Spiking networks

…dynamics and studying computation in spiking networks. Petrovici is a senior researcher at the University of Bern. The conversation was recorded on 18 June 2026 and released on 23 September 2026; the publisher’s audio runs 1 hour, 41 minutes and 46 seconds.

…Networks with only excitatory synaptic plasticity can become unstable due to runaway excitation, compromising a circuit's ability to store memories. To counter this, homeostatic synaptic plasticity of inhibitory connections provide a matched negative feedback that stabilizes neural activity and any associated learning. In this study, we outline the conditions under which recurrent

Deep feedforward and recurrent networks have become popular choices for models of neural computation, but they do not provide a clear link to the details of the underlying biophysical processes. Here we argue that the reason this link has been difficult to establish is because biological circuits operate in a fundamentally different way. To illustrate this, we present a new theory of neural computation in excitatory-inhibitory spiking networks, which captures the precise computational role of each neuron and every spike. By assuming low-rank recurrent connectivity, we show that spiking populat

…Established simulators for large-scale spiking networks with biologically plausible connectivity and synaptic density have historically targeted CPUs, with GPU support and ports being a relatively recent development[1][2]. Here, we present a work

Coherent oscillations of neural activity are ubiquitous across brain spatial and temporal scales and have been associated with the formation of sensory or behavioral representations [1]. Refined experimental techniques report today a new complexity of this oscillatory activity, especially in the gamma (30-100 Hz) band. Indeed, the frequency of these oscillations shows a large variability through time [2], questioning the classical view of γ oscillations being separated into different frequency bands. Oscillations appear instead in bursts characterised by rapid, synchronous neuronal firing. Thi

Friedemann Zenke discusses learning in spiking networks and the challenges of translating machine-learning methods into biologically plausible rules.

…has attempted to limit the focus on spiking by smoothing over the spike threshold or by approximating spiking dynamics with firing-rate dynamics. Here, we take a novel perspective that captures the full potential of spike-based computation. Based on previous studies of the geometry of efficient spike-coding networks, we consider a population of neurons with low-rank connectivity, allowing

…levels are controlled in biological networks through feedback control (homeostasis), it is not clear what the effects on population coding are, and therefore how performance and energy can be traded off. In this talk we will consider this tradeoff in auto-encoding networks, in which there is a clear definition of performance (the coding loss). We first show how SNNs follow a characteristic trade-off curve between activity levels and coding lo

…and it can induce similarly structured networks to exhibit different behaviors. Yet, relatively few theoretic studies have attempted to detangle the role and function the various types of modulation play throughout the brain. In particular, it is important to understand how changing neuromodulator concentrations can move a network through its space of possible behaviors. A better understanding of neuromodulatory movement through behaviora

…Spiking and firing rate network models can exhibit such sensitivity to initial conditions that are reflected in their dynamic entropy rate and attractor dimensionality computed from th

…we graphically illustrate how neural networks can become robust. We study spiking networks that generate low-dimensional representations, and we show that the neurons’ subthreshold voltages are confined to a convex region in a lower-dimensional voltage subspace, which we call a ‘bounding box.’ Any changes in network parameters (such as number of neurons, dimensionality of inputs, firing thresholds, synaptic weights, or transmission delays) can all be understood as def