Figure 2: A generic neural circuit driving behavioural variability.

(a) When the premotor-to-motor projections are topographically organized, fluctuations in the inputs to the effectors are large. Left: circuit architecture. In the motor network, neurons in the same group (same colour, projecting to the same effector) share a fraction f of premotor inputs (arrows coloured as the corresponding group) and have a fraction 1−f of non-shared inputs (grey arrows). Right: inputs to the effectors are variable . (b,c) In the premotor network, single neuron activity is irregular and neurons are very weakly correlated. (b) Top: Raster plots of E (red) and I (blue) premotor neurons. Bottom: instantaneous mean activity of E and I neurons (bars: 100 ms and 10 Hz). (c) Voltage of two excitatory premotor neurons (top) and their spike CCs (bottom). (d,e) In the motor network, single neuron activity is irregular and neurons are correlated. (d) Top: Raster plots of E and I populations. Bottom: instantaneous mean activity of E and I neurons (scale bar: 100 ms and 10 Hz). (e) Voltage traces of two neurons in the motor network projecting to different (top) and same (bottom) effectors. Bottom: pairs of neurons projecting to the same effector are substantially correlated (right); pairs projecting to different effectors are weakly correlated (left; see also Fig. 4). (f) The variability of the inputs to the effectors increases with the fraction of shared inputs and is substantial even if the number of inputs per effector, M, is large.This is because in the motor network the activities of the neurons belonging to the same group are correlated. (g) The circuit amplifies fluctuations. The amplification factor, , (see Methods section) measures the ratio between the variability of the effectors and of the input to the motor network . It increases linearly with the average number of synapses per neuron, K (mean±s.e.m.; see also Supplementary Fig. 3f). (h) Connection probability of two neurons in the motor network depends on their distance (see Methods section) with a footprint σ_rec. The diameter of the motor network is λ=1,000 μm. (i) decreases when narrowing the footprint of the recurrent interactions in the motor network. Red dot in the figure corresponds to the parameters used in a–e.