1
00:00:00,000 --> 00:00:05,680
the way your brain works changes as you go from one state to another, right?

2
00:00:05,680 --> 00:00:08,360
From being totally zoned out, right?

3
00:00:08,360 --> 00:00:10,880
You know, the undergraduate in the back of the large lecture hall,

4
00:00:10,880 --> 00:00:12,960
maybe doing the head bob thing, right?

5
00:00:12,960 --> 00:00:16,600
Between like that state versus being really super focused and,

6
00:00:16,880 --> 00:00:20,120
and like paying close attention to something that's really important, right?

7
00:00:20,120 --> 00:00:24,760
You can feel that your brain is operating differently in different states.

8
00:00:25,080 --> 00:00:26,720
So we spent a long time looking at that.

9
00:00:26,720 --> 00:00:29,840
And then we thought, you know, we're missing half the signals

10
00:00:29,840 --> 00:00:31,160
or more in the brain.

11
00:00:32,960 --> 00:00:36,400
The human brain is the most complex structure in the known universe.

12
00:00:36,400 --> 00:00:40,840
And we are in the middle of a scientific revolution to understand its inner workings.

13
00:00:41,000 --> 00:00:44,120
Join us for a conversation with world renowned neuroscientists

14
00:00:44,120 --> 00:00:45,720
as they visit Rochester.

15
00:00:45,720 --> 00:00:49,400
I am Dr. John Foxe, director of the Del Monte Institute for Neuroscience

16
00:00:49,400 --> 00:00:51,120
at the University of Rochester.

17
00:00:51,120 --> 00:00:53,880
And you are listening to Neuroscience Perspectives.

18
00:00:53,880 --> 00:00:57,400
Hi, I'm John Fox, director of the Del Monte Institute for Neuroscience

19
00:00:57,400 --> 00:00:58,800
at the University of Rochester.

20
00:00:58,800 --> 00:01:02,480
And I'd like to welcome you to another episode of Neuroscience Perspectives.

21
00:01:02,480 --> 00:01:06,480
Today, I have a fantastic guest with us, Dr. Jessica Cardin,

22
00:01:06,480 --> 00:01:10,720
who's an associate professor of neuroscience at Yale University School of Medicine.

23
00:01:10,720 --> 00:01:13,200
Jess, welcome to Neuroscience Perspectives.

24
00:01:13,200 --> 00:01:14,920
Thank you so much for having me.

25
00:01:14,920 --> 00:01:17,000
Well, we really appreciate you being here.

26
00:01:17,000 --> 00:01:21,600
So you really, you know, you're sort of pioneer of studying circuit dynamics.

27
00:01:21,600 --> 00:01:26,800
And kind of a scavenger of techniques to get at that, which is just fantastic.

28
00:01:26,800 --> 00:01:28,200
I really like that, yes.

29
00:01:28,200 --> 00:01:31,400
Just, you know, if there's a technique out there that can help you get after something,

30
00:01:31,400 --> 00:01:33,920
that's sort of just looking at the work that you've been doing.

31
00:01:33,920 --> 00:01:36,560
It's like you're going to get it and make it make it work for you.

32
00:01:36,560 --> 00:01:38,560
And I think that's really admirable.

33
00:01:38,560 --> 00:01:40,840
But yeah, what gets you up in the morning?

34
00:01:40,840 --> 00:01:43,040
What are you excited about at the moment?

35
00:01:43,040 --> 00:01:48,200
Oh, my gosh, we have diversified so much in the last few years.

36
00:01:48,200 --> 00:01:53,800
The real truth is that I get bored after four or five years of doing any one thing.

37
00:01:53,800 --> 00:01:59,200
So every few years, I think it's partly that I get a little restless and we do something new,

38
00:01:59,200 --> 00:02:03,000
or someone new comes to the lab and has a different set of ideas.

39
00:02:03,000 --> 00:02:05,920
And then we go off in a completely different direction than I was anticipating.

40
00:02:05,920 --> 00:02:12,440
So sometimes that takes us in directions that I would not have predicted.

41
00:02:12,440 --> 00:02:20,240
But right now, you know, we had this long series of experiments where we were really trying

42
00:02:20,240 --> 00:02:30,480
to deeply understand, you know, how the way your brain works changes as you go from one state

43
00:02:30,480 --> 00:02:35,520
to another, right, from being totally zoned out, right, you know, the undergraduate in the back

44
00:02:35,520 --> 00:02:39,800
of the large lecture hall, maybe doing the head bob thing, right, between like that,

45
00:02:39,800 --> 00:02:44,840
versus being really super focused and, and like paying close attention to something that's

46
00:02:44,840 --> 00:02:49,920
really important, right, you can feel that your brain is operating differently in different

47
00:02:49,920 --> 00:02:50,920
states.

48
00:02:50,920 --> 00:02:52,400
So we spent a long time looking at that.

49
00:02:52,400 --> 00:02:57,120
And then we thought, you know, we're missing half the signals or more in the brain.

50
00:02:57,120 --> 00:03:02,360
There are all we're only looking at neurons, the firing of neurons, we're like, we're not

51
00:03:02,360 --> 00:03:05,080
looking at any of the other relevant signals.

