Information Processing by Neuronal Populations Chapter 5 Measuring distributed properties of neural representations beyond the decoding of local variables: implications for cognition Adam Johnson, Jadin C. Jackson, and A. David Redish Summary by B.-H. Kim Biointelligence Lab School of Computer Sci. & Eng. Seoul National University

(c) 2000-2008 SNU CSE Biointelligence Lab Outline Introduction Representation / Encoding (tuning curves) / Decoding (reconstruction) Non-local reconstruction (memory and cognition) Self-consistency (coherency) Comparing actual and expected activity patterns Validation by simulations Self-consistency in a Bayesian framework Multiple models in hippocampus conclusions (c) 2000-2008 SNU CSE Biointelligence Lab

Introduction Neural representations are distributed X S Neural Activity sensory description moter planning for behavior cognitive process in between Modern recording technologies Neural ensembles Behavioral variables Immediate reconstruction X simultaneous recording of large neural ensembles (> 100 cells simultaneously) from awake behaving animals. Distributed representation Representation of non-local values for cognitive process Neural representations within ensembles Neural Activity Measuring self-consistency Dynamic changes in the self-consistency  Indicative of cognitive process S Check the extent the firing pattern matches expectations (c) 2000-2008 SNU CSE Biointelligence Lab

Ensemble-based reconstruction Sensory process Response World Memory process Decision making Information Neural Representation Action Behavior x encoded information preceding experience planned future behaviors s neural activity spikes If information is consistently represented across a population of neurons, then it should be possible to infer the expectations of the variable x by examining the neural activity across the population. Encoding model: hypothesized relationship tuning curves mutual information I(x;s) linear filter kernel Encoding Decoding depends on the encoding model Decoding (reconstruction) conditional independency assumption non-prob. methods (c) 2000-2008 SNU CSE Biointelligence Lab

Hippocampus in a brain Hippocampus plays major roles in short term memory and spatial navigation (c) 2000-2008 SNU CSE Biointelligence Lab

Non-local reconstruction (memory and cognition) – Example of a rat experiment Non-local reconstruction is a sign of memory and cognition Internal representation reflects Primary inputs (information on location) rats perform active behavioral tracks on an environment location of the animal ex) slow dynamics in which reconstruction tracks behavior Different information processing modes Internal representation deviate from the external world S cognition potentially plays a role There is a connection of the observable world with rat’s invisible goals or motivations sleeping /pausing at feeder sites: reflecting recently experienced memories rather than the current location reconstruction during non-attentive waking states: representations of non-local information ex) fast dynamics of replay Neural Activity external variable internal representation (c) 2000-2008 SNU CSE Biointelligence Lab

Authors’ contribution Defining self-consistency (or coherency), in order to differentiate between models Implications of multiple generative models for understanding these multiple information processing modes (c) 2000-2008 SNU CSE Biointelligence Lab

Self-consistency : Motivation Possible pitfall of the ensemble-based reconstruction Risky assumption: brain rigidly adheres to representing the present behavioral status Reconstruction errors are viewed as “noise in the system” Ignoring the cognitive questions of memory and recall Questions What is recall or confusion? How does the brain represent competing values in ambiguous situations? How do units within a network function together to form a coherent representation? (c) 2000-2008 SNU CSE Biointelligence Lab

Self-consistency: an Example (tuning curve) A coherent or self-consistent representation The firing of all neurons in a network conforms to some pattern expected from observations during normal encoding. A: unimodal tuning curve B: coherent network firing pattern C: bimodal representation – ambiguous or incoherent state of the network D: confused or incoherent state of the network (behavioral variable) (c) 2000-2008 SNU CSE Biointelligence Lab

Measures for self-consistency Studies of Redish, Averbeck, Georgopoulous Jackson and Redish (2003) Defining the expected ‘activity packet’ Reflecting actual ‘reconstruction errors’ in the ‘self-consistency’ of the representation Measures: comparing actual and expected activity patterns k: available cells in the ensemble T: turning curve F: firing rate Most sensitive Recommended for real use (c) 2000-2008 SNU CSE Biointelligence Lab

Validation by simulations – Setting Attractor network used for the simulations Standard local-excitatory/global-inhibitory network Features of the structure symmetric local excitatory connections btw neurons with similar preferred directions Global inhibition with periodic boundary conditions Can be thought as a circular ring of neurons with a stable attractor state (c) 2000-2008 SNU CSE Biointelligence Lab

Validation by simulations – Issue 1 Random network firing vs. stable activity mode (c) 2000-2008 SNU CSE Biointelligence Lab

Validation by simulations – Issue 2 Rotation vs. jump (c) 2000-2008 SNU CSE Biointelligence Lab

Validation by simulations – Issue 3 Ambiguous vs. single-valued representations (c) 2000-2008 SNU CSE Biointelligence Lab

Self-consistency in a Bayesian Framework Self-consistency measure in a Bayesian Framework (probability of a given neural activity set) (observed neural activity) (decoded neural representation) (M: the generative model used) (c) 2000-2008 SNU CSE Biointelligence Lab

Multiple models in hippocampus Spatial representations within the hippocampus Generally, the neural activity of place cells and the decoded spatial representation very well predicts the animal’s position However, place cell activity can remain well organized even when the decoded representation does not match the animal’s position Properties of hippocampus of interest Multiple brain states Multiple spatiotemporal dynamics, even during awake behaviors Predictive filter Prediction step Correction step (c) 2000-2008 SNU CSE Biointelligence Lab

Multiple generative models in the hippocampus 4 generative models used (prob. dist. is spread with different rates) Percentage of samples in which each model was found to be the most consistent (c) 2000-2008 SNU CSE Biointelligence Lab

Self-consistency (coherency) measures Summary Title revisited Measuring distributed properties of neural representations - self-consistency measures in neural ensemble beyond the decoding of local variables - Immediate reconstruction implications for cognition Dynamic changes in the self-consistency  Indicative of cognitive process Self-consistency (coherency) measures (c) 2000-2008 SNU CSE Biointelligence Lab