From receptive fields to dynamic activity patterns Cees van Leeuwen Laboratory for Perceptual Dynamics

Overview Receptive fields in V1 Patterned activity in V1 Patterned activity in the whole brain 2 Roles for whole-brain activity

Classical Receptive field: Body surface where stimulation could elicit a reflex. Sherrington (1906) ©

Classical Receptive field: portion of sensory space that can elicit neuronal responses when stimulated ©

Receptive fields in V1

Classical Receptive field: region of visual space where a luminous stimulus could drive electrical responses in a retinal ganglion cell Hartline (1938) ©

Classical Receptive field in vision: a two-dimensional region in visual space whose size can range from a few minutes of arc to tens of degrees © On-center and Off-center receptive fields. The receptive fields of retinal ganglion cells and thalamic neurons are organized as two concentric circles with different contrast polarities. On- center neurons respond to the presentation of a light spot on a dark background and off-center neurons to the presentation of a dark spot on a light background.

Convergence Shapley et al. 2007

Classical Receptive Field in V1 Hubel and Wiesel (1962): First characterization of receptive fields in primary visual cortex and classification based on receptive field structure. Some cortical cells respond to light and dark spots in different subregions of the receptive field These are called simple cells. All other cells (the majority) are called complex cells (Martinez & Alonso, 2003) © Receptive fields of four primate V1 neurons (9o-20o eccentricity). The receptive field of each neuron was mapped with light spots (continuous lines, top panels) and dark spots (dotted lines, bottom panels). Unlike complex cells (c,d), simple cells (a,b) respond to light and dark spots in different regions of the receptive field (Figure taken from Chen et al., 2009).

Receptive Fields as the cradle (and the cage) of our thinking about perception Perception begins with a mosaic of features and proceeds in a bottom-up fashion Segregation (functional specialization at the level of cells and regions) Convergence Cannot explain visual experience More of an ideology than a science

Layers of V1 The primary visual cortex has distinct anatomical layers, each with characteristic synaptic connections. (Adapted from Lund 1988)

Context-specific responses in V1 Yeh et al., 2009 PNAS Example spatial maps of V1 cells in layer 4C and layer 2/3. (A) Two simple cells and one complex cell in layer 4C. (B) Two simple cells and one complex cell in layer 2/3. For each example, the Hartley subspace maps are drawn at the top and the sparse-noise maps at the bottom. Spatial maps are shown as color maps (grid size: 0.2°) in which on subregions are represented in red and off subregions are in blue.

Spatiotemporal receptive field Shapley et al. 2007

Spatiotemporal receptive field A spatial receptive field plotted at different time delays between stimulus and neuronal response. The response function will be influenced by: The state of the neuron prior to stimulation (ranging from habituation to expectancy) The state of surrounding neurons in the same layer The state of surrounding layers The state of surrounding areas of the visual system The state of the whole brain Oculomotor-related effects (responses depend on whether the stimulus is flashed or the result of a saccade; MacEvoy et al. 2007).

Patterned activity in V1

Single-cell vs Population responses Weak selectivity at the single-cell level can still lead to strong responses at the population level. Complex selectivity + nonlinearity can lead to flexibility in read-out. Rentzeperis et al. (in press).

Spatiotemporal activation pattern of the VSDI signal. Ayzenshtat I et al. J. Neurosci. 2012;32: ©2012 by Society for Neuroscience

Population Response in v1 Macaque: faces/scrambled face discrimination task The early response was highly correlated with local luminance of the stimulus The late response showed a much lower correlation to the local luminance, was confined to central parts of the face images, and was highly correlated with the animal’s perceptual report. “Our study reveals a continuous spatial encoding of low- and high-level features of natural images in V1. The low level is directly linked to the stimulus basic local attributes and the high level is correlated with the perceptual outcome of the stimulus processing.” Ayzenshtat, et al., 2012