52
00:03:05,080 --> 00:03:10,680
And so we tell our audience what those other signals are that right.

53
00:03:10,680 --> 00:03:15,800
So, you know, so there are all of these neurotransmitters floating around, right?

54
00:03:15,800 --> 00:03:19,520
Sometimes, you know, in the shorthand for this is neuroscientists, sometimes that your

55
00:03:19,520 --> 00:03:24,680
brain is sort of swimming in a soup of neurotransmitters and other chemicals that are that are, you

56
00:03:24,680 --> 00:03:26,880
know, floating around and being released all the time.

57
00:03:26,880 --> 00:03:31,480
And some of them are pretty familiar, I think, to most people, things like acetylcholine

58
00:03:31,480 --> 00:03:35,320
or especially norepinephrine, right, those are things that you hear about often.

59
00:03:35,320 --> 00:03:42,120
And they're, they're frequently targets of, you know, therapeutic drugs for psychiatric

60
00:03:42,120 --> 00:03:46,760
conditions, you know, because we know that when they're released, they modify brain activity.

61
00:03:46,760 --> 00:03:47,760
Right.

62
00:03:47,760 --> 00:03:52,600
And, and we know that they're associated with things like changes in your mood or changes

63
00:03:52,600 --> 00:03:55,120
in your attention levels.

64
00:03:55,120 --> 00:03:56,120
Right.

65
00:03:56,120 --> 00:03:58,440
And so we thought, ah, we are missing all of these signals.

66
00:03:58,440 --> 00:04:02,200
And so now we've developed a whole series of approaches in the lab where we can look

67
00:04:02,200 --> 00:04:06,120
at the neurons, right, the brain cells, and we also can see all those other signals at

68
00:04:06,120 --> 00:04:07,120
the same time.

69
00:04:07,120 --> 00:04:08,120
Right, right.

70
00:04:08,120 --> 00:04:09,120
All right.

71
00:04:09,120 --> 00:04:12,040
And then, you know, in parallel to that, we also started thinking about, well, wait, there

72
00:04:12,040 --> 00:04:15,360
are other cell types, like we're only looking at one cell type, you know, maybe we should

73
00:04:15,360 --> 00:04:19,120
be looking at these other cell types that are in the brain that do all these other functions,

74
00:04:19,120 --> 00:04:23,720
you know, that may be related to some of these changes in brain function over time.

75
00:04:23,720 --> 00:04:28,360
And so that took us to looking at these other cell types that are in the brain that do all

76
00:04:28,360 --> 00:04:29,360
these other functions over time.

77
00:04:29,360 --> 00:04:30,360
And so we started looking at non-neuronal cells.

78
00:04:30,360 --> 00:04:31,360
Right.

79
00:04:31,360 --> 00:04:36,360
So we just now, for the first time, have projects starting up looking at non-neuronal cells,

80
00:04:36,360 --> 00:04:42,360
which is super exciting, right, because, you know, it's a, it's a whole side of, of the

81
00:04:42,360 --> 00:04:45,360
brain that, you know, I've never really thought about.

82
00:04:45,360 --> 00:04:46,360
Yeah.

83
00:04:46,360 --> 00:04:47,360
Right.

84
00:04:47,360 --> 00:04:51,360
And all to the end of sort of decision-making processes or motivated behavior.

85
00:04:51,360 --> 00:04:52,360
Yeah.

86
00:04:52,360 --> 00:04:53,360
Yeah, exactly.

87
00:04:53,360 --> 00:04:54,360
Yeah.

88
00:04:54,360 --> 00:04:55,360
It's, it's an interesting thing.

89
00:04:55,360 --> 00:04:56,360
You're getting all of these signals.

90
00:04:56,360 --> 00:05:00,480
So, so maybe a philosophical question and, and, you know, I think a lot of people in

91
00:05:00,480 --> 00:05:04,920
the sciences quite would laugh at this, like, you know, if we, if we get a bit, if we, if

92
00:05:04,920 --> 00:05:09,160
we could measure every single neuron in the brain and every neurotransmitter system and

93
00:05:09,160 --> 00:05:13,640
arousal system and all the rest of it, and we were able to gather all that information,

94
00:05:13,640 --> 00:05:16,160
would we, would we get to new understanding?

95
00:05:16,160 --> 00:05:18,960
Is it, is it, or would we have recreated the monster?

96
00:05:18,960 --> 00:05:19,960
Right.

97
00:05:19,960 --> 00:05:20,960
Yeah.

98
00:05:20,960 --> 00:05:24,520
I mean, do you need something even more complex than the brain in order to be able to understand

99
00:05:24,520 --> 00:05:26,080
the full workings of the brain?

100
00:05:26,080 --> 00:05:27,080
Right.

101
00:05:27,080 --> 00:05:29,720
This is the thing that keeps teenagers up at night, you know, like staring at the ceiling

102
00:05:29,720 --> 00:05:30,720
and stuff.

103
00:05:30,720 --> 00:05:32,920
Like, how does my brain work?

104
00:05:32,920 --> 00:05:35,280
We have, we have sort of parallel problems.

