1. Deriving connectivity patterns in the primary visual cortex from spontaneous neuronal activity and feature maps Barak Blumenfeld, Dmitri Bibitchkov, Shmuel Naaman, Amiram Grinvald and Misha Tsodyks Department of Neurobiology, Weizmann institute of Science, Rehovot, Israel

2. Abstract Population activity across the surface of the primary visual cortex exhibits well-known regular patterns. The location and the shape of activity patches depend on features of the stimulus such as orientation. Recent studies have shown that activity patterns generated spontaneously are similar to those evoked by different orientations of a moving grating stimulus [Kenet, et. al., Nature 2003]. This suggests the existence of intrinsic preferred states of the cortical network in this area of the brain. We deduce possible connections in such a network from a set of single condition orientation maps obtained by voltage-sensitive dye imaging. We assume the maps as attractor states of a recurrent neural network and model the connectivity using a modified version of the pseudo-inverse rule of the Hopfield network. The results suggest a local distance- dependent Mexican-hat shaped connectivity. Long-range connections also exist and depend mainly on the difference in orientation selectivity of the connected pixels. The strength of connections correlates strongly with orientation selectivity of the neurons. The dependence of the obtained synaptic weights on the distance between neurons correlates with the pattern of correlations in the spontaneous activity, suggesting that intrinsic connectivity in neuronal networks in this area of the brain underlies the activity in both spontaneous and evoked regimes.

3. ABC Figure 2. Orientation single condition maps obtained by voltage sensitive dye optical imaging of a cat's area 17/18. The activity was evoked by a moving grating stimulus with an orientation of (A) 0° (horizontal), (B) 45°, and (C) 90° (vertical). The direction of motion was perpendicular to the stimulus orientation. Figure 1. Experimental setup for the voltage sensitive dye optical imaging. Experimental setup

4. Templates EvokedSpontaneous PCAKohonen map Figure 4. Kohonen algorithm performs a topological mapping of spontaneous activity frames onto a set of templates on a ring. The shapes of the learned templates resemble the evoked orientation maps. Figure 3. Projections of 24 single condition orientation maps M k corresponding to orientations θ k onto a plane spanned by the 1 st two principle components p 1, p 2. The data is fitted by a circle (solid line). Selectivity If orientation maps form a perfect ring: Topology of intrinsic states

5. Figure 5. Activity patterns obtained by voltage sensitive dye optical imaging. The pattern in (A) was evoked by a 0° moving grating stimulus. It is very similar to the spontaneous pattern (B). Spontaneous Evoked Figure 6. Preferred orientation maps calculated using evoked single condition maps (A) and Kohonen templates of spontaneous spontaneous activity (B) [Kenet, et. al., 2003]. Spontaneous activity patterns B Spontaneous A

6. Network dynamics: T Network connectivity: Pattern correlation matrix: For a linear gain function, the connectivity results in a Hopfield network, which stores two patterns corresponding to the principle components of orientation maps: Recurrent neural network with functional maps as attractors: Fixed points of dynamics: Network model with pseudo-inverse connectivity

7. Figure 9. Average synaptic weights as a function of the difference between preferred orientations of the pre- and post synaptic neurons, for the pseudo inverse connectivity. Figure 10. Connectivity of individual pixels. (A) Afferent synaptic weights of the pixel marked by the yellow dot. (B) : Activity pattern evoked by a 90º stimulus. The yellow dot marks the same pixel as in (A). (C) Afferent synaptic weights of another pixel (close to a pinweel). -2 0 1 2 x 10 -3 -4 -3 -2 0 1 2 3 4 5 -2 0 1 2 x 10 -3 A CB Dependence of connectivity on orientation selectivity

8. Figure 8. Synaptic weights of the attractor network as a function of distance between pre- and post synaptic neuron, for the pseudo inverse connectivity. The bin size was the size of one pixel, which was ~50μm. Figure 7. Average pixel-by-pixel correlation coefficient of recorded spontaneous activity as a function of distance between pixels on the cortical surface. Solid line: fit using a Mexican hat function Dependence of connectivity on spatial separation

9. Initial Stationary SimulationExperiment ABC Initial Stationary Simulation Experiment DEF Figure 5.2 Simulations of the pseudo inverse connectivity model with random initial activity. Panels (A),(D) show the initial random activity patterns for two trails. Panels (B),(E) show the corrsponding stationary activity patterns (t=300). Panels (C),(F) show the corresponding evoked activity pattern: (C) 37.5º and (F) 112.5º. In all trails, the stationary activity pattern was similar to one particular evoked pattern, and was never a mixture of several patterns evoked by different orientations. This property is to be attributed to the non-linearity of the gain function. By considering this type of simulation as a model for spontaneous activity, we conclude that the pseudo-inverse connectivity can indeed produce the typical activity patterns spontaneously. Network simulations

10. References: Kenet T., Bibitchkov D., Tsodyks M., Grinvald A., Arieli A. (2003) Spontaneously emerging cortical representations of visual attributes. Nature 425: 954-956 Personnaz L., Guyon I.I., Dreyfus G. (1986) Collective computational properties of neural networks: New learning mechanisms. PHYS. REV. A. Nov;34(5):4217-4228. Primary visual cortex has intrinsic activity states that emerge both spontaneously and due to visual stimulation and can originate from intra- cortical interactions in this area of the brain. Intrinsic states corresponding to orientation maps lie on a ring embedded into a high-dimensional space of neuronal activities. Attractor neural network with pseudo-inverse connectivity is capable to generate experimental activity patterns. The strength of modelled connections depends on the degree of selectivity of connected neurons and on the difference between their preferred orientations. Conclusions