1. 9 Distributed Population Codes in Sensory and Memory Representations of the Neocortex Matthias Munk Summarized by Eun Seok Lee Biointelligence Laboratory, Seoul National University http://bi.snu.ac.kr/

2. Summary Not single-unit level, but neuronal coding and integrative processes in neocortex are presented. Distributed representations are inevitable. 2 nd level organization – mesoscopic level of neuronal signal transformation and processes is needed for complex and adaptive functions of memory Cortical network operations at the mesoscopic level: 3 kind of activity patterns are presented. 2(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

3. Introduction Distributed representations are inevitable from  Large neuronal circuits to a detailed and adaptive analysis of complex information Optimized dynamical structure and operation of multiple parallel neuronal processes The concept – information coding is based on tuning functions of many individual neurons thought to express their stimulus specificity in an independent way. 2 nd level organization – spatial relation of neurons, convergence of neuronal signals from different modalities into “higher” areas of executive functions or the complex memory formations 3(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

4. Introduction Collection of neuronal signals be used to reconstruct population codes like ‘population vector’ analysis in motor, sensory, memory areas of cortex Brain processes rapidly changing relations among content and context for which more dynamic neuronal representations are required We explore mesoscopic range of cortical signals that might contribute to distributed codes in the cortex with particular emphasis on the malleability of representations as the underlying mechanism of learning processes. 4(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

5. Why is neuronal coding a difficult issue? Central problem in neuroscience: How the brain or neocortex codes information and how the signals are used by neuronal processes for the control of behavior “self-referencing system” “ongoing self-maintaining system” – so treating brain as an input-output system can have only limited success. Many studies in neuronal procedure and functional mechanism lacks “comprehensive understanding of the embedding of these many partial functions into ongoing processes or, into a “brain’s life” that all prior experience and memories may influence processing. Neuronal coding, the embedding of specific functions requires that representations which use a particular neuronal code are sensitive to the context, but at the same time allow for a sufficient degree of invariance to make perceptual processes reliable and therefore support recognition. 5(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

6. Why is neuronal coding a difficult issue? A simple distributed code would not work and the system needs to organize distributed activity in such a way that different processes can run simultaneously without interference in the same network – how ensembles of neurons can self-organize for this purpose Neuronal coding has to involve constant dynamical restructuring of activity which has to incorporate contextual information and therefore has to include many distributed circuits if not the entire brain without loosing the specificity of concurrent processing 6(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

7. What properties does a neuronal code need to have? Code – an instruction or rule with which a signal that contains info is transformed into another signal that can serve as a message or command which is transmitted to a receiver or target system. Neuronal code can only be deciphered if, in addition to the transmitted neuronal activity patterns that have been generated by applying the rules of a particular code, the neuronal readout mechanism has been identified and used to interpret the transmitted signals. 7(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

8. Established and proposed codes Code for occurrence of sensory stimuli has been considered as most fundamental historically. Not single neurons. Codes have been proposed which make use of activity coordinated among distributed populations of neurons. Neuronal processing itself is in general highly distributed and may cause high energy demand due to the need for coordination among remote neuron populations. How the brain codes information – distributed spatial patterns of activity seem to reflect a coding strategy As spatial and temporal resolution of functional imaging techniques improves, more detailed information about the macroscopic and mesoscopic aspects of distributed representations not with micro-electrode experiments 8(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

9. Cortical network operations at the mesoscopic level: 3 activity patterns Promising candidate signals for serving neuronal computations Depend on intact cortical connectivity, and its own dynamical properties 1. Cortical avalanches: represent waves of transmitted synchronous activity. Most recent descovered patterns. 2. Spatio-temporal spike patterns as a correlate of synfire chains: the concept of how cortical connectivity might determine brain activity 3. Oscillations: the oldest form of brain activity. Important constituent of modern concepts of brain function 9(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

10. Cortical network operations at the mesoscopic level - Avalanches Definition: Propagating synchronized signals of large neuronal populations Good evidence for successful transmission processes at the population level Depending on it, neuronal network can be in a “critical” state: the diversity of processes with respect to their size and duration is scale-free. Patterned neuronal activity is not confined to the stimulus or behavior-related processes, but relevant for intrinsic and ongoing processes: in human brains, entire territories seem to be devoted to processing intrinsic signals, not to the well-known sensory domains. 10(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

11. Cortical network operations at the mesoscopic level - Avalanches 11(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/ Fig. 9.1. (a)Avalanches of different length and size (b)Size distribution of avalanches (c)Branching pattern of a self-organizing critical process

12. 12(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/ Cortical network operations at the mesoscopic level - Avalanches Fig. 9.2. Intrinsic and extrinsic cortical systems related to natural scene stimulation. Extrinsic: driven by sensory stimulation in different modalities Intrinsic: territories within the gaps of the extrinsic system identified by highly correlated activity among each other

13. Cortical network operations at the mesoscopic level – Synfire chains Synchronous transmission in a stable fashion Feature: Ubiquitous divergence and convergence of cortico-cortical connectivity Neurophysiological correlate of synfire chains: precise “spatiotemporal spike patterns” occurred in frontal areas 13(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/ Fig. 9.3. (a)Network supporting synfire activity (b)Spatiotemporal patterns recorded from frontal cortex (cell 12  cell 2  cell 13)

14. Cortical network operations at the mesoscopic level - Oscillations Cortical oscillations be useful for understanding how large populations of cortical neurons may be coordinated so that they can directly code information or support complex neuronal operations State dependence found in EEG study: rhythms at other frequency ranges are to some extent state dependent, but may participate in or support neuronal coding of information. 14(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/

15. 15(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/ Cortical network operations at the mesoscopic level - Oscillations Fig.9.4. Oscillations: Lateral aspect of a macaque brain with recording positions (upper left) and averaged autocorrelation (gray) and cross-correlation (white) functions with shift predictors (lower right) computed from transcortical field potentials recorded simultaneously in four cortical areas.

16. Population codes and distributed representations 16(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/ Fig. 9.5. Distributed and sparse representation. (a) Neuronal population data after multidimensional scaling (b) Parameters used to generate a physical model of the faces (c) Population vectors indicating the match between model and neuronal data. Representations for complex visual objects are distributed over large populations of neurons in inferotemporal cortex. Tuning of individual cells is broad, but the acuity of the population of cells is close to identifying individual faces.

17. Visual representations during short-term memory: units versus populations 17(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/ Fig. 9.6. Stimulus selectivity and distributed activity patterns in lateral prefrontal cortex of monkeys performing a simple visual memory task. Memorizing mechanism  the stabilization of activity patterns Not by a single dynamical principle like sustained spike firing, but integration of many different dynamical aspects in an adaptive fashion provide sufficient stability of representations Multiple intricate mechanisms have to cooperate to achieve adaptive memory performance Populations of neurons may be a useful level of organization for a better understanding of memory mechanisms

18. Visual representations during short-term memory: units versus populations 18(C) 2009, SNU Biointelligence Lab, http://bi.snu.ac.kr/ Comparing time course of stable patterns/ firing rates: different groups are co-activated during different epochs with increased frequency of stable patterns.  different groups of neurons cooperate in order to constitute the distributed activity patterns at the population level. Multiple cortical and subcortical areas participate in al complex cognitive functions. Fig. 9. 7. Strength of ensemble selectivity and simultaneous modulation of firing rates.