105
00:05:35,280 --> 00:05:36,280
Right.

106
00:05:36,280 --> 00:05:39,200
And this is, this was the start of the Brain Initiative actually with the NIH.

107
00:05:39,200 --> 00:05:40,200
Right.

108
00:05:40,200 --> 00:05:44,600
This is the, the Brain Initiative, which has funded an enormous amount of spectacular technology

109
00:05:44,600 --> 00:05:49,120
development and new ways of accessing brain circuits.

110
00:05:49,120 --> 00:05:50,280
Right.

111
00:05:50,280 --> 00:05:55,480
Started because of this idea that if you could just measure everything, you can measure all

112
00:05:55,480 --> 00:05:59,320
the neurons, right, you would really understand the system.

113
00:05:59,320 --> 00:06:04,560
And that has given rise, I think, to this enormous upswing in big data in neuroscience,

114
00:06:04,560 --> 00:06:05,560
right?

115
00:06:05,560 --> 00:06:06,560
Get in there and record everything.

116
00:06:06,560 --> 00:06:07,560
Right.

117
00:06:07,560 --> 00:06:08,560
Yeah.

118
00:06:08,560 --> 00:06:10,760
And then you, so, so we have this one problem, which is like, how do you do that?

119
00:06:10,760 --> 00:06:11,760
Right.

120
00:06:11,760 --> 00:06:13,640
So we need new technical approaches and that's been really productive.

121
00:06:13,640 --> 00:06:14,640
Right.

122
00:06:14,640 --> 00:06:15,640
We have all kinds of new approaches for this.

123
00:06:15,640 --> 00:06:19,240
You know, you can, you can record all kinds of things, but you have a parallel problem,

124
00:06:19,240 --> 00:06:23,440
which is what, what do we do with this enormous pile of data?

125
00:06:23,440 --> 00:06:24,440
Right.

126
00:06:24,440 --> 00:06:26,040
And how do you turn it into human understanding?

127
00:06:26,040 --> 00:06:27,040
Yeah.

128
00:06:27,040 --> 00:06:28,040
Something that we can.

129
00:06:28,040 --> 00:06:29,040
Biological interpretability.

130
00:06:29,040 --> 00:06:30,040
Right.

131
00:06:30,040 --> 00:06:33,360
Like how do we, how do we distill this into something that I can actually tell you that

132
00:06:33,360 --> 00:06:35,680
gives you intuition about how the brain works?

133
00:06:35,680 --> 00:06:36,680
Right.

134
00:06:36,680 --> 00:06:37,680
Exactly.

135
00:06:37,680 --> 00:06:38,680
Yeah.

136
00:06:38,680 --> 00:06:39,680
And that's a much bigger problem as it turns out.

137
00:06:39,680 --> 00:06:43,080
And, you know, people are solving that in different ways.

138
00:06:43,080 --> 00:06:48,280
And I think, you know, we've spent some time in our own group and in collaboration with

139
00:06:48,280 --> 00:06:53,520
Mike Higley's lab, with whom we share a lot of projects and equipment and stuff, developing

140
00:06:53,520 --> 00:06:59,360
new analytical techniques, new math, basically, like playing around with some math.

141
00:06:59,360 --> 00:07:04,800
But also now we've kind of branched out into machine learning approaches and other, other

142
00:07:04,800 --> 00:07:10,740
model based approaches that can help us take this incredibly complex series of signals

143
00:07:10,740 --> 00:07:14,360
and try to distill it down into what are the important bits?

144
00:07:14,360 --> 00:07:18,760
That, you know, this, I'm really interested in this idea of taking what is very, very

145
00:07:18,760 --> 00:07:25,600
complex material data, doing some dimensionality reduction on it to turn it into something

146
00:07:25,600 --> 00:07:28,280
that a human being can actually wrap their mind around.

147
00:07:28,280 --> 00:07:32,200
But then the next step, of course, is one thing to have your training and to be able

148
00:07:32,200 --> 00:07:35,240
to understand these concepts because of your training.

149
00:07:35,240 --> 00:07:36,240
Right.

150
00:07:36,240 --> 00:07:39,080
But then to communicate that to the public who pay the bills.

151
00:07:39,080 --> 00:07:40,080
Yeah.

152
00:07:40,080 --> 00:07:42,080
Does that keep you up at night too?

153
00:07:42,080 --> 00:07:43,080
Or do you worry about that?

154
00:07:43,080 --> 00:07:50,480
I asked you in the context of a time, like a time, geopolitical time where the public

155
00:07:50,480 --> 00:07:56,280
trust in science, which used to be extraordinary, you know, 95 percent of people in the early

156
00:07:56,280 --> 00:08:00,800
naughties, you know, just thought, you know, had profound trust in science.

157
00:08:00,800 --> 00:08:04,680
And, you know, we come to a pandemic and we've lost our audience a little bit.

158
00:08:04,680 --> 00:08:09,080
You know, there's a, people have sown a lot of doubt and so on.

159
00:08:09,080 --> 00:08:10,080
Yeah.

