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TNI: Computational Neuroscience Instructors: Peter Latham Maneesh Sahani Peter Dayan

TNI: Computational Neuroscience Instructors: Peter Latham Maneesh Sahani Peter Dayan

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## TNI: Computational Neuroscience Instructors: Peter Latham Maneesh Sahani Peter Dayan

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**TNI: Computational Neuroscience**Instructors: Peter Latham Maneesh Sahani Peter Dayan TAs: Arthur Guez, aguez@gatsby.ucl.ac.uk Marius Pachitariu, marius@gatsby.ucl.ac.uk Website: http://www.gatsby.ucl.ac.uk/~aguez/tn1/ Lectures: Tuesday/Friday, 11:00-1:00. Review: Tuesday, starting at 4:30. Homework: Assigned Friday, due Friday (1 week later). first homework: assigned Oct. 7, due Oct. 14.**What is computational neuroscience?**Our goal: figure out how the brain works.**There are about 10 billion cubes of**this size in your brain! 10 microns**How do we go about making sense of this mess?**David Marr (1945-1980) proposed three levels of analysis: 1. the problem (computational level) 2. the strategy (algorithmic level) 3. how it’s actually done by networks of neurons (implementational level)**Example #1: memory.**the problem: recall events, typically based on partial information.**r3**r2 r1 activity space Example #1: memory. the problem: recall events, typically based on partial information. associative or content-addressable memory. an algorithm: dynamical systems with fixed points.**Example #1: memory.**the problem: recall events, typically based on partial information. associative or content-addressable memory. an algorithm: dynamical systems with fixed points. neural implementation: Hopfield networks. xi = sign(∑j Jij xj)**Example #2: vision.**the problem (Marr): 2-D image on retina → 3-D reconstruction of a visual scene.**Example #2: vision.**the problem (modern version): 2-D image on retina → recover the latent variables. house sun tree bad artist**Example #2: vision.**the problem (modern version): 2-D image on retina → recover the latent variables. house sun tree bad artist cloud**Example #2: vision.**the problem (modern version): 2-D image on retina → reconstruction of latent variables. an algorithm: graphical models. x1 x2 x3 latent variables r1 r2 r3 r4 low level representation**Example #2: vision.**the problem (modern version): 2-D image on retina → reconstruction of latent variables. an algorithm: graphical models. x1 x2 x3 latent variables inference r1 r2 r3 r4 low level representation**Example #2: vision.**the problem (modern version): 2-D image on retina → reconstruction of latent variables. an algorithm: graphical models. implementation in networks of neurons: no clue.**Comment #1:**the problem: the algorithm: neural implementation:**Comment #1:**the problem: easier the algorithm: harder neural implementation: harder often ignored!!!**Comment #1:**the problem: easier the algorithm: harder neural implementation: harder A common approach: Experimental observation → model Usually very underconstrained!!!!**Comment #1:**the problem: easier the algorithm: harder neural implementation: harder Example i: CPGs (central pattern generators) rate rate Too easy!!!**Comment #1:**the problem: easier the algorithm: harder neural implementation: harder Example ii: single cell modeling CdV/dt = -gL(V – VL) – n4(V – VK) … dn/dt = … … lots and lots of parameters … which ones should you use?**Comment #1:**the problem: easier the algorithm: harder neural implementation: harder Example iii: network modeling lots and lots of parameters × thousands**r3**x1 x2 x3 r2 r1 r2 r3 r4 r1 activity space Comment #2: the problem: easier the algorithm: harder neural implementation: harder You need to know a lot of math!!!!!**Comment #3:**the problem: easier the algorithm: harder neural implementation: harder This is a good goal, but it’s hard to do in practice. Our actual bread and butter: 1. Explaining observations (mathematically) 2. Using sophisticated analysis to design simple experiments that test hypotheses.**Comment #3:**Two experiments: - record, using loose patch, from a bunch of cells in culture - block synaptic transmission - record again - found quantitative support for the balanced regime. J. Neurophys., 83:808-827, 828-835, 2000**Comment #3:**Two experiments: - perform whole cell recordings in vivo - stimulate cells with a current pulse every couple hundred ms - build current-triggered PSTH - showed that the brain is intrinsically very noisy, and is likely to be using a rate code. Nature, 466:123-127 (2010)**Comment #4:**the problem: easier the algorithm: harder neural implementation: harder some algorithms are easy to implement on a computer but hard in a brain, and vice-versa. these are linked!!!**Comment #4:**hard for a brain, easy for a computer: A-1 z=x+y ∫dx ... easy for a brain, hard for a computer: associative memory**Comment #4:**the problem: easier the algorithm: harder neural implementation: harder some algorithms are easy to implement on a computer but hard in a brain, and vice-versa. we should be looking for the vice-versa ones. it can be hard to tell which is which. these are linked!!!**Your cortex unfolded**neocortex (cognition) 6 layers ~30 cm ~0.5 cm subcortical structures (emotions, reward, homeostasis, much much more)**Your cortex unfolded**1 cubic millimeter, ~3*10-5 oz**1 mm3 of cortex:**50,000 neurons 10000 connections/neuron (=> 500 million connections) 4 km of axons**1 mm3 of cortex:**50,000 neurons 10000 connections/neuron (=> 500 million connections) 4 km of axons 1 mm2 of a CPU: 1 million transistors 2 connections/transistor (=> 2 million connections) .002 km of wire**1 mm3 of cortex:**50,000 neurons 10000 connections/neuron (=> 500 million connections) 4 km of axons whole brain (2 kg): 1011 neurons 1015 connections 8 million km of axons 1 mm2 of a CPU: 1 million transistors 2 connections/transistor (=> 2 million connections) .002 km of wire whole CPU: 109 transistors 2*109 connections 2 km of wire**1 mm3 of cortex:**50,000 neurons 10000 connections/neuron (=> 500 million connections) 4 km of axons whole brain (2 kg): 1011 neurons 1015 connections 8 million km of axons 1 mm2 of a CPU: 1 million transistors 2 connections/transistor (=> 2 million connections) .002 km of wire whole CPU: 109 transistors 2*109 connections 2 km of wire**dendrites (input)**soma (spike generation) axon (output) +20 mV 1 ms voltage -50 mV 100 ms time**synapse**current flow**synapse**current flow**+20 mV**voltage -50 mV 100 ms time**neuron i**neuron j neuron j emits a spike: EPSP V on neuron i t 10 ms**neuron i**neuron j neuron j emits a spike: IPSP V on neuron i t 10 ms**neuron i**neuron j neuron j emits a spike: IPSP V on neuron i t amplitude = wij 10 ms**neuron i**neuron j neuron j emits a spike: changes with learning IPSP V on neuron i t amplitude = wij 10 ms**wij**current flow**x**latent variables peripheral spikes r sensory processing ^ r “direct” code for latent variables cognition memory action selection brain ^ r' “direct” code for motor actions motor processing r' peripheral spikes x' motor actions