591 research outputs found
Reconstruction of recurrent synaptic connectivity of thousands of neurons from simulated spiking activity
Dynamics and function of neuronal networks are determined by their synaptic
connectivity. Current experimental methods to analyze synaptic network
structure on the cellular level, however, cover only small fractions of
functional neuronal circuits, typically without a simultaneous record of
neuronal spiking activity. Here we present a method for the reconstruction of
large recurrent neuronal networks from thousands of parallel spike train
recordings. We employ maximum likelihood estimation of a generalized linear
model of the spiking activity in continuous time. For this model the point
process likelihood is concave, such that a global optimum of the parameters can
be obtained by gradient ascent. Previous methods, including those of the same
class, did not allow recurrent networks of that order of magnitude to be
reconstructed due to prohibitive computational cost and numerical
instabilities. We describe a minimal model that is optimized for large networks
and an efficient scheme for its parallelized numerical optimization on generic
computing clusters. For a simulated balanced random network of 1000 neurons,
synaptic connectivity is recovered with a misclassification error rate of less
than 1% under ideal conditions. We show that the error rate remains low in a
series of example cases under progressively less ideal conditions. Finally, we
successfully reconstruct the connectivity of a hidden synfire chain that is
embedded in a random network, which requires clustering of the network
connectivity to reveal the synfire groups. Our results demonstrate how synaptic
connectivity could potentially be inferred from large-scale parallel spike
train recordings.Comment: This is the final version of the manuscript from the publisher which
supersedes our original pre-print version. The spike data used in this paper
and the code that implements our connectivity reconstruction method are
publicly available for download at http://dx.doi.org/10.5281/zenodo.17662 and
http://dx.doi.org/10.5281/zenodo.17663 respectivel
Model-free reconstruction of neuronal network connectivity from calcium imaging signals
A systematic assessment of global neural network connectivity through direct
electrophysiological assays has remained technically unfeasible even in
dissociated neuronal cultures. We introduce an improved algorithmic approach
based on Transfer Entropy to reconstruct approximations to network structural
connectivities from network activity monitored through calcium fluorescence
imaging. Based on information theory, our method requires no prior assumptions
on the statistics of neuronal firing and neuronal connections. The performance
of our algorithm is benchmarked on surrogate time-series of calcium
fluorescence generated by the simulated dynamics of a network with known
ground-truth topology. We find that the effective network topology revealed by
Transfer Entropy depends qualitatively on the time-dependent dynamic state of
the network (e.g., bursting or non-bursting). We thus demonstrate how
conditioning with respect to the global mean activity improves the performance
of our method. [...] Compared to other reconstruction strategies such as
cross-correlation or Granger Causality methods, our method based on improved
Transfer Entropy is remarkably more accurate. In particular, it provides a good
reconstruction of the network clustering coefficient, allowing to discriminate
between weakly or strongly clustered topologies, whereas on the other hand an
approach based on cross-correlations would invariantly detect artificially high
levels of clustering. Finally, we present the applicability of our method to
real recordings of in vitro cortical cultures. We demonstrate that these
networks are characterized by an elevated level of clustering compared to a
random graph (although not extreme) and by a markedly non-local connectivity.Comment: 54 pages, 8 figures (+9 supplementary figures), 1 table; submitted
for publicatio
Stochastic Synapses Enable Efficient Brain-Inspired Learning Machines
Recent studies have shown that synaptic unreliability is a robust and
sufficient mechanism for inducing the stochasticity observed in cortex. Here,
we introduce Synaptic Sampling Machines, a class of neural network models that
uses synaptic stochasticity as a means to Monte Carlo sampling and unsupervised
learning. Similar to the original formulation of Boltzmann machines, these
models can be viewed as a stochastic counterpart of Hopfield networks, but
where stochasticity is induced by a random mask over the connections. Synaptic
stochasticity plays the dual role of an efficient mechanism for sampling, and a
regularizer during learning akin to DropConnect. A local synaptic plasticity
rule implementing an event-driven form of contrastive divergence enables the
learning of generative models in an on-line fashion. Synaptic sampling machines
perform equally well using discrete-timed artificial units (as in Hopfield
networks) or continuous-timed leaky integrate & fire neurons. The learned
representations are remarkably sparse and robust to reductions in bit precision
and synapse pruning: removal of more than 75% of the weakest connections
followed by cursory re-learning causes a negligible performance loss on
benchmark classification tasks. The spiking neuron-based synaptic sampling
