Network-timing-dependent plasticity

Bursts of activity in networks of neurons are thought to convey salient information and drive synaptic plasticity. Here we report that network bursts also exert a profound effect on Spike-Timing-Dependent Plasticity (STDP). In acute slices of juvenile rat somatosensory cortex we paired a network burst, which alone induced long-term depression (LTD), with STDP-induced long-term potentiation (LTP) and LTD. We observed that STDP-induced LTP was either unaffected, blocked or flipped into LTD by the network burst, and that STDP-induced LTD was either saturated or flipped into LTP, depending on the relative timing of the network burst with respect to spike coincidences of the STDP event. We hypothesized that network bursts flip STDP-induced LTP to LTD by depleting resources needed for LTP and therefore developed a resource-dependent STDP learning rule. In a model neural network under the influence of the proposed resource-dependent STDP rule, we found that excitatory synaptic coupling was homeostatically regulated to produce power law distributed burst amplitudes reflecting self-organized criticality, a state that ensures optimal information coding

An algorithm to predict the connectome of neural microcircuits

Experimentally mapping synaptic connections, in terms of the numbers and locations of their synapses and estimating connection probabilities, is still not a tractable task, even for small volumes of tissue. In fact, the six layers of the neocortex contain thousands of unique types of synaptic connections between the many different types of neurons, of which only a handful have been characterized experimentally. Here we present a theoretical framework and a data-driven algorithmic strategy to digitally reconstruct the complete synaptic connectivity between the different types of neurons in a small well-defined volume of tissue the micro scale connectome of a neural microcircuit. By enforcing a set of established principles of synaptic connectivity, and leveraging interdependencies between fundamental properties of neural microcircuits to constrain the reconstructed connectivity, the algorithm yields three parameters per connection type that predict the anatomy of all types of biologically viable synaptic connections. The predictions reproduce a spectrum of experimental data on synaptic connectivity not used by the algorithm. We conclude that an algorithmic approach to the connectome can serve as a tool to accelerate experimental mapping, indicating the minimal dataset required to make useful predictions, identifying the datasets required to improve their accuracy, testing the feasibility of experimental measurements, and making it possible to test hypotheses of synaptic connectivity

Reconstruction and simulation of neocortical microcircuitry

We present a first-draft digital reconstruction of the microcircuitry of somatosensory cortex of juvenile rat. The reconstruction uses cellular and synaptic organizing principles to algorithmically reconstruct detailed anatomy and physiology from sparse experimental data. An objective anatomical method defines a neocortical volume of 0.29 ± 0.01 mm3 containing ∼31,000 neurons, and patch-clamp studies identify 55 layer-specific morphological and 207 morpho-electrical neuron subtypes. When digitally reconstructed neurons are positioned in the volume and synapse formation is restricted to biological bouton densities and numbers of synapses per connection, their overlapping arbors form ∼8 million connections with ∼37 million synapses. Simulations reproduce an array of in vitro and in vivo experiments without parameter tuning. Additionally, we find a spectrum of network states with a sharp transition from synchronous to asynchronous activity, modulated by physiological mechanisms. The spectrum of network states, dynamically reconfigured around this transition, supports diverse information processing strategies

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Cortical reliability amid noise and chaos

Typical responses of cortical neurons to identical sensory stimuli appear highly variable. It has thus been proposed that the cortex primarily uses a rate code. However, other studies have argued for spike-time coding under certain conditions. The potential role of spike-time coding is directly limited by the internally generated variability of cortical circuits, which remains largely unexplored. Here, we quantify this internally generated variability using a biophysical model of rat neocortical microcircuitry with biologically realistic noise sources. We find that stochastic neurotransmitter release is a critical component of internally generated variability, causing rapidly diverging, chaotic recurrent network dynamics. Surprisingly, the same non-linear recurrent network dynamics can transiently overcome the chaos in response to weak feed-forward thalamocortical inputs, and support reliable spike times with millisecond precision. Our model shows that the noisy and chaotic network dynamics of recurrent cortical microcircuitry are compatible with stimulus-evoked, millisecond spike-time reliability, resolving a long-standing debate

Morphological Diversity Strongly Constrains Synaptic Connectivity and Plasticity

Synaptic connectivity between neurons is naturally constrained by the anatomical overlap of neuronal arbors, the space on the axon available for synapses, and by physiological mechanisms that form synapses at a subset of potential synapse locations. What is not known is how these constraints impact emergent connectivity in a circuit with diverse morphologies. We investigated the role of morphological diversity within and across neuronal types on emergent connectivity in a model of neocortical microcircuitry. We found that the average overlap between the dendritic and axonal arbors of different types of neurons determines neuron-type specific patterns of distance-dependent connectivity, severely constraining the space of possible connectomes. However, higher order connectivity motifs depend on the diverse branching patterns of individual arbors of neurons belonging to the same type. Morphological diversity across neuronal types, therefore, imposes a specific structure on first order connectivity, and morphological diversity within neuronal types imposes a higher order structure of connectivity. We estimate that the morphological constraints resulting from diversity within and across neuron types together lead to a 10-fold reduction of the entropy of possible connectivity configurations, revealing an upper bound on the space explored by structural plasticity

An integration layer for neural simulation: PyNN in the software forest

Following the principle of separation of concerns, there is a trend in the development of neural simulation software away from monolithic simulation tools and towards an ecosystem of specialized components, each of well-defined scope, that can be combined in different combinations according to scientific need [1]. Examples of such components are CSA [2] for specifying network connectivity, NineML [3] for describing the mathematics of individual neuron and synapse models, NeuroML [4] for specifying neuronal morphology and the placement of functional elements, such as ion channels and synapses, within this morphology, Neo [5] for representing electrophysiological signals recorded from simulated neurons and synapses, NEST [6] and NEURON [7] for performing the simulations, and MUSIC [8] for facilitating runtime exchange of data between different software tools.PyNN [9] is a Python API for simulator-independent specification of spiking neuronal network models and simulation protocols. A script written in PyNN can be run on any supported simulator (or neuromorphic hardware platform) without modification. From its conception, PyNN has had an integrative role, making it easier to use multiple simulators in a single project (for cross-checking, etc.) and to port a model from one simulator to another. Recent developments have emphasized still further the potential of the PyNN approach as an integration layer, simplifying the task of gluing together different software components in order to construct a federated neural simulation platform customized to the scientific problem of interest.We report here on a number of recent enhancements to PyNN, each of which involves integration of an external software component with the PyNN API: (1) using CSA specifications of neuronal connectivity in PyNN, with automated pass-through of CSA objects to the underlying simulator, where this is supported (NEST), for efficiency; (2) import and export of NeuroML model descriptions into/from PyNN; (3) integration into PyNN of the Neo library for structured handling of electrophysiology data, greatly increasing the number of output data formats available to PyNN and making it much easier to use the same analysis/visualization tool for both simulation-derived and experimental data; (4) PyNN support for simulator-