Traveling Waves in V1 ( A) Voltage-Sensitive Dye time courses of an anesthetized monkey n a 6 X 6 mm array following a small grating presented for 250 ms. (B) Time courses at the retinal location of the stimulus and the other 4.5mm away (Grinvald et al., 1994) (C) Spread of activity in area V1 and V2 of awake monkey, after onset of a small visual stimulus. The large response is in V1 the smaller in V2 as delineated by the ocular dominance map at bottom right. (D) Spatial profiles measured through axis parallel to the V1/V2 border, at different time points (Slovin et al., 2002). Review by Sato et al., 2012 Neuron

Traveling Waves in V1 Review by Sato et al., 2012 Neuron LFP (L) and spike activity (R) in V1 (Busse et al., 2009) Cats shown rapid sequence of bars, each for 32 ms at a random position, orientation, and spatial phase. LFP and spike responses to bars having the optimal orientation for the site. (A)Average time course of the LFP for multiple stimulus distances from the receptive field center. (B) Heat map of the LFP responses (C)Amplitude of the traces in (A) as a function of distance. (D–F) Same as (A)–(C) but for the multiunit spike responses. Spike trains were smoothed with a Gaussian window

The stimulus-evoked population response in visual cortex of awake monkey is a propagating wave Muller, Reynaud, Chavane & Destexhe (2014) Nature Communications (a) Single-trial phase-latency maps for V1 ROI in the 50 ms stimulus presentation condition, from trials 1, 3 and 10. These maps are calculated at ms after stimulus onset. Note black box in top panel corresponds to V1 ROI in (a). (b) Phase-latency maps for spontaneous waves observed during no-stimulus, blank conditions. Note the varying color axis and temporal points for the spontaneous maps.

Origin of Traveling Waves LGN transmission delays? Neuh! (No such systematicity) Top-down? Neuh! (also when animal is anesthetized) Horizontal connections within V1 (Bosking et al., 1997; Creutzfeldt et al., 1977; Fisken et al., 1975; Gilbert & Wiesel, 1979; Rockland & Lund, 1982). Propagate with the same speed as the waves do.2-.3 m/s. Connect like with like (just like the waves do!)

Interesting Features of Traveling Waves in V1 They are observed in the Layers 2-3 Facilitory They occur in spontaneous activity (non-rem sleep; quiet wakefulness) and weak stimulation. They cover large regions of space The more intense stimulation, the shorter- range the waves

Most interesting Feature of Traveling Waves in V1 Sites with similar orientation preference are more strongly linked than sites with dissimilar orientation preference. The abscissa represents the absolute difference in orientation between the reference site and the site of the spike- triggered LFP. The ordinate corresponds to residual z-score values of the amplitude after subtracting the distance dependence predicted by the exponential fits. Plotted are the mean and standard error bars for three different bins of orientation difference. Nauhaus et al. (2009) Nature Neurosc

Spontaneous vs Evoked Visual stimulation modifies the effective lateral connectivity in the cortex. Each row shows the dependence of the spike-triggered LFP amplitudes as a pseudo- color image in spontaneous and driven conditions and as scatter plots. The top two examples correspond to two different monkeys. The bottom two are from two different cats. Nauhaus et al Nat. NSc.

Proposed roles of Traveling Waves Facilitory interaction amongst stimuli – Integration (Gilbert, 1992; Kapadia et al., 1999; Polat et al., 1998) – Receptive field tuning (Angelucci & Bressloff, 2006; Cavanaugh et al., 2002) – Normalization (Carandini & Heeger, 2012).

Patterned Activity in the Whole Brain

29 Scalp EEG originates from cortical pyramidal cells reflects the postsynaptic dendritic potentials aggregated from 6 cm 2 of cortical gyri tissue frequencies undistorted by head tissues Adapted from Ivanitsky, Nikolaev, Ivanitsky 1999

30 Dynamics of EEG phase Phase contains essential information about temporal structure of signals Advantages: – relative phase captures spatiotemporal ordering of cortical areas – Meta-stable dynamics can be observed through abrupt changes in relative phase Varela et al Nature Rev

Spontaneous Phase Waves Ito et al. 2007

Traveling and Standing waves Ito et al Instability index of relative phase

First Role of Whole Brain Activity

Brain Dynamics Waves of Phase constitute local and global modes Spontaneous transitions between global modes Variability of these modes in regularity, velocity, duration, etc. These modes are recruited for information processing.

