Analysis of in-vivo extracellular recordings. Ryan Morrill Bootcamp 9/10/2014

Transcription

1 Analysis of in-vivo extracellular recordings Ryan Morrill Bootcamp 9/10/2014

2 Goals for the lecture Be able to: Conceptually understand some of the analysis and jargon encountered in a typical (sensory) systems neuroscience paper: Spike sorting Spike train PSTH Tuning curves Receptive fields STA & reverse correlation Encoding vs. decoding Interpret graphical representations of single-unit data; understand how these plots are generated

3 Outline Part 1: how to get spikes & spike trains? Recap: extracellular recording How to isolate spikes from a raw trace, step-bystep Putative cell type specificity Spike trains & some graphical representations Part 2: what to do with these data? Tuning and receptive fields: selectivity of neurons Spike-triggered average Spectro(spatio)-temporal receptive fields Encoding & decoding

4 A note about biases Systems neuroscience has a historical bias for study of sensory and motor function Why? Tight control over input parameters (sensory) or careful monitoring of output (motor) This lecture will focus on sensory systems neuroscience, but general principles can be applied elsewhere Similar analysis methods can be used for in vivo intracellular recording methods N.B.: much more information can be gained from in vivo recordings: morphological ID of neurons, subthreshold voltage changes ion channel populations, etc.

5 Big questions for this afternoon How do we get from this: to this? Raw voltage trace from extracellular recording Patterns of spiking activity to this? Conclusions about neuronal function

6 Outline Part 1: how to get spikes & spike trains? Recap: extracellular recording How to isolate spikes from a raw trace, step-bystep Putative cell type specificity Spike trains & some graphical representations Part 2: what to do with these data? Tuning and receptive fields: selectivity of neurons Spike-triggered average Spectro(spatio)-temporal receptive fields Encoding & decoding

7 Recap: extracellular recordings Recorded from outside cells with very thin microelectrodes implanted in the brain Microelectrodes pick up activity from a few nearby neurons Recorded action potentials typically referred to as spikes Why unit instead of neuron? Humphrey & Schmidt 1991 Extracellular single-unit recording methods. in Neurophysiological Techniques.

8 How do we find spikes in a voltage trace? Step 1: bandpass filter raw trace Step 2: amplitude threshold Step 3: spike sorting

9 Step 1: bandpass filter raw voltage trace (e.g. between 300 & 3000 Hz) Why is this important? Bonus: what are we looking at if we instead looking at lower (<300 Hz) frequencies?

10 Step 2: amplitude threshold to determine spikes What happens if threshold is too high? Too low? Problem: what if your electrode picks up action potentials from multiple units?

11 Multiple units on the same channel? Spike sorting Each neuron tends to fire spikes of a particular shape Spike sorting is clustering based on similarity of spike shapes In practice, often uses dimensionality reduction algorithms such as principal component analysis (PCA) - won t get into this today Spike sorting is a messy and often unsatisfying solution Quian Quiroga 2007 Spike Sorting, Scholarpedia

12 Step 3: Spike sorting

13 Cool, but Downside of extracellular recordings: usually blind to cell type However, can make inferences about cell type based on spike shape

14 Classifying cells based on spike shape Parvalbumin-expressing GABAergic interneurons (inhibitory) short-duration action potentials, narrow spiking Glutamatergic pyramidal neurons (excitatory) longer-duration APs, broad spiking There are exceptions! Betz cells in M1 (excit.) have short-duration spikes (Vigneswaran et al J. Neurosci.) Somatostatin cells (inhib.) are broad spiking Csicsvari et al J Neurosci.

15 Information from spikes Q: Does the shape of a single spike tell us much beyond which cluster it falls into? A: Not really Often recordings are abstracted into stream of binary events; 0 for no spike, 1 for spike This is a spike train Q: What features of a spike train might be interesting? A: group brainstorm time

16 Visualizing spike trains: raster plots Cortical neurons show highly variable response patterns, so many stimulus presentation trials are required From website of Prof. David Heeger, NYU

17 PSTH Peri-stimulus time histogram peri-event time histogram Simply, histogram of the time at which a neuron fires Why is this useful? Jeff Knowles, Doupe Lab, 2014 (?)

