Spontaneous Activity in the Zebrafish Tectum Reorganizes over Development and Is Influenced by Visual Experience

Abstract

Spontaneous patterns of activity in the developing visual system may play an important role in shaping the brain for function. During the period 4-9 dpf (days post-fertilization), larval zebrafish learn to hunt prey, a behavior that is critically dependent on the optic tectum. However, how spontaneous activity develops in the tectum over this period and the effect of visual experience are unknown. Here we performed two-photon calcium imaging of GCaMP6s zebrafish larvae at all days from 4 to 9 dpf. Using recently developed graph theoretic techniques, we found significant changes in both single-cell and population activity characteristics over development. In particular, we identified days 5-6 as a critical moment in the reorganization of the underlying functional network. Altering visual experience early in development altered the statistics of tectal activity, and dark rearing also caused a long-lasting deficit in the ability to capture prey. Thus, tectal development is shaped by both intrinsic factors and visual experience.

Graph analysis of looming-selective networks in the tectum, and its replication in a simple computational model

“…Synaptic competition promoted connectivity in weakly connected neurons, while "punishing" overconnected cells, which created light-frame, openwork graph structures (Fiete et al, 2010), and STDP coordinated activity within sub-networks, increasing modularity (Stam et al, 2010;Litwin-Kumar and Doiron, 2014), similar to how it was previously described for other types of plasticity (Damicelli et al, 2018;Triplett et al, 2018). We did not observe changes in the number of neuronal ensembles (Avitan et al, 2017;Pietri et al, 2017), but we believe this is because our experiments were not suited for ensemble detection, as we worked with strong shared inputs that reliably activated almost every neuron in the network. This made our experiments very much unlike the case of spontaneous activity, when different sub-networks get activated randomly, with activity propagating within modules more readily than between them (Avitan et al, 2017).…”

“…We did not observe changes in the number of neuronal ensembles (Avitan et al, 2017;Pietri et al, 2017), but we believe this is because our experiments were not suited for ensemble detection, as we worked with strong shared inputs that reliably activated almost every neuron in the network. This made our experiments very much unlike the case of spontaneous activity, when different sub-networks get activated randomly, with activity propagating within modules more readily than between them (Avitan et al, 2017).…”

“…As a simplistic way to assess that, we performed factor analysis of trial-to-trial population response variability, and checked whether it changed in development, as it is known to happen for tectal spontaneous activity (Xu et al, 2011). The total number of principle components needed to describe 80% of variance across cells (Avitan et al, 2017) was similar in stage 46 and 49 tadpoles, with a possibility of mild increase in response richness in older animals (p >0.05 for each stimulus type alone, but consistent across stimuli: 51±14 and 65±28, p t =0.1 for looming; 49±12 and 62±32, p t =0.2 for flashes; 51±14 and 64±26, p t =0.1 for scrambled).…”

“…To better describe and visualize network activation variability, we looked for ensembles of tectal cells using spectral clustering (Thompson et al, 2016). Unlike for spontaneous activity, we could not easily aggregate activity states into clusters (Avitan et al, 2017), as activation in our networks was driven by shared sensory inputs. Instead, we subtracted average responses of each cell from its responses in individual trials, and calculated pairwise correlations on the remaining "anomalies" of trial-by-trial activation ( Figure 3D).…”

Behavioral signatures of a developing neural code

“…We binarized the raster of the neurons based on a threshold of mean amplitude plus two standard deviations for each neuron (Avitan et al, 2017). We then estimated the probability that a neuron was active in response to a particular stimulus s, P(r = 1|s), in the frames between 1.5 s and 3.5 s after any of the presentations of stimulus s. The probability of the stimulus, P(s), was uniform by experimental design.…”