SPATIOTEMPORAL ANALYSIS OF SYNCHRONIZATION OF NEURAL ENSEMBLES FOR SPATIAL DISCRIMINATIONS IN CAT STRIATE CORTEX

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1 SPATIOTEMPORAL ANALYSIS OF SYNCHRONIZATION OF NEURAL ENSEMBLES FOR SPATIAL DISCRIMINATIONS IN CAT STRIATE CORTEX Jason M Samonds Department of Biomedical Engineering Vanderbilt University The auto-content wizard was not used in this presentation

2 Background 12 Preferred or Peak Orientation A B C Single-unit Responses area centralis Area 17 Visual Stimulus Average Firing Rate Bandwidth 25 Amplifiers Medial-Lateral Drifting Sinusoid Grating Posterior - Anterior 1 mm 1º Orientation ( )

3 Experimental Hypotheses 1. Based on the orientation-dependence of correlated firing 1, are visual cortical cells cooperative? (yes) 2. Does the cooperation grow with larger groups of cells? 3. Does the response timing depend on the orientation of the visual stimulus? (yes) 4. How is the temporal structure related to synchronization and cooperation in the cortex? 1. Snider RK, Kabara JF, Roig BR, and Bonds AB. Burst firing and modulation of functional connectivity in cat striate cortex. J Neurophysiol 8:73-744, 1998.

4 How do we measure cooperation? (type analysis KL distance) Stimulus Response Letters (k) Types (p(k)) Markov Order (D) (conditional types) Stimulus Repetition Probability of Letter Bin 3 Bin 2 Bin 1 Stimulus Repetition Time Bin Width Bin Probability of Letter 12 3 Bin 3 Bin 2 Bin 1 d( P P ) = 1 2 P( k k, k B K 1 1 b b 1 b 2 P1 ( kb, kb 1,..., kb D)*log2 b= 1 k= P2 ( kb kb 1, kb 2,..., k,..., k b D b D ) ) Neuron 1 Time Bin 123 Neuron 2 Johnson DH, Gruner CM, Baggerly K, and Seshagiri C. Information-theoretic analysis of neural coding. J Comp Neurosci, 1:47-69, 21.

5 Cooperation between area 17 neuron pairs enhances fine discrimination of orientation Samonds JM, Allison JD, Brown HA, Bonds AB. J Neurosci 23: , 23 Cooperation among neuron pairs Number of Pairs ± 4.4% Fine (<1 degrees) Gross (>1 degrees) 57.6 ± 31.9% >1 Synergy (%) Accumulated Dependency (bits) ± 16.9% narrower Orientation (degrees) Firing Rate (sps)

6 Cooperative synchronized assemblies enhance orientation discrimination Samonds JM, Allison JD, Brown HA, Bonds AB. Proc Nat Acad Sci USA 11: , 24. What are synchronized assemblies? area centralis Area msec Medial-Lateral mm Posterior - Anterior LAC 5 degrees

7 Dependency vs. Assembly size Rate Dependency Rate (bits/s) Number of Cells Half-Height Bandwidth (degrees) Rate Dependency Dependency 31% narrow er Number of Cells

8 Cooperation vs. Assembly Size 6 5 R 2 =.78 R 2 = With Joint-Firing Independent 1% Synergy (%) R 2 =.26 R 2 = degrees KL Distance 4% 6% 8% 1 2 degrees 27 degrees 2% 34 degrees Dependency Rate (bits/s) Number of Cells

9 Testing coding when singles cells fail to provide information KL Distance Rate Single cells fail to discriminate fine orientation Independent With Joint-firing KL distance = Orientation Difference (deg) 5 ms

10 How do we quantify timing information? (metric-space analysis) Spike Times Spike Intervals S A S A S 1 S 2 S 3 S 1 S ,4 S 4 S 3 5 S 5 6 S 4 S 6 S B S B S A cost = q x time cost = q x time S B D spike [q] = min(cost) Victor JD and Purpura KP. Nature and precision of temporal coding in visual cortex: a metric-space analysis. J Neurophysiol, 76: , 1996.

11 From another angle: differences in cortical coding between fine and coarse discrimination of orientation Samonds JM, Bonds AB. J Neurophysiol 91: , 24. Orientation-dependent time shift in cortical responses Stimulus Trial Time (s) Preferred OR -4º

12 Information from spike timing Information (bits) q (1/s) q (1/s) Fine Coarse Number of Cells bits 68.9 ms q (1/s) Fine (<1º).24 bits 9.2 ms Coarse (>1º) 256

13 How do we quantify structure and synchronization? JPSTH Analysis 5 ms JPSTH -5 ms Cross-correlogram PSTH Cell 2 5 ms 5 ms ms 5 ms PSTH Cell 1 ms Aertsen AMHJ, Gerstein GL, Habib MK, and Palm G. Dynamics of neuronal firing correlation: modulation of effective connectivity. J Neurophysiol 61:9-917, 1989.

