Sensory information is often processed by populations of neurons that vary with respect to their levels of spiking activity. It appears plausible that neurons that fire more often are capable of following faster changes in the input signal than those with lower firing rates. In this study, we test this intuitive assumption by systematically investigating how the spiking activity of a neuron affects information transmission. To this end, we employ the coherence function as a spectral measure of information transmission and study the relation between the width of the coherence function, which we call coding bandwidth, and the firing rate. The reexamination of experimental data from a previous study on weakly-electric fish and the simulation of biologically inspired neuron models fitted to real neurons reveal indeed a significant correlation between firing rate and coding bandwidth. In addition, known analytical expressions for the coherence function for the stochastic leaky integrate-and-fire model demonstrate a linear relation for high firing rates. However, in contrast to intuition, the width of the coherence function becomes independent of the firing rate and is completely set by the membrane time constant for very low firing rates.
Determining the amount of information a neuron receives per unit of time is key to understanding brain connectivity and how neural networks encode and transmit information. In particular, estimating this information flow by distinguishing between excitatory and inhibitory synaptic contributions is critical to understan...
R. M. Delicado-Moll, A. Guillamón, A. E. Teruel et al.· bioRxiv· 0 citations
Typical changes in a neuronal circuit’s operating regime — such as shifts in sensorimotor conditions or in brain state — reshuffle firing rates across its neurons. One well-established key organising principle is rate preservation: firing rates correlate strongly across conditions. Here, we demonstrate that the joint d...
This work presents a Hebbian local learning rule that models synaptic modification as a function of calcium traces tracking neuronal activity and demonstrates how spike timing and rate can be complementary in their role of shaping the connectivity of spiking neural networks.
Willian Soares Girāo, Nicoletta Risi, Caroline Geisler et al.· Neuromorphic Computing and E...· 0 citations
Neurons involved in a computation are remarkably diverse. They display a range of thresholds, time scales and spiking history dependence, and often exhibit adaptation that enhances computational power by concentrating spikes at transient stimuli. We seek basic organizational principles for coding with cellular heteroge...
Raphaël Lafond-Mercier, Leonard Maler, André Longtin· bioRxiv· 0 citations
Sensory pathways differ widely across species and modalities in anatomy and function. Yet many solve variants of the same ancient, pervasive problem: quickly detecting meaningful changes in the world. Sometimes animals need to make these detections from noisy signals with millisecond precision to survive. We suggest ho...
Travis Monk, Shivaram Mani, Paul Hurley et al.· Neural Computation· 0 citations
A spiking neuron is a dynamical system exhibiting a limit cycle (a spike) when subject to sufficiently excitatory input. Famous neuroscience experiments by Bryant and Segundo as well as Mainen and Sejnowski revealed that the spike times of some biological neurons are more reliable when subject to time-varying stimuli c...
Léopold Van Brandt, A. Ascoli, Michele Bonnin et al.· 0 citations
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