machines outperform existing spike-based unsupervised learners, while
potentially offering substantial advantages in terms of power and complexity,
and are thus promising models for on-line learning in brain-inspired hardware
Identification of excitatory-inhibitory links and network topology in large-scale neuronal assemblies from multi-electrode recordings
Functional-effective connectivity and network topology are nowadays key issues for studying brain physiological functions and pathologies. Inferring neuronal connectivity from electrophysiological recordings presents open challenges and unsolved problems. In this work, we present a cross-correlation based method for reliably estimating not only excitatory but also inhibitory links, by analyzing multi-unit spike activity from large-scale neuronal networks. The method is validated by means of realistic simulations of large-scale neuronal populations. New results related to functional connectivity estimation and network topology identification obtained by experimental electrophysiological recordings from high-density and large-scale (i.e., 4096 electrodes) microtransducer arrays coupled to in vitro neural populations are presented. Specifically, we show that: (i) functional inhibitory connections are accurately identified in in vitro cortical networks, providing that a reasonable firing rate and recording length are achieved; (ii) small-world topology, with scale-free and rich-club features are reliably obtained, on condition that a minimum number of active recording sites are available. The method and procedure can be directly extended and applied to in vivo multi-units brain activity recordings
Single Biological Neurons as Temporally Precise Spatio-Temporal Pattern Recognizers
This PhD thesis is focused on the central idea that single neurons in the
brain should be regarded as temporally precise and highly complex
spatio-temporal pattern recognizers. This is opposed to the prevalent view of
biological neurons as simple and mainly spatial pattern recognizers by most
neuroscientists today. In this thesis, I will attempt to demonstrate that this
is an important distinction, predominantly because the above-mentioned
computational properties of single neurons have far-reaching implications with
respect to the various brain circuits that neurons compose, and on how
information is encoded by neuronal activity in the brain. Namely, that these
particular "low-level" details at the single neuron level have substantial
system-wide ramifications. In the introduction we will highlight the main
components that comprise a neural microcircuit that can perform useful
computations and illustrate the inter-dependence of these components from a
system perspective. In chapter 1 we discuss the great complexity of the
spatio-temporal input-output relationship of cortical neurons that are the
result of morphological structure and biophysical properties of the neuron. In
chapter 2 we demonstrate that single neurons can generate temporally precise
output patterns in response to specific spatio-temporal input patterns with a
very simple biologically plausible learning rule. In chapter 3, we use the
differentiable deep network analog of a realistic cortical neuron as a tool to
approximate the gradient of the output of the neuron with respect to its input
and use this capability in an attempt to teach the neuron to perform nonlinear
XOR operation. In chapter 4 we expand chapter 3 to describe extension of our
ideas to neuronal networks composed of many realistic biological spiking
neurons that represent either small microcircuits or entire brain regions
How To Record a Million Synaptic Weights in a Hippocampal Slice
A key step toward understanding the function of a brain circuit is to find its wiring diagram. New methods for optical stimulation and optical recording of neurons make it possible to map circuit connectivity on a very large scale. However, single synapses produce small responses that are difficult to measure on a large scale. Here I analyze how single synaptic responses may be detectable using relatively coarse readouts such as optical recording of somatic calcium. I model a network consisting of 10,000 input axons and 100 CA1 pyramidal neurons, each represented using 19 compartments with voltage-gated channels and calcium dynamics. As single synaptic inputs cannot produce a measurable somatic calcium response, I stimulate many inputs as a baseline to elicit somatic action potentials leading to a strong calcium signal. I compare statistics of responses with or without a single axonal input riding on this baseline. Through simulations I show that a single additional input shifts the distribution of the number of output action potentials. Stochastic resonance due to probabilistic synaptic release makes this shift easier to detect. With ∼80 stimulus repetitions this approach can resolve up to 35% of individual activated synapses even in the presence of 20% recording noise. While the technique is applicable using conventional electrical stimulation and extracellular recording, optical methods promise much greater scaling, since the number of synapses scales as the product of the number of inputs and outputs. I extrapolate from current high-speed optical stimulation and recording methods, and show that this approach may scale up to the order of a million synapses in a single two-hour slice-recording experiment
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