16 research outputs found
Simultaneous multi-patch-clamp and extracellular-array recordings: Single neuron reflects network activity
The increasing number of recording electrodes enhances the capability of
capturing the network's cooperative activity, however, using too many monitors
might alter the properties of the measured neural network and induce noise.
Using a technique that merges simultaneous multi-patch-clamp and
multi-electrode array recordings of neural networks in-vitro, we show that the
membrane potential of a single neuron is a reliable and super-sensitive probe
for monitoring such cooperative activities and their detailed rhythms.
Specifically, the membrane potential and the spiking activity of a single
neuron are either highly correlated or highly anti-correlated with the
time-dependent macroscopic activity of the entire network. This surprising
observation also sheds light on the cooperative origin of neuronal burst in
cultured networks. Our findings present an alternative flexible approach to the
technique based on a massive tiling of networks by large-scale arrays of
electrodes to monitor their activity.Comment: 36 pages, 9 figure
Broadband Macroscopic Cortical Oscillations Emerge from Intrinsic Neuronal Response Failures
Broadband spontaneous macroscopic neural oscillations are rhythmic cortical
firing which were extensively examined during the last century, however, their
possible origination is still controversial. In this work we show how
macroscopic oscillations emerge in solely excitatory random networks and
without topological constraints. We experimentally and theoretically show that
these oscillations stem from the counterintuitive underlying mechanism - the
intrinsic stochastic neuronal response failures. These neuronal response
failures, which are characterized by short-term memory, lead to cooperation
among neurons, resulting in sub- or several- Hertz macroscopic oscillations
which coexist with high frequency gamma oscillations. A quantitative interplay
between the statistical network properties and the emerging oscillations is
supported by simulations of large networks based on single-neuron in-vitro
experiments and a Langevin equation describing the network dynamics. Results
call for the examination of these oscillations in the presence of inhibition
and external drives.Comment: 21 pages, 5 figure
Fast Reversible Learning based on Neurons functioning as Anisotropic Multiplex Hubs
Neural networks are composed of neurons and synapses, which are responsible
for learning in a slow adaptive dynamical process. Here we experimentally show
that neurons act like independent anisotropic multiplex hubs, which relay and
mute incoming signals following their input directions. Theoretically, the
observed information routing enriches the computational capabilities of neurons
by allowing, for instance, equalization among different information routes in
the network, as well as high-frequency transmission of complex time-dependent
signals constructed via several parallel routes. In addition, this kind of hubs
adaptively eliminate very noisy neurons from the dynamics of the network,
preventing masking of information transmission. The timescales for these
features are several seconds at most, as opposed to the imprint of information
by the synaptic plasticity, a process which exceeds minutes. Results open the
horizon to the understanding of fast and adaptive learning realities in higher
cognitive functionalities of the brain.Comment: 6 pages, 4 figure
Neuronal Response Impedance Mechanism Implementing Cooperative Networks with Low Firing Rates and Microseconds Precision
Realizations of low firing rates in neural networks usually require globally
balanced distributions among excitatory and inhibitory links, while feasibility
of temporal coding is limited by neuronal millisecond precision. We show that
cooperation, governing global network features, emerges through nodal
properties, as opposed to link distributions. Using in vitro and in vivo
experiments we demonstrate microsecond precision of neuronal response timings
under low stimulation frequencies, whereas moderate frequencies result in a
chaotic neuronal phase characterized by degraded precision. Above a critical
stimulation frequency, which varies among neurons, response failures were found
to emerge stochastically such that the neuron functions as a low pass filter,
saturating the average inter-spike-interval. This intrinsic neuronal response
impedance mechanism leads to cooperation on a network level, such that firing
rates are suppressed towards the lowest neuronal critical frequency
simultaneously with neuronal microsecond precision. Our findings open up
opportunities of controlling global features of network dynamics through few
nodes with extreme properties.Comment: 35 pages, 13 figure
Significant anisotropic neuronal refractory period plasticity
Refractory periods are an unavoidable feature of excitable elements, resulting in necessary time-lags for re-excitation. Herein, we measure neuronal absolute refractory periods (ARPs) in synaptic blocked neuronal cultures. In so doing, we show that their duration can be significantly extended by dozens of milliseconds using preceding evoked spikes generated by extracellular stimulations. The ARP increases with the frequency of preceding stimulations, and saturates at the intermittent phase of the neuronal response latency, where a short relative refractory period might appear. Nevertheless, preceding stimulations via a different extracellular route does not affect the ARP. It is also found to be independent of preceding intracellular stimulations. All these features strongly suggest that the anisotropic ARPs originate in neuronal dendrites. The results demonstrate the fast and significant plasticity of the neuronal ARP, depending on the firing activity of its connecting neurons, which is expected to affect network dynamics
Embedding information in physically generated random bit sequences while maintaining certified randomness
Ultrafast physical random bit generation at hundreds of Gb/s rates, with verified randomness, is a crucial ingredient in secure communication and has recently emerged using optics-based physical systems. Here we examine the inverse problem and measure the ratio of information bits that can be systematically embedded in a random bit sequence without degrading its certified randomness. These ratios exceed 0.01 in experimentally obtained long random bit sequences. Based on these findings we propose a high-capacity private-key cryptosystem with a finite key length, where the existence as well as the content of the communication is concealed in the random sequence. Our results call for a rethinking of the current quantitative definition of practical classical randomness as well as the measure of randomness generated by quantum methods, which have to include bounds using the proposed inverse information embedding method