Evoked Phase Waves Alexander et al

Information Processing Maxim States with low synchrony reflect local information processing States with high synchrony reflect global communication Alternation of states is functionally coordinated

Nikolaev, Gong & van Leeuwen, Clin Neurophysiol 2005 Phase synchrony is information-Specific

unambiguous (left) and ambiguous (right) dot lattices

Patterned Activity Spontaneous patterns of synchronization and desynchronization Desynchronization following stimulation (Regional) resynchronization reflects the information communicated

Wave duration reflect stimulus information Nikolaev, et al., Cereb Cortex 2010 information content increase

Second Role of Whole Brain Activity

Modular Small Worlds Optimal local and global connectivity The brain is a modular small world Structure emerges following spontaneous large-scale wave activity (GDP in prenatal rats) and re-emerges in functional architecture following non-REM sleep Brain diseases linked to disturbance of modular small-world functional architecture: Schizophrenia, Alzheimer, Autism(?)

Symbiosis of Structure and Function: a theoretical model Wave sequences help shape the architecture The architecture should sustain wave sequences

Neural Mass Model Breakspear et al Return plot in three dimensions. Potential of pyramidal (V) and inhibitory (Z) neurons, average number of open potassium ion channels (W)

Poincaré section of the Mass Model

Logistic Map

Coupled Logistic Maps Note: the Network structure is a Small World (Watts & Strogatz, 1997)

Coupled Maps: From Random to Small-world Organization Gong & van Leeuwen, 2003; 2004; Kwok et al, 2007; Rubinov et al, 2009; van den Berg & van Leeuwen, 2004; van den Berg et al., 2012

Adaptive Rewiring BeforeAfter

Network Evolution Initial (row 1), evolving (row 2) and asymptotic (row 3) network configurations for structural (column 1), fast (column 2) and slow time scale functional (column 3) networks. Fast time scale networks represent the instantaneous patterns of dynamical synchrony. Slow time scale networks based on the correlation coefficient of 100 consecutive functional states. Nodes in all networks were reordered to maximize the appearance of modules, Rubinov, et al. (2009).

Connectivity Macaque Cortex (Young, 1993; Sporns & Zwi, 2004) TopologyPath LengthCluster Index MC Random (0.0051)*0.1497(0.0030)* Lattice (0.0099)* (0.0002)* Rand(io) (0.0133)* (0.0047)* Latt(io) (0.1173)* (0.0211)*

Conclusions Dynamic activity shows global modes In V1 In the whole cortex Functional coordination in the service of local processing and global communication Dynamic activity helps create optimal architecture to sustain this type of activity

Thanks to: Previous PDL: Sergei Gepshtein (SALK); Pulin Gong (U Sydney); Junji Ito (FZ Juelich); Hironori Nakatani (Tokyo U),Gijs Plomp (Geneva); Ivan Tyukin (Leicester), and technical staff. Current PDL: David Alexander, Chie Nakatani, Tomer Fekete, Andrey Nikolaev, Erik Steur, Chris Trengove, graduate students and staff. External: Daan van den Berg, Michael Breakspear, Michael Kubovy, Thomas Lachmann, Michael Rubinov, Johan Wagemans, and many others.

Thank You! Lab: (senior) postdocs: David Alexander, Tomer Fekete, Chie Nakatani, Andrey Nikolaev, Erik Steur, Chris Trengove; Ph.D. students: Mojtaba Chehelcheraghi, Nicholas Jarman, Radha Nila Meghanathan, Alessandro Solfo, Steffen Theobald, Aleksandra Zharikova (and alumni) Collaborators: Sergei Gepshtein (SALK), Thomas Lachmann (Kaiserslautern), Antonino Raffone (Rome), Narayanan Srinivasan (Allahabad), Ivan Tyukin (Leicester), Johan Wagemans (and their teams)