes that people engage in. Computational neuroscience helps us understand how those cognitive processes could be executed by neurons. However, explanations at each of these levels can inform one another. For example, there is a productive feedback loop between AI and cognitive science, with AI leading to new theories of human cognition and cognitive science highlighting the ways in which AI still falls short. I think similar feedback loops can be constructed between all three disciplines.

4 What current developments are you most excited about?

The three things I am currently most excited about are understanding how people are so good at finding strategies for solving complex problems (something that is still beyond the limits of current AI), the potential for crowdsourcing technology to be transformative for the way that we conduct behavioral research (I think it is currently massively underutilized), and exploiting the advances in deep learning for representing complex stimuli such as images and text in order to study human cognition with naturalistic stimuli.

5 What do you hope to learn at CCN 2017?

I’m really interested in those feedback loops between different disciplines, so I hope to learn more about how machine learning and AI have been influencing contemporary neuroscience.

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.