14 Relationships between spike train spatiotemporal structure and synchronization in cat visual cortex Samonds JM, Bonds AB. (in review) 24. Examples of temporal structure Auto-correlation Auto-correlation (%) Bursts Auto-correlation (%) Bursting Refractory Period 1 st Peak Auto-correlation Auto-correlation (%) Time Lag (ms) Bursting with Gamma Oscillation Time Lag (ms) 2 nd Peak Fourier Energy Time Lag (ms) 43 Hz Frequency (Hz) ACH RDH (RDH) Lebedev MA and Wise SP (2) Oscillations in the premotor cortex: single-unit activity from awake, behaving monkeys. Exp Brain Res 13:

15 Serial dependency of oscillation extrinsic source Auto-correlation (%) ACH RDH Less oscillation after shuffling intervals (not intrinsic).6.3. Oscillation (1 st peak) Oscillation (2 nd peak) Inhibitory GABAergic interneuronal networks Pyramidal Cell GABA Basket Cell Interneuron Traub RD, Cunning ham MO, Glovelli T, LeBeau FEN, Bibbig A, Buhl EH, and Whittington MA. GABA-enhanced collective behavior in neuronal axons underlies persistent gamma-frequency oscillations. Proc Nat Acad Sci USA 1: , 23. Cunningham MO, Davies CH, Buhl EH, Kopell N, and Whittington MA. Gamma oscillations induced by kainite receptor activation in the entorhinal cortex in vitro. J Neurosci 23: , 23. GABA

16 Refractory Period intrinsic source 2.5 Auto-correlation (%) R 2 =.48 burst Firing Rate (sps) AHP 3. Auto-correlation (%) R 2 =.27 R 2 =.6 Agmon and Connors, 1989; Connors and Gutnick, 199; Traub and Miles, 1991; Silva-Barrat et al., 1992; Frenceschetti et al., 1995; Gray and McCormick, 1996; Brumberg et al., 2; Nowak et al., Bursting AC (%) Refractory Oscillation

17 Does oscillation drive synchrony? Auto-correlation (%) Cell Cell 13 A B C Cell 11 ACH RDH Time Lag (ms) Time Lag (ms) Time Lag (ms) 4. D Cell E Cell Cross-correlation (%) Time Lag (ms) Time Lag (ms)

18 Moderate correlation between bursts or oscillation and synchrony 4. R 2 =.22 R 2 =.29 Synchrony (cross-correlation peak) 3. Cross-correlation (%) R 2 = Auto-correlation (%) Refractory Bursts Oscillations

19 Oscillation maintains synchronization Oscillatory Non-oscillatory Time Lag (ms) Cross-correlation (%) Time Lag (ms) Cross-correlation (%) Time (ms) Time (ms) Maintained Decay Cross-correlation (%) Oscillation Starting Correlation Correlation After 2 s No Oscillation

20 Transient Synchrony? Shifts in the timing of the response onset determine orientation-dependent synchrony A Stimulus Receptive Fields B Response Stimulus Receptive Fields Response C.3 174º D.3 184º Probability Probability E Cross-correlation (%) Time (s) F Time (s) Lag Time (ms) Lag Time (ms)

21 The input rather than oscillation drives cortical synchronization Synchrony depends on input Reliability of synchronous input Cross-correlation (%) R 2 = R 2 = Latency (ms) SD Latency (ms)

22 Supra-threshold Activation Coherence/Correlation Synchronous Input Interval Structure Synchronous Output Spike Threshold Incoherent or Unstructured Noise Asynchronous Inputs Asynchronous Outputs Burst and/or Oscillation Threshold 2 Coherent or Structured Activation of Multiple Cells Synchronous Inputs Galarreta and Hestrin, 21; Harris et al., 21 Synchronous Outputs Bursts Gamma Oscillation 2Agmon and Connors, 1989; Connors and Gutnick, 199; Traub and Miles, 1991; Silva-Barrat et al., 1992; Frenceschetti et al., 1995; Gray and McCormick, 1996; Brumberg et al., 2; Nowak et al., 23

23 Real-time visualization of neural synchrony for identifying coordinated cell assemblies Samonds JM, Bonds AB. J Neurosci Methods (in press) 24. Present multi-unit response feedback

24 Creating the synchrony map 1. Sample the response ( t = 6 ms) 2. Assign pattern number (digitize) Only 1 pixel can be active for a particular sampled moment of time Reorganize with respect to the number of cells in a pattern Also organized from cell 1 to cell 6 (orthogonal to strength)

25 Synchrony Map Video Real-time synchrony map Moderate Moderate Weak Strong Strong Dependency Weak

26 Looking at larger and more complex integration area centralis 1 x 1 Array (3 Visual Field) Anterior Posterior 5 x 5 Array (1 Visual Field) Area 17 Lateral Medial 2 mm Area 18 AC 5 degrees 3 visual field (57 cm)

27 More subtle and complex forms of coherence Perceptual separation of objects Coherent Plaids and contours Can you detect the mouse? Structure from coherent motion Global Structure Natural Scenes

28 Acknowledgements Co-conspirators experiments: AB Bonds John Allison Heather Brown Zhiyi Zhou Michael Gallucci Technical assistance: Don Johnson Jonathan Victor George Gerstein Ross Snider Feedback and discussion: Peter Dayan Reinhard Eckhorn Bill Newsome Wolf Singer Cristoph von der Malsburg Gerald Westheimer Hugh Wilson Supported by the Graduate School and National Eye Institute Grants RO1EY and RO1EY