Theta and gamma rhythmic coding through two spike output modes in the hippocampus during spatial navigation

Hippocampal CA1 neurons generate single spikes and stereotyped bursts of spikes. However, it is unclear how individual neurons dynamically switch between these output modes and whether these two spiking outputs relay distinct information. We performed extracellular recordings in spatially navigating rats and cellular voltage imaging and optogenetics in awake mice. We found that spike bursts are preferentially linked to cellular and network theta rhythms (3–12 Hz) and encode an animal's position via theta phase precession, particularly as animals are entering a place field. In contrast, single spikes exhibit additional coupling to gamma rhythms (30–100 Hz), particularly as animals leave a place field. Biophysical modeling suggests that intracellular properties alone are sufficient to explain the observed input frequency-dependent spike coding. Thus, hippocampal neurons regulate the generation of bursts and single spikes according to frequency-specific network and intracellular dynamics, suggesting that these spiking modes perform distinct computations to support spatial behavior.Fil: Lowet, Eric. Boston University; Estados UnidosFil: Sheehan, Daniel J.. Boston University; Estados UnidosFil: Chialva, Ulises. Universidad Nacional del Sur. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca; ArgentinaFil: De Oliveira Pena, Rodrigo. New Jersey Institute of Technology; Estados UnidosFil: Mount, Rebecca A.. Boston University; Estados UnidosFil: Xiao, Sheng. Boston University; Estados UnidosFil: Zhou, Samuel L.. Boston University; Estados UnidosFil: Tseng, Hua-an. Boston University; Estados UnidosFil: Gritton, Howard. University of Illinois. Urbana - Champaign; Estados UnidosFil: Shroff, Sanaya. Boston University; Estados UnidosFil: Kondabolu, Krishnakanth. Boston University; Estados UnidosFil: Cheung, Cyrus. Boston University; Estados UnidosFil: Wang, Yangyang. Boston University; Estados UnidosFil: Piatkevich, Kiryl D.. Westlake University; ChinaFil: Boyden, Edward S.. McGovern Institute for Brain Research; Estados Unidos. Massachusetts Institute of Technology; Estados UnidosFil: Mertz, Jerome. Boston University; Estados UnidosFil: Hasselmo, Michael E.. Boston University; Estados UnidosFil: Rotstein, Horacio. New Jersey Institute of Technology; Estados UnidosFil: Han, Xue. Boston University; Estados Unido

25th Annual Computational Neuroscience Meeting: CNS-2016

Abstracts of the 25th Annual Computational Neuroscience Meeting: CNS-2016 Seogwipo City, Jeju-do, South Korea. 2–7 July 201

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Tuning Neural Synchronization:The Role of Variable Oscillation Frequencies in Neural Circuits

Brain oscillations emerge during sensory and cognitive processes and have been classified into different frequency bands. Yet, even within the same frequency band and between nearby brain locations, the exact frequencies of brain oscillations can differ. These frequency differences (detuning) have been largely ignored and play little role in current functional theories of brain oscillations. This contrasts with the crucial role that detuning plays in synchronization theory, as originally derived in physical systems. Here, we propose that detuning is equally important to understand synchronization in biological systems. Detuning is a critical control parameter in synchronization, which is not only important in shaping phase-locking, but also in establishing preferred phase relations between oscillators. We review recent evidence that frequency differences between brain locations are ubiquitous and essential in shaping temporal neural coordination. With the rise of powerful experimental techniques to probe brain oscillations, the contributions of exact frequency and detuning across neural circuits will become increasingly clear and will play a key part in developing a new understanding of the role of oscillations in brain function

Tuning Neural Synchronization: The Role of Variable Oscillation Frequencies in Neural Circuits

Brain oscillations emerge during sensory and cognitive processes and have been classified into different frequency bands. Yet, even within the same frequency band and between nearby brain locations, the exact frequencies of brain oscillations can differ. These frequency differences (detuning) have been largely ignored and play little role in current functional theories of brain oscillations. This contrasts with the crucial role that detuning plays in synchronization theory, as originally derived in physical systems. Here, we propose that detuning is equally important to understand synchronization in biological systems. Detuning is a critical control parameter in synchronization, which is not only important in shaping phase-locking, but also in establishing preferred phase relations between oscillators. We review recent evidence that frequency differences between brain locations are ubiquitous and essential in shaping temporal neural coordination. With the rise of powerful experimental techniques to probe brain oscillations, the contributions of exact frequency and detuning across neural circuits will become increasingly clear and will play a key part in developing a new understanding of the role of oscillations in brain function

