Over the past cen­tu­ry, we’ve been to the moon, we’ve split the atom, we’ve sequenced the human genome, but were still only at the very begin­ning of our under­stand­ing of the human brain. This is one of the great chal­lenges that we face. If we can under­stand the brain, we can devel­op bet­ter treat­ments for brain dis­or­ders, we can design bet­ter robots, bet­ter com­put­ers, and ulti­mate­ly we can bet­ter under­stand ourselves.

Alan Turing was one of the first to sug­gest that the brain com­putes. In oth­er words, that it imple­ments algo­rithms to solve prob­lems, like com­put­ers. And Turing also was one of the first to design and help build a com­put­er, the Automatic Computing Engine from 1950, and it was a process that he actu­al­ly called build­ing a brain. Since that time, there’s been tremen­dous advances in com­put­ing hard­ware, in algo­rithms. And there’s been a lot of recent excite­ment about deep learn­ing net­works, which are inspired by the hier­ar­chi­cal pro­cess­ing and the visu­al sys­tem of the brain, and which can be trained to rec­og­nize pat­terns like human faces, or cats in YouTube videos.

This com­bi­na­tion of fast hard­ware and improved algo­rithms has now allowed com­put­ers to out­per­form brains on a range of dif­fer­ent tasks, for exam­ple play­ing chess or play­ing video games, and com­put­ers can­not chal­lenge the per­for­mance of humans for sophis­ti­cat­ed tasks like object recog­ni­tion, one of the holy grails of com­put­er vision. However, brains are still bet­ter than com­put­ers at many tasks, par­tic­u­lar­ly those that involve inter­act­ing with the real world.

Here we have Lionel Messi scor­ing a beau­ti­ful goal. [clip unavail­able] And for com­par­i­son here we have the cur­rent state of the art of robot soccer.

This is tak­en from the 2015 robot soc­cer World Cup. The key ques­tion now is, what is the ori­gin of the supe­ri­or per­for­mance of the human brain?

First of all, neu­rons are remark­able com­put­ing devices. Each neu­ron gets up to ten thou­sand synap­tic inputs, each of which is plas­tic and allows neu­rons and brains to store huge amounts of infor­ma­tion much more effi­cient­ly than com­put­ers can. Brains run on only about twelve watts of pow­er, orders of mag­ni­tude more effi­cient than the computer.

But neu­rons can­not just store infor­ma­tion, they also can process infor­ma­tion. So in my lab we’re using lasers to acti­vate indi­vid­ual synap­tic inputs in pat­terns to sin­gle den­drites. And with these exper­i­ments we can show that sin­gle neu­rons can already solve com­pu­ta­tion­al tasks like pat­tern recognition.

If neu­rons are so smart, why is it so hard to under­stand how the brain works? Well, there’s two main prob­lems. The first is that neu­rons are embed­ded in neur­al cir­cuits, which are very com­pli­cat­ed. This is an image of a neur­al cir­cuit tak­en from the so-called brain­bow mouse devel­oped by Jeff Lichtman and Josh Sanes at Harvard. And you can see from this image that recon­struct­ing the wiring dia­gram of such a cir­cuit, even when all the neu­rons are labeled with dif­fer­ent col­ors, is a great challenge. 

The sec­ond prob­lem is that we don’t know the neur­al code. This is a simul­ta­ne­ous record­ing from hun­dreds of neu­rons in the cor­tex by my col­league Nick Steinmetz at UCL. And each neu­ron fires a spike or an action poten­tial. So each dot here is a sin­gle action poten­tial in a sin­gle neu­ron in this cir­cuit. And just like it’s hard to read a mes­sage in Morse code if you don’t know the code, it’s very hard to fig­ure out how neu­rons are pro­cess­ing information. 

But we’re now at a piv­otal point in neu­ro­science where we have new tools to crack this prob­lem, com­ing from two unex­pect­ed sources in nature, the jel­ly­fish, and green algae. And these two crea­tures have giv­en us two new pro­teins that now allows us to read and write activ­i­ty in neur­al cir­cuits in the intact intact brain.

So, the jel­ly­fish has giv­en us green flu­o­res­cent pro­tein, which we can link up with a cal­ci­um sen­sor to make a genetically-encoded cal­ci­um sen­sor. And here we’ve imple­ment­ed this, we’ve expressed the sen­sor in cor­ti­cal neu­rons in my lab. And you can see from this movie that you can use the flash­es of light to record the activ­i­ty of thou­sands of neu­rons in the intact brain. 

Green algae have giv­en us a pro­tein called chan­nel­rhodopsin, which is a light-sensitive chan­nel which when expressed in neu­rons allows you to con­trol the activ­i­ty of neu­rons with light. And here my col­league Karl Deisseroth at Stanford has expressed chan­nel­rhodopsin in neu­rons of the motor cor­tex. And you can see that when you switch on the blue light, you can con­trol the behav­ior of this mouse with light alone. This is called optogenetics.

Now, would­n’t it be great if we could com­bine these two rev­o­lu­tions by being able to read out and also manip­u­late the activ­i­ty of neu­rons in the intact neur­al cir­cuit and use indi­vid­ual beams of light to manip­u­late the neur­al code on the spa­tial and tem­po­ral scale in which it nor­mal­ly hap­pens in the brain? So here’s how we’re approach­ing this dream exper­i­ment in my lab. We’re coex­press­ing a cal­ci­um sen­sor and an opto­ge­net­ic probe derived from chan­nel­rhodopsin, in cor­ti­cal neu­rons, and then using indi­vid­ual beams of laser light to manip­u­late indi­vid­ual neu­rons inde­pen­dent of their neigh­bors. We can then use this approach to mea­sure the neur­al code dur­ing behav­ior, and also manip­u­late the neur­al code dur­ing behav­ior, to make causal links between pat­terns of activ­i­ty and behavior.

So we’re at a new fron­tier in neu­ro­science where we can both read and write activ­i­ty in neur­al cir­cuits, and use this approach to crack the neur­al code and devel­op new treat­ments for dis­ease. For exam­ple, restor­ing vision in the reti­na. And we can also har­ness this new infor­ma­tion to devel­op bet­ter com­put­er chips, and bet­ter robots. So it’s a time of tremen­dous oppor­tu­ni­ty. Thank you.