160
00:08:10,080 --> 00:08:17,400
And I think my perception of some of what's gone wrong, I think, is in how we all think

161
00:08:17,400 --> 00:08:22,480
and communicate about what the scale of the problem and the unknowns is.

162
00:08:22,480 --> 00:08:28,560
You know, we've been, you know, as a field, right, neuroscience in the sense of putting

163
00:08:28,560 --> 00:08:35,640
a lecture in someone's brain has existed since maybe the 1920s, let's say, right, where the

164
00:08:35,640 --> 00:08:42,440
first recordings were made of patients, right, you know, showing changes in brain activity

165
00:08:42,440 --> 00:08:45,640
with changes in cognitive function, right.

166
00:08:45,640 --> 00:08:50,440
So you ask patients to do a task, a math problem, and see a change in brain activity, right.

167
00:08:50,440 --> 00:08:52,840
That's dates to the 1920s.

168
00:08:52,840 --> 00:08:55,860
So basically it's been about a hundred years, right.

169
00:08:55,860 --> 00:08:58,720
And you would think that in a hundred years we might have solved everything.

170
00:08:58,720 --> 00:09:01,600
But the truth is that it's so complex, right.

171
00:09:01,600 --> 00:09:06,320
I mean, it is fascinating and complex and it's an enormous problem, right.

172
00:09:06,320 --> 00:09:09,040
Like, we just don't, we don't have all the answers, right.

173
00:09:09,040 --> 00:09:15,120
And I think it's a mistake to suggest that we are so competent, so good.

174
00:09:15,120 --> 00:09:20,040
We're just so smart that we're going to solve the brain now, right.

175
00:09:20,040 --> 00:09:22,560
We're probably going to be working on this for hundreds of years, right.

176
00:09:22,560 --> 00:09:26,080
We, you know, I would give it another century, right.

177
00:09:26,080 --> 00:09:29,600
You know, it's an enormous challenge, right.

178
00:09:29,600 --> 00:09:33,440
So I think some of our, some of it's our fault for not communicating what the hell we're

179
00:09:33,440 --> 00:09:35,160
doing, right.

180
00:09:35,160 --> 00:09:42,620
You know, and, and then some of it is also this, this idea that, you know, we should

181
00:09:42,620 --> 00:09:45,600
be only focusing on disease, right.

182
00:09:45,600 --> 00:09:51,280
Because I, in my mind, I think the basic research that how does the thing work should go hand

183
00:09:51,280 --> 00:09:54,320
in hand with how do I fix it when it's broken?

184
00:09:54,320 --> 00:09:56,000
You just anticipated two of my questions.

185
00:09:56,000 --> 00:09:58,680
One of them, because I really wanted to, I wanted to pivot to that because I think that's

186
00:09:58,680 --> 00:10:04,240
a super important point, which is, you know, just studying something for the sake of the

187
00:10:04,240 --> 00:10:10,080
knowledge of how it works and not having to have the filter of utility in a disease or

188
00:10:10,080 --> 00:10:11,320
something down the road.

189
00:10:11,320 --> 00:10:15,440
But you do think about disease in your work and, and we do.

190
00:10:15,440 --> 00:10:16,440
Yeah.

191
00:10:16,440 --> 00:10:20,400
We used to work primarily on neurodevelopmental disorders, right.

192
00:10:20,400 --> 00:10:26,320
So autism, schizophrenia, and in my lab, because most of what we do is fairly reductionist,

193
00:10:26,320 --> 00:10:27,320
right.

194
00:10:27,320 --> 00:10:29,680
And then they're in the circuits and manipulating things.

195
00:10:29,680 --> 00:10:33,160
It's mostly animal models of genetic diseases, right.

196
00:10:33,160 --> 00:10:37,400
So specific genetic mutations that are known to be associated with a disease.

197
00:10:37,400 --> 00:10:41,520
And in our goal is really to try to understand what are the consequences of that genetic

198
00:10:41,520 --> 00:10:42,960
mutation, right.

199
00:10:42,960 --> 00:10:49,600
And not necessarily at the level of expression of a, of a gene, but rather it's more holistic

200
00:10:49,600 --> 00:10:52,960
consequences for how does that change the way the brain works.

201
00:10:52,960 --> 00:10:54,200
Right.

202
00:10:54,200 --> 00:11:00,880
And then in recent years, we sort of kind of fell a little bit into the other end of

203
00:11:00,880 --> 00:11:02,080
things, if you will.

204
00:11:02,080 --> 00:11:05,960
So aging and neurodegeneration.

205
00:11:05,960 --> 00:11:12,280
And there we were looking at several models of Alzheimer's disease.

206
00:11:12,280 --> 00:11:17,960
And in part, because the same sort of thing I said earlier, you know, we, we kind of fell

207
00:11:17,960 --> 00:11:23,100
into a couple of interesting ideas and then realized that there were interesting questions

208
00:11:23,100 --> 00:11:24,880
that we hadn't answered yet.

209
00:11:24,880 --> 00:11:29,920
And you know, we started doing these longitudinal studies where we're imaging individual animals

210
00:11:29,920 --> 00:11:32,360
as they age, you know, which is really fascinating.