18 Unit A Unit B Jeff Knowles, Doupe Lab, 2014 (?)

19 Summary: Part 1 Extracellular recording analysis: Start out with noisy voltage trace Filter to isolate frequencies of that might contain spikes Threshold to find high-amplitude events (spikes) Spike sort to separate single-unit waveforms from other single-units or multi-unit activity Spike shape can give us an educated guess about cell type We re most interested in sequences of spikes ( spike trains ) Commonly these data are plotted as a raster or peri-stimulus time histogram

20 Outline Part 1: how to get spikes & spike trains? Recap: extracellular recording How to isolate spikes from a raw trace, step-bystep Putative cell type specificity Spike trains & some graphical representations Part 2: what to do with these data? Tuning and receptive fields: selectivity of neurons Spike-triggered average Spectro(spatio)-temporal receptive fields Encoding & decoding

21 Neurons respond selectively Many neurons selectively respond to particular aspects of sensory, motor, association or cognitive information This selectivity is known as tuning E.g., simple cells of V1 are tuned to specific orientations. Complex cells are tuned to movements in specific directions Tuning curves characterize the average response of a neuron to a given stimulus as its parameters change along a gradient

22 Tuning curves Dayan & Abbott, Theoretical Neuroscience

23 Tuning curves: sound frequency

24 Tuning for complex stimuli Monkey anterior IT cortex Kobatake & Tanaka 1994 J. Neurophys.

25 Receptive field The portion of sensory space that can elicit sensory responses when stimulated Alonso & Chen 2009 Receptive Field Scholarpedia Multiple dimensions of receptive fields, for example: 1-D: carbon chain length of odorant 2-D: skin surface Multiple-D: space, time, tuning properties of visual neurons Sensory homunculus, Cortical homunculus Wikipedia

26 Stimulus response tuning curve works well if your stimulus space is constrained - e.g. sine wave tones for A1, moving bars for V1 What if you don t know what types of stimuli to use? when might we encounter this situation?

27 Spike-triggered average What stimuli produce a given response? Start with an isolated unit in a given region of interest Present a time varying, (often) complex stimulus Average time window before each spike Result: linear estimate of a neuron s receptive field Falls under reverse correlation methods

28 Spike-triggered average: an illustrative example I want to know what types of scenes in a movie you all like (you are neurons) You raise your hands after you ve seen something you like (raising hands = spiking) I examine movie segments before each hand raise event, then determine common features of segments (STA) (If I ve shown you a wide-enough range of movies) I now know something about what each of you like

29 Spike-triggered average Dayan & Abbott, Theoretical Neuroscience

30 Stimulus: contrast-modulated noise Neill & Stryker 2008 J. Neurosci.

31 STRF: multiple dimensions of selectivity Activated Inhibited Spectro-temporal receptive field, Wikipedia

32 STRF: what can it tell us? Best frequency Response latency Inhibitory response regions Spectral interactions Temporal interactions Spectro-temporal receptive field, Wikipedia

33 STRF demo (If we have time) odel_neuron/

34 Canonical types of neural codes Rate code no. of spikes within specified timeframe carries information e.g. motor neurons firing rate determines force of contraction Temporal code precise timing of single spikes (or high-freq. fluctuations of firing rate) within encoding window carries info e.g. auditory system and interaural phase differerence

35 Encoding vs. decoding Sensory neurons can be thought of as filters Encoding: map from stimulus response Decoding: map from response stimulus

36 Decoding: an example

37 Summary: Part 2 Neurons respond selectively Selectivity can be quantified in tuning curves Receptive fields are areas in stimulus space to which a neuron responds Spike triggered averaging can be used to estimate a linear receptive field Spectro(spatio)-temporal receptive fields (STRF) depict stimulus responsivity in multiple dimensions (usually time & something else) Rate code: firing rate matters; temporal code: precise timing of single spikes matter Encoding: stimulus response; decoding: response stimulus

38 Thanks for listening Contact:

39 Time and spatial scales of electrophysiology methods Humphrey & Schmidt 1991 Extracellular single-unit recording methods. in Neurophysiological Techniques.

40 Mulit-unit (pre-spike sorting) Single-unit (post-spike sorting)