Prelims

Gamma oscillations contribute significantly to the manner in which neural activity is bound into functional assemblies. The mechanisms that underlie the human gamma response, however, are poorly understood. Previous computational models of gamma rely heavily on the results of invasive recordings in animals, and it is difficult to assess whether these models hold in humans. Computational models of gamma predict specific changes in gamma spectral response with increased excitatory drive. Hence, differences and commonalities between spikes, LFPs and MEG in the spectral responses to changes in excitatory drive can lead to a refinement of existing gamma models. We compared gamma spectral responses to varying contrasts in a monkey dataset acquired previously (Roberts et al., 2013) with spectral responses to similar contrast variations in a new human MEG dataset. We found parametric frequency shifts with increasing contrast in human MEG at the single-subject and the single-trial level, analogous to those observed in the monkey. Additionally, we observed parametric modulations of spectral asymmetry, consistent across spikes, LFP and MEG. However, while gamma power scaled linearly with contrast in MEG, it saturated at high contrasts in both the LFP and spiking data. Thus, while gamma frequency changes to varying contrasts were comparable across spikes, LFP and MEG, gamma power changes were not. This indicates that gamma frequency may be a more stable parameter across scales of measurements and species than gamma power. The comparative approach undertaken here represents a fruitful path towards a better understanding of gamma oscillations in the human brain

Accurate spike time prediction from LFP in monkey visual cortex: A non-linear system identification approach

Aims: The relationship between collective population activity (LFP) and spikes underpins network computation, yet it remains poorly understood. Previous studies utilized pre-defined LFP features to predict spiking from simultaneously recorded LFP, and have reported good prediction of spike bursts but only moderate accuracies for individual spikes. Our aim was to utilize a data-driven approach, without relying on feature selection, to predict individual spike times. Methods: The relationship between LFPs and multi-unit spike trains in monkey early visual cortex during passive viewing of grating stimuli was analyzed using a variant of the general Volterra approach (Laguerre-Volterra network). Network parameters were trained based on a hybrid Genetic Algorithm ? Interior Point optimization method, and model selection was achieved via cross-validation. The Matthews Correlation Coefficient (-1 Results: Single trial MCCs ranged from 0.45 to 0.66 (median=0.60). Superior performance of 2nd order relative to 1st order models indicated a nonlinear relationship between LFPs and spikes in visual cortex. Consistent with other studies, the PDMs of the identified system exhibited low-pass (theta frequency) and high-pass (gamma frequency) characteristics. Conclusions: We successfully predicted multi-unit spike times from local LFPs with reasonable accuracy and without selection of a-priori features. Our approach enhances our understanding of spike precision and spike timing, and of the network principles underlying the neural cod

Areas V1 and V2 show microsaccade-related 3-4-Hz covariation in gamma power and frequency

Neuronal gamma-band synchronization (25-80 Hz) in visual cortex appears sustained and stable during prolonged visual stimulation when investigated with conventional averages across trials. However, recent studies in macaque visual cortex have used single-trial analyses to show that both power and frequency of gamma oscillations exhibit substantial moment-by-moment variation. This has raised the question of whether these apparently random variations might limit the functional role of gamma-band synchronization for neural processing. Here, we studied the moment-by-moment variation in gamma oscillation power and frequency, as well as inter-areal gamma synchronization, by simultaneously recording local field potentials in V1 and V2 of two macaque monkeys. We additionally analyzed electrocorticographic V1 data from a third monkey. Our analyses confirm that gamma-band synchronization is not stationary and sustained but undergoes moment-by-moment variations in power and frequency. However, those variations are neither random and nor a possible obstacle to neural communication. Instead, the gamma power and frequency variations are highly structured, shared between areas and shaped by a microsaccade-related 3-4-Hz theta rhythm. Our findings provide experimental support for the suggestion that cross-frequency coupling might structure and facilitate the information flow between brain regions