211
00:11:32,360 --> 00:11:36,400
It generates an enormous amount of data, which we're so grappling with.

212
00:11:36,400 --> 00:11:40,880
But you know, there are multiple ways to look at the disease models, right.

213
00:11:40,880 --> 00:11:42,760
One of them is that they're a tool, right.

214
00:11:42,760 --> 00:11:47,200
So it is a targeted way of breaking the system, right.

215
00:11:47,200 --> 00:11:50,800
And then by perturbing it, you understand a little bit better how it worked when it

216
00:11:50,800 --> 00:11:51,800
was working.

217
00:11:51,800 --> 00:11:52,800
Right.

218
00:11:52,800 --> 00:11:57,440
And another way to look at the disease model work is that these are all very complicated

219
00:11:57,440 --> 00:11:59,680
disorders, right.

220
00:11:59,680 --> 00:12:03,320
And they, they don't have autism is a great example.

221
00:12:03,320 --> 00:12:06,800
They're like a thousand autism related genes, right.

222
00:12:06,800 --> 00:12:10,120
And they do wildly different cell biological things.

223
00:12:10,120 --> 00:12:18,140
And we at a fundamental level do not understand how genetic perturbation of genes that do

224
00:12:18,140 --> 00:12:23,280
completely different things at the level of a single cell could give rise to something

225
00:12:23,280 --> 00:12:26,640
that we perceive as being a common end point.

226
00:12:26,640 --> 00:12:30,320
We perceive all of those consequences as having something in common.

227
00:12:30,320 --> 00:12:33,800
It's all under the umbrella of autism spectrum disorder.

228
00:12:33,800 --> 00:12:34,800
Right.

229
00:12:34,800 --> 00:12:40,200
So that, that lack of understanding really highlights our lack of knowledge about the

230
00:12:40,200 --> 00:12:43,040
basics of how the brain really works.

231
00:12:43,040 --> 00:12:48,040
And this kind of idea too with diseases, like you mentioned, autism and schizophrenia, we're

232
00:12:48,040 --> 00:12:49,720
quite a lot of those genes.

233
00:12:49,720 --> 00:12:51,960
The two diseases have in common, not all of them.

234
00:12:51,960 --> 00:12:52,960
Yeah, absolutely.

235
00:12:52,960 --> 00:12:57,960
And yet, you know, perturbations in the genetics will send one person on a trajectory that

236
00:12:57,960 --> 00:13:03,840
ends up with an autism phenotype, which is really very distinct from schizophrenia phenotype.

237
00:13:03,840 --> 00:13:05,080
And so yeah, we've a lot to learn.

238
00:13:05,080 --> 00:13:06,080
We have a lot to learn.

239
00:13:06,080 --> 00:13:07,080
Yeah.

240
00:13:07,080 --> 00:13:09,840
There is just an enormous amount of unknown there, right.

241
00:13:09,840 --> 00:13:14,600
And that's kind of why I feel like we do a bad job of communicating the scale of the

242
00:13:14,600 --> 00:13:15,600
challenge.

243
00:13:15,600 --> 00:13:16,600
Right.

244
00:13:16,600 --> 00:13:17,600
You know, it's a...

245
00:13:17,600 --> 00:13:21,560
You said there as well, I, you know, we'll probably be doing it for another hundred years.

246
00:13:21,560 --> 00:13:23,280
I don't, I don't doubt that that's true.

247
00:13:23,280 --> 00:13:29,320
And a question that was in my mind is like, you know, you're in the lab like myself, you

248
00:13:29,320 --> 00:13:32,080
know, and you're thinking about next year and the year after and that, but if you were

249
00:13:32,080 --> 00:13:35,560
to project that 10 years, like where do you, where do you see us in 10 years?

250
00:13:35,560 --> 00:13:38,200
Is it an impossible question?

251
00:13:38,200 --> 00:13:41,480
It is.

252
00:13:41,480 --> 00:13:48,020
So my part of the field, my little chunk of the field, right, is racing towards a much

253
00:13:48,020 --> 00:13:53,080
more model-based, high throughput, high dimensional data set kind of approach.

254
00:13:53,080 --> 00:13:54,080
Right.

255
00:13:54,080 --> 00:14:00,540
So we are kind of converging on multimodal data with lots of different signals and needing

256
00:14:00,540 --> 00:14:06,060
those, those more model-based approaches to help us make sense of things.

257
00:14:06,060 --> 00:14:11,320
But so we're sort of, I think a lot of the field is moving towards the more holistic

258
00:14:11,320 --> 00:14:15,040
view of how things are working, how many different signals or parts of the brain are interacting

259
00:14:15,040 --> 00:14:16,040
at the same time.

260
00:14:16,040 --> 00:14:20,240
And I, I'm, and you mentioned machine learning has a big component of this because you start

261
00:14:20,240 --> 00:14:25,000
to take these extraordinary quantities of data that are from different systems, you

262
00:14:25,000 --> 00:14:29,800
know, on different timescales and try to put them into one matrix and get some, derive

263
00:14:29,800 --> 00:14:30,800
some understanding.

