The impact of spike timing variability on the signal-encoding performance of neural spiking models

It remains unclear whether the variability of neuronal spike trains in vivo arises due to biological noise sources or represents highly precise encoding of temporally varying synaptic input signals. Determining the variability of spike timing can provide fundamental insights into the nature of strategies used in the brain to represent and transmit information in the form of discrete spike trains. In this study, we employ a signal estimation paradigm to determine how variability in spike timing affects encoding of random time-varying signals. We assess this for two types of spiking models: an integrate-and-fire model with random threshold and a more biophysically realistic stochastic ion channel model. Using the coding fraction and mutual information as information-theoretic measures, we quantify the efficacy of optimal linear decoding of random inputs from the model outputs and study the relationship between efficacy and variability in the output spike train. Our findings suggest that variability does not necessarily hinder signal decoding for the biophysically plausible encoders examined and that the functional role of spiking variability depends intimately on the nature of the encoder and the signal processing task; variability can either enhance or impede decoding performance

Noise in neurons is message-dependent

Neuronal responses are conspicuously variable. We focus on one particular aspect of that variability: the precision of action potential timing. We show that for common models of noisy spike generation, elementary considerations imply that such variability is a function of the input, and can be made arbitrarily large or small by a suitable choice of inputs. Our considerations are expected to extend to virtually any mechanism of spike generation, and we illustrate them with data from the visual pathway. Thus, a simplification usually made in the application of information theory to neural processing is violated: noise {\sl is not independent of the message}. However, we also show the existence of {\sl error-correcting} topologies, which can achieve better timing reliability than their components.Comment: 6 pages,6 figures. Proceedings of the National Academy of Sciences (in press

Transient Resetting: A Novel Mechanism for Synchrony and Its Biological Examples

The study of synchronization in biological systems is essential for the understanding of the rhythmic phenomena of living organisms at both molecular and cellular levels. In this paper, by using simple dynamical systems theory, we present a novel mechanism, named transient resetting, for the synchronization of uncoupled biological oscillators with stimuli. This mechanism not only can unify and extend many existing results on (deterministic and stochastic) stimulus-induced synchrony, but also may actually play an important role in biological rhythms. We argue that transient resetting is a possible mechanism for the synchronization in many biological organisms, which might also be further used in medical therapy of rhythmic disorders. Examples on the synchronization of neural and circadian oscillators are presented to verify our hypothesis.Comment: 17 pages, 7 figure

Revisiting chaos in stimulus-driven spiking networks: signal encoding and discrimination

Highly connected recurrent neural networks often produce chaotic dynamics, meaning their precise activity is sensitive to small perturbations. What are the consequences for how such networks encode streams of temporal stimuli? On the one hand, chaos is a strong source of randomness, suggesting that small changes in stimuli will be obscured by intrinsically generated variability. On the other hand, recent work shows that the type of chaos that occurs in spiking networks can have a surprisingly low-dimensional structure, suggesting that there may be "room" for fine stimulus features to be precisely resolved. Here we show that strongly chaotic networks produce patterned spikes that reliably encode time-dependent stimuli: using a decoder sensitive to spike times on timescales of 10's of ms, one can easily distinguish responses to very similar inputs. Moreover, recurrence serves to distribute signals throughout chaotic networks so that small groups of cells can encode substantial information about signals arriving elsewhere. A conclusion is that the presence of strong chaos in recurrent networks does not prohibit precise stimulus encoding.Comment: 8 figure

Dopaminergic Regulation of Neuronal Circuits in Prefrontal Cortex

Neuromodulators, like dopamine, have considerable influence on the\ud processing capabilities of neural networks. \ud This has for instance been shown in the working memory functions\ud of prefrontal cortex, which may be regulated by altering the\ud dopamine level. Experimental work provides evidence on the biochemical\ud and electrophysiological actions of dopamine receptors, but there are few \ud theories concerning their significance for computational properties \ud (ServanPrintzCohen90,Hasselmo94).\ud We point to experimental data on neuromodulatory regulation of \ud temporal properties of excitatory neurons and depolarization of inhibitory \ud neurons, and suggest computational models employing these effects.\ud Changes in membrane potential may be modelled by the firing threshold,\ud and temporal properties by a parameterization of neuronal responsiveness \ud according to the preceding spike interval.\ud We apply these concepts to two examples using spiking neural networks.\ud In the first case, there is a change in the input synchronization of\ud neuronal groups, which leads to\ud changes in the formation of synchronized neuronal ensembles.\ud In the second case, the threshold\ud of interneurons influences lateral inhibition, and the switch from a \ud winner-take-all network to a parallel feedforward mode of processing.\ud Both concepts are interesting for the modeling of cognitive functions and may\ud have explanatory power for behavioral changes associated with dopamine \ud regulation

Phasic firing and coincidence detection by subthreshold negative feedback: divisive or subtractive or, better, both

Phasic neurons typically fire only for a fast-rising input, say at the onset of a step current, but not for steady or slow inputs, a property associated with type III excitability. Phasic neurons can show extraordinary temporal precision for phase locking and coincidence detection. Exemplars are found in the auditory brain stem where precise timing is used in sound localization. Phasicness at the cellular level arises from a dynamic, voltage-gated, negative feedback that can be recruited subthreshold, preventing the neuron from reaching spike threshold if the voltage does not rise fast enough. We consider two mechanisms for phasicness: a low threshold potassium current (subtractive mechanism) and a sodium current with subthreshold inactivation (divisive mechanism). We develop and analyze three reduced models with either divisive or subtractive mechanisms or both to gain insight into the dynamical mechanisms for the potentially high temporal precision of type III-excitable neurons. We compare their firing properties and performance for a range of stimuli. The models have characteristic non-monotonic input-output relations, firing rate vs. input intensity, for either stochastic current injection or Poisson-timed excitatory synaptic conductance trains. We assess performance according to precision of phase-locking and coincidence detection by the models' responses to repetitive packets of unitary excitatory synaptic inputs with more or less temporal coherence. We find that each mechanism contributes features but best performance is attained if both are present. The subtractive mechanism confers extraordinary precision for phase locking and coincidence detection but only within a restricted parameter range when the divisive mechanism of sodium inactivation is inoperative. The divisive mechanism guarantees robustness of phasic properties, without compromising excitability, although with somewhat less precision. Finally, we demonstrate that brief transient inhibition if properly timed can enhance the reliability of firing.Postprint (published version