Detailed Program for OS Brain and Cognition

Evidence of temporal coding in neural systems.

How neural activity encodes the stimulus and animal behavior is central to neuroscience.

Given the noisy nature of spiking activity it is argued that neurons encode information by modulating their firing rates. This is called rate coding. Rate coding makes sense, because not only individual neurons are noisy, they are connected to several thousand neurons with weak and unreliable synapses. However, the idea of rate coding is too simplistic and limited in its repertoire. Together with rate coding, evidence is accumulating for a temporal code, that indicates the role of millisecond precise, co-ordinated spiking of a group of neurons.

In this seminar series, we would discuss papers which have shown that precise timing of spikes of a neuron and millisecond precise coordinated spiking in a group of neurons may carry information about the stimulus and behavior of animals. The experimental studies chosen for the seminar series describe novel methods to analyze multiple-single-unit spike trains and extract the temporal code, not only for the early sensory information processing (e.g. visual [1,8,11,12,17], auditory [9], tactile [10], olfactory [5]) but also in the behavioral tasks involving episodic memory [13,14] and other cognitive function [6-7].
The noise in the neural systems makes it rather difficult to infer temporal code from the spiking activity of just a few neurons and often data can be misinterpreted.  So we will discuss few studies which points out the potential errors in the analysis and interpretation of multiple-single-unit-activity and propose necessary statistical tests to correctly infer the temporal code [2-4].
Further, it maybe possible that rate and temporal coding are he two sides of same coin. In fact there have been suggestion, motivated form the analysis of spiking activity in hippocampus, that allow transformation of rate code into temporal code [15,16].
 
Neural Code
1.  W Bialek, F Rieke, RR de Ruyter van Steveninck, and D Warland  (1991) Reading the neural code. Science  Vol. 252. no. 5014, pp. 1854 - 1857

Problems in analysis of multiple single unit data -- TAKEN
2. A. Mokeichev, M. Okun, O. Barak, Y. Katz, O. Ben-Shahar, I. Lampl (2007) Stochastic Emergence of Repeating Cortical Motifs in Spontaneous Membrane Potential Fluctuations In Vivo Neuron, Volume 53, Issue 3, Pages 413-425
3. Yuji Ikegaya, Gloster Aaron, Rosa Cossart, Dmitriy Aronov, Ilan Lampl, David Ferster, Rafael Yuste (2004) Synfire Chains and Cortical Songs: Temporal Modules of Cortical Activity. Science Vol. 304. no. 5670, pp. 559 - 564

*Controls for checking significance of spike patterns -- TAKEN
4. Alex Roxin Vincent Hakim and Nicolas Brunel (2008) The Statistics of Repeating Patterns of Cortical Activity Can Be Reproduced by a Model Network of Stochastic Binary Neurons. J. Neuroscience 28(42):10734-10745.

*Information encoding and decoding in Olfactory system
5. Broome BM, Jayaraman V, Laurent G. (2006) Encoding and decoding of overlapping odor sequences. Neuron. 51(4):467-82.

Neuron assembly dynamics
6. Vaadia E, Haalman I, Abeles M, Bergman H, Prut Y, Slovin H, Aertsen A (1995) Dynamics of neuronal interactions in monkey cortex in relation to behavioral events. Nature 373: 515-518
7. Averbeck BB JW Sohn, D. Lee (2006) Activity in prefrontal cortex during dynamic selection of action sequences.Nature Neuroscience 9, 276 - 282

Information carried by single spikes (8-10) -- TAKEN
8. Tim Gollisch and Markus Meister (2008) Rapid Neural Coding in the Retina with Relative Spike Latencies. Science Vol. 319. no. 5866, pp. 1108 - 1111
9. Chase and Young (2007) First-spike latency information in single neurons increases when referenced to population onset. PNAS March 20, 2007 vol. 104 no. 1 5175-5180
10. Johansson and Birznieks (2004) First spikes in ensembles of human tactile afferents code complex spatial fingertip events. Nature Neuroscience 7, 170 - 177 (2004)

Rank order coding (11-12) -- TAKEN
11. Thorpe S, Delorme A, Van Rullen R. (2001) Spike-based strategies for rapid processing. Neural Netw. 2001 Jul-Sep;14(6-7):715-25.
12. Van Rullen R, Thorpe SJ. (2001) Rate coding versus temporal order coding: what the retinal ganglion cells tell the visual cortex. Neural Comput. 2001 Jun;13(6):1255-83.

Hippocampus cell assemblies (13-14) -- TAKEN
13. Lin L, Osan R, Tsien JZ. (2006) Organizing principles of real-time memory encoding: neural clique assemblies and universal neural codes. Trends Neurosci. 2006 Jan;29(1):48-57.
14. Lin L, Osan R, Shoham S, Jin W, Zuo W, Tsien JZ. (2005) Identification of network-level coding units for real-time representation of episodic experiences in the hippocampus. Proc Natl Acad Sci U S A. 2005 Apr 26;102(17):6125-30.

Phase precession in the hippocampus (15-16)
15. Mehta MR, Lee AK, Wilson MA. (2002) Role of experience and oscillations in transforming a rate code into a temporal code. Nature. Jun 13;417(6890):741-6
16. O'Keefe, J. & Recce, M. L. (1993) Phase relationship between hippocampal place units and the EEG theta rhythm. Hippocampus 3, 317Ð330

*LFP and spike may represent different information
17. Andrei Belitski, Arthur Gretton, Cesare Magri, Yusuke Murayama, Marcelo A. Montemurro, Nikos K. Logothetis and Stefano Panzeri (2008) Low-Frequency Local Field Potentials and Spikes in Primary Visual Cortex Convey Independent Visual Information. Journal of Neuroscience, 28(22):5696-5709

* indicate that the selected papers maybe more involved in the methodology. More suitable for graduate student with analytical background.