264
00:14:30,800 --> 00:14:35,160
And this is the part where it's difficult for a human brain to grapple with all that.

265
00:14:35,160 --> 00:14:38,800
So yeah, I mean, and so I see a lot of that coming, you know, I think there's going to

266
00:14:38,800 --> 00:14:43,040
be more and more of that, which is, you know, great because it gives us that more holistic

267
00:14:43,040 --> 00:14:45,680
understanding comes with some caveats.

268
00:14:45,680 --> 00:14:51,640
Like you still need to go back again and get that biological interpretability at the other

269
00:14:51,640 --> 00:14:52,640
side.

270
00:14:52,640 --> 00:14:57,480
So, so big role for, for the AI revolution that we're really in the, in the middle of,

271
00:14:57,480 --> 00:15:00,360
you think in helping to understand this or?

272
00:15:00,360 --> 00:15:03,400
It's a great set of tools in combination with all the other tools.

273
00:15:03,400 --> 00:15:06,720
I, I am, I'm a biologist at heart.

274
00:15:06,720 --> 00:15:13,160
In fact, I'm actually a sort of old school, you know, crusty old electrophysiologist at

275
00:15:13,160 --> 00:15:14,160
heart.

276
00:15:14,160 --> 00:15:20,320
So in, so really, truly I want to see ground truth data at the end of the day.

277
00:15:20,320 --> 00:15:22,080
So for me, it has to come full circle, right?

278
00:15:22,080 --> 00:15:25,960
I love, I love all these tools, but at the end of the day, I want to be able to say I

279
00:15:25,960 --> 00:15:32,200
gained a mechanistic level of insight into how the circuits in the brain are working.

280
00:15:32,200 --> 00:15:34,640
So tell me when you, when did you catch the science bug?

281
00:15:34,640 --> 00:15:37,680
Like, was that, was that baked in early?

282
00:15:37,680 --> 00:15:39,280
It actually was.

283
00:15:39,280 --> 00:15:46,320
I was very fortunate to have, you know, parents who love to, you know, follow their kids'

284
00:15:46,320 --> 00:15:50,720
passions, you know, and explore things and are really supportive.

285
00:15:50,720 --> 00:15:58,120
And so, you know, I got interested in looking at things in pond water and my parents, you

286
00:15:58,120 --> 00:16:03,640
know, took us out to the local pond and we had water samples and, you know, put hay in

287
00:16:03,640 --> 00:16:07,720
some of them and not in others and looked to see what creatures developed and, you know,

288
00:16:07,720 --> 00:16:12,520
had a little microscope and, you know, so you're an experimentist from the, you know,

289
00:16:12,520 --> 00:16:17,680
but, but with a lot of encouragement, you know, to, to keep exploring, you know, and

290
00:16:17,680 --> 00:16:19,480
that was your parents where you had major influence.

291
00:16:19,480 --> 00:16:21,000
Where did you grow up?

292
00:16:21,000 --> 00:16:23,040
A little bit of everywhere actually.

293
00:16:23,040 --> 00:16:24,040
Yeah.

294
00:16:24,040 --> 00:16:26,200
Originally from Poughkeepsie, New York.

295
00:16:26,200 --> 00:16:32,760
So I'm fond of Upstate, but we moved every few years for a while because my dad worked

296
00:16:32,760 --> 00:16:33,760
for IBM actually.

297
00:16:33,760 --> 00:16:39,840
And so I've lived in Poughkeepsie and I've lived in Dallas, Dallas in the late eighties,

298
00:16:39,840 --> 00:16:42,040
which was fabulous.

299
00:16:42,040 --> 00:16:43,040
And then in Boulder, Colorado.

300
00:16:43,040 --> 00:16:44,040
Very good.

301
00:16:44,040 --> 00:16:45,960
Dallas in the late eighties.

302
00:16:45,960 --> 00:16:48,400
Was the TV show still running with JR?

303
00:16:48,400 --> 00:16:49,400
Yeah.

304
00:16:49,400 --> 00:16:53,200
And, and, and no lie, it was all big hair and, and pickup trucks.

305
00:16:53,200 --> 00:16:55,880
So it was, it was an interesting transition.

306
00:16:55,880 --> 00:16:56,880
Fantastic.

307
00:16:56,880 --> 00:17:01,240
But, but you know, sort of fortuitous for me as well, because I landed in the end in

308
00:17:01,240 --> 00:17:06,040
a school there that had a very strong science program and encouraged all the kids to do

309
00:17:06,040 --> 00:17:08,400
science projects for the local science fairs.

310
00:17:08,400 --> 00:17:09,400
Right.

311
00:17:09,400 --> 00:17:11,600
And so, because it was an expectation, right?

312
00:17:11,600 --> 00:17:12,840
Like everybody did this.

313
00:17:12,840 --> 00:17:13,840
Right.

314
00:17:13,840 --> 00:17:19,880
You know, we got into doing science fair projects and, you know, and so I think it, it sort

315
00:17:19,880 --> 00:17:23,640
of carried that thread through a little bit, you know.

316
00:17:23,640 --> 00:17:28,040
Tell me about mentorship then, like along the, along the road, right.

