09:07:06 I actually thought each of us, we're doing this for each of them.
09:07:11 Fields. But it doesn't matter. So it's just because today is the first neurobiology sort session.
09:07:17 Just very quickly, so we've actually been talking a lot about how to keep this in their meta-level and try to discuss questions, big questions, general questions, and how to take this forward.
09:07:30 Actually Joseph and I were discussing this yesterday matter.
09:07:34 We kept you out of it because we were very offended that you prefer to be with your Korean friends, and we thought and how to consolidate what is a good way to consolidate what's happening here?
09:07:47 So that it doesn't become a day on its own isolated, and the next day you forget about what was done the previous day, and we don't have good solutions.
09:07:57 One thing we thought of is to have dinners where the theme of the day is discussed, and they can tell you an example of how not to do it. For example.
09:08:07 Yesterday we had a small dinner at my place. We didn't invite the sword, and then we didn't talk about size.
09:08:12 So this is one way, not to do it, but I'm sure it will increase the chances of us collaborating in the future in some way.
09:08:20 So just a quick overview. I think it's it's a really incredibly interesting time.
09:08:28 In systems, neuroscience. And there's been. And I think really dramatic improvement in how we sample data. All sorts of data.
09:08:39 And we've gone from the classical studies of Tollman and and Seward Bensik that we're looking at latent learning.
09:08:49 But now we can actually record, thanks to deep learning and machine learning, we can analyze the behavior of individuals and populations of animals.
09:08:59 We can also do as shown here on the right hand side, we can simultaneously sample large population, urinal population activity, while simultaneously recording whisker movement, pupil size, vocalizations, and so on.
09:09:15 So I think this has by accident in part increased the amount of information we care about latent learning, about what the animals are learning.
09:09:24 How about things that are not necessarily task related? And I and we will talk a lot about that.
09:09:33 And for me what would be very interesting is if we find a principled way of looking at statistical learning.
09:09:39 So we were saying, is statistical learning a useful concept.
09:09:43 Given that all learning is at some levels statistical. And so I don't know if this is a useful way of looking at it or not.
09:09:52 I would love to discuss it. Given the dimensions that imp Pynche on learning like, for example, the stimulus load, whether it's multimodal or uni-modal, the complexity, whether there is a reinforcement or not and so on how do these the quantification of this different
09:10:11 variety. How does it affects the type of learning, the type of plasticity, the structures that are involved, and whether we would find that there are some like, I've tried to indicate with a circles there that there is some way of or some levels?
09:10:30 Of of each of these variables that activate a different type of circuit, a different type of plasticity.
09:10:39 And and whether we can map this to then the brain different circuits in the brain have focused here on cortical and sensory cortices.
09:10:52 But of course, also hippocampal, higher order, cortices, and so on, and also, of course, with how this brain interacts with the environment as yet.
09:10:59 For example, just mention how the brain samples the activity around active sensing, and so on.
09:11:05 If you can think, or if you're interested in discussing principle ways of looking at this, I think it would be quite interesting just to give you an overview of what are the neurobiology themes that we will have.
09:11:18 We have Athena today talking on reinforcement.
09:11:22 Kishorea will talk about implicit learning, of of task structure.
09:11:31 You kidding? Oh, my God, okay, I'm sorry.
09:11:36 I thought I had copied it. Actually, II said.
09:11:46 But I thought I had copied this from the excel sheet when they're obviously not. I'm so sorry.
09:11:56 Okay. Right? Thanks. Anybody else. Yeah. Maraballa and Christina are not here to complain.
09:12:08 So I'll check, and I will check back. I apologize.
09:12:11 I'm really sorry Siad is going to talk about the interaction between sensory like motor contingencies and sensory sampling.
09:12:24 Simon Rumper is going to talk about the stability of representation and did I spell it correctly?
09:12:31 And my real-law is from the Sumatra sensory field.
09:12:36 We don't know yet in detail what he's going to be talking about, and, Christina, what you can read it.
09:12:42 So it's I think it's a very exciting set of themes that really touch upon many of these questions.
09:12:49 And this is something that we haven't actually mentioned, that we spend some time, Matthew, Joseph and I.
09:12:54 Discussing what would be themes that we would find interesting to discuss, and these were not necessarily themes that had to be the title of assaults.
09:13:04 Session, but that may be the thoughts or the audience could bring up or keep in mind while we're having this discussion, and there I can put this also in the Google top.
09:13:17 There very general themes that I think are very relevant for statistical learning.
09:13:22 For example, the question of chunking, what does chunking got to do with object detection, even with stream segregation?
09:13:31 And I think I shouldn't go through each of them.
09:13:33 But the second one is a favorite of mine. A neural feature, selectivity.
09:13:39 What is tuning, how stable it is so, for example, in the auditory system we know that frequency tuning is quite stable to the context unless there is plasticity, but in general it is relatively stable and relation to the context, while for example, intensity, coding is
09:13:55 incredibly context, sensitive and attuning to intensity, changes dramatically with the statistics of the context.
09:14:03 And I think this has a lot to do with representation.
09:14:05 How Its information represented, and how does the brain read this relatively unstable representation?
09:14:12 And I really do not want to go through each of them.
09:14:15 I think by now you probably have time to to read them, and I want to leave time for I mean, I'm really looking forward to hearing.
09:14:26 Okay, we should give you time to read them. I'm an actor.
09:14:35 His in particular, that I would love to mention. So we had the discussion about attention.
09:14:44 Your modulation, and supervised, or reinforcement learning, and whether they are related in any way, and how they are related.
09:14:54 Statistical Learning and Predictive coding. What is the relationships?
09:14:58 I think these are all themes that are embedded in Ashley.
09:15:02 Any of the questions that that we we are bringing up, and I think it would be good to think about the mass questions and try to get answers.
09:15:13 That's it. I have nothing else to say except give me the money.
09:15:19 Q.