317
00:17:28,040 --> 00:17:32,040
You pick up people and there are people who have a major impact on, on the trajectory.

318
00:17:32,040 --> 00:17:36,160
Is there one or two people that you'd point to specifically and times in your life where,

319
00:17:36,160 --> 00:17:39,520
where, you know, this really made a big impact on you?

320
00:17:39,520 --> 00:17:40,520
Yeah, absolutely.

321
00:17:40,520 --> 00:17:49,040
I mean, a lot of it came from, you know, the sort of early mentorship, you know, in college

322
00:17:49,040 --> 00:17:55,760
in, you know, not even, not even in a continuous sense, but in like key moments where, you

323
00:17:55,760 --> 00:18:00,560
know, people take you into their lab and give you a, you know, a spot in the lab and a project

324
00:18:00,560 --> 00:18:04,200
to work on, you know, and then encourage you to keep going.

325
00:18:04,200 --> 00:18:11,660
And for me, grad school was amazing, you know, because you end up with not just one mentor,

326
00:18:11,660 --> 00:18:14,560
but a whole team of people in the end that you interact with.

327
00:18:14,560 --> 00:18:19,360
And they may not all agree with each other in the end or have different mentoring strategies,

328
00:18:19,360 --> 00:18:22,720
but you can kind of pick and choose a little bit, you know, the things that work for you.

329
00:18:22,720 --> 00:18:23,720
Right, right.

330
00:18:23,720 --> 00:18:28,120
And through those experiences, have you, have you picked up a specific mentoring style yourself?

331
00:18:28,120 --> 00:18:33,160
Is there something that you bring or that you'd say this is, this is a key component?

332
00:18:33,160 --> 00:18:39,760
I think, you know, thus far in my experience, you know, so I've been at Yale for 14 years

333
00:18:39,760 --> 00:18:41,980
or so.

334
00:18:41,980 --> 00:18:43,600
Every student is different, right?

335
00:18:43,600 --> 00:18:48,980
You know, and I don't, I think if I have a mentoring style, it might be mostly centered

336
00:18:48,980 --> 00:18:56,200
around being very patient, you know, very, trying to be very, very tolerant of the differences

337
00:18:56,200 --> 00:19:02,520
between other people, how their styles might, might interact with others in the lab group

338
00:19:02,520 --> 00:19:03,520
or with me.

339
00:19:03,520 --> 00:19:09,880
You know, I, I try not to impose too many of my own, the expectations I had for myself,

340
00:19:09,880 --> 00:19:14,440
I try not to impose them on my students because their style and their, their learning style

341
00:19:14,440 --> 00:19:17,840
might be different from mine or their goals in life might be different from mine.

342
00:19:17,840 --> 00:19:24,960
So yeah, I love that answer in, I often get asked the same thing and I say like, you know,

343
00:19:24,960 --> 00:19:29,720
people in the lab are just human beings, like the human beings in your world, in your life,

344
00:19:29,720 --> 00:19:33,360
and they come in all shapes and sizes and you have to figure out a way to fit around

345
00:19:33,360 --> 00:19:34,360
that.

346
00:19:34,360 --> 00:19:40,000
Yeah, everybody has a real life too, you know, it isn't, it isn't science 100% of the time,

347
00:19:40,000 --> 00:19:45,360
although sometimes it is like, I have trouble turning off the science in my head.

348
00:19:45,360 --> 00:19:53,160
But you know, people come to the lab and they have had a bad day or they have, you know,

349
00:19:53,160 --> 00:19:57,920
a family crisis or, you know, or something else is more important that day than, you

350
00:19:57,920 --> 00:19:59,200
know, this one experiment.

351
00:19:59,200 --> 00:20:02,400
And you know, that's, that's a real thing.

352
00:20:02,400 --> 00:20:06,560
It's not just science, it's also everybody's real life too.

353
00:20:06,560 --> 00:20:08,000
Now we can't close this interview.

354
00:20:08,000 --> 00:20:12,640
We have a few more minutes, I think, without talking about your laundry room and the three

355
00:20:12,640 --> 00:20:17,400
blind mice and the nine mice, you have to tell that story.

356
00:20:17,400 --> 00:20:22,600
What went on there and how did that happen and how did your mother tolerate this?

357
00:20:22,600 --> 00:20:25,200
It was partly their idea, my parents right now.

358
00:20:25,200 --> 00:20:30,840
This is again back to high school science fairs, you know, and I had, we actually done

359
00:20:30,840 --> 00:20:36,120
some science fairs using pet guinea pigs in the years prior to this.

360
00:20:36,120 --> 00:20:41,280
So I had guinea pigs and we, we had done learning and memory experiments, you know, with like,

361
00:20:41,280 --> 00:20:45,200
and they learned to associate a cue with a food reward and that kind of thing.

362
00:20:45,200 --> 00:20:47,240
And so what age are you now?

363
00:20:47,240 --> 00:20:51,560
This, so sort of in like eighth and ninth grade.

364
00:20:51,560 --> 00:20:59,200
So preteen, you know, pretty, you know, somewhere 12, 12 to 13.

365
00:20:59,200 --> 00:21:06,800
Yeah, young, you know, and, and I, I had wanted, you know, to do another science fair project

366
00:21:06,800 --> 00:21:07,800
like this.

367
00:21:07,800 --> 00:21:12,080
So my parents helped me find a researcher to local university.

368
00:21:12,080 --> 00:21:19,320
This was Southern Methodist, you know, in Dallas and a very kind researcher who gave

369
00:21:19,320 --> 00:21:23,320
me a set of mice, lab mice to work with, which we would never do now, right?

370
00:21:23,320 --> 00:21:26,520
Like, you know, sending lab mice home to some kid's house.

371
00:21:26,520 --> 00:21:32,120
So we took the mice home, had a group of males and a group of females, and I was interested

372
00:21:32,120 --> 00:21:33,920
in sex differences and learning and memory.

373
00:21:33,920 --> 00:21:35,760
And so we had a group of males and a group of females.

374
00:21:35,760 --> 00:21:40,320
We kept them in the laundry room for a while and I would run them on this T maze based

375
00:21:40,320 --> 00:21:43,120
task where there was a cue.

376
00:21:43,120 --> 00:21:47,520
And I also learned a bit about programming because this is the, we, we had to collect

377
00:21:47,520 --> 00:21:51,120
all the behavioral data and then do statistics on it and everything.

378
00:21:51,120 --> 00:21:58,160
So, and, and then even in Texas, it gets cold at night sometimes and the laundry room got

379
00:21:58,160 --> 00:22:01,000
very cold one night and we had a whole bunch of mice with hypothermia.

380
00:22:01,000 --> 00:22:08,480
And so my mom would tell you that she spent hours resuscitating hypothermic mice.

381
00:22:08,480 --> 00:22:10,120
Was the hairdryer involved?

382
00:22:10,120 --> 00:22:13,120
I think just towels.

383
00:22:13,120 --> 00:22:17,720
And you know, but in the end it was, it was actually a really cool data set.

384
00:22:17,720 --> 00:22:23,520
You know, we did great statistics and I learned all about Chi-square tests and like, you know,

385
00:22:23,520 --> 00:22:26,760
and we had this sort of longitudinal learning data for males and females.

386
00:22:26,760 --> 00:22:31,600
It turns out the males were better and better at learning the task.

387
00:22:31,600 --> 00:22:37,680
And you know, and it was, you know, you could look at that and say that has all the parts

388
00:22:37,680 --> 00:22:40,880
of a, the same thing that I do with my grad students now.

389
00:22:40,880 --> 00:22:44,960
Let me ask you one last thing before we, before we stop.

390
00:22:44,960 --> 00:22:49,200
You know, we, a lot of people who tune into neuroscience perspectives are youngsters and

391
00:22:49,200 --> 00:22:53,440
young grad students or, you know, college students thinking about getting into a career

392
00:22:53,440 --> 00:22:54,440
in science.

393
00:22:54,440 --> 00:22:59,880
Would you have, do you have some gems, surprise of wisdom, some guidance?

394
00:22:59,880 --> 00:23:00,960
I love my job.

395
00:23:00,960 --> 00:23:04,400
This is a, this is so much fun, right?

396
00:23:04,400 --> 00:23:11,760
It's, it's, it's a, it's a profession or a career for people who love to play, right?

397
00:23:11,760 --> 00:23:17,680
Love to think about ideas and be creative and play with the science.

398
00:23:17,680 --> 00:23:23,040
It has room for people who come from different backgrounds with different perspectives.

399
00:23:23,040 --> 00:23:27,720
There's plenty of room for that in science these days, which is wonderful.

400
00:23:27,720 --> 00:23:28,720
Right.

401
00:23:28,720 --> 00:23:34,720
And you know, and there are kind of endless opportunities right now.

402
00:23:34,720 --> 00:23:35,720
Right.

403
00:23:35,720 --> 00:23:43,800
I mean, there are an enormous number of opportunities to do basic science, translational science,

404
00:23:43,800 --> 00:23:44,800
right?

405
00:23:44,800 --> 00:23:45,800
All the different flavors, right?

406
00:23:45,800 --> 00:23:50,200
And you know, there are certainly these days more fellowships and summer opportunities

407
00:23:50,200 --> 00:23:56,200
and internships and postdoc spots and everything than there were 20 or 30 years ago, right?

408
00:23:56,200 --> 00:24:01,400
So I think I would view it as a, as an opportunity, you know, if I were a student right now.

409
00:24:01,400 --> 00:24:02,960
Well, that's, it's fantastic.

410
00:24:02,960 --> 00:24:06,640
It's completely clear from the time we got spent together that you love your job.

411
00:24:06,640 --> 00:24:08,880
And I think that's a great message to go out on.

412
00:24:08,880 --> 00:24:10,440
Jess, thanks so much for being here in Rochester.

413
00:24:10,440 --> 00:24:11,440
Thanks.

414
00:24:11,440 --> 00:24:12,440
It was really a pleasure.

415
00:24:12,440 --> 00:24:13,440
Great to see you.

416
00:24:13,440 --> 00:24:20,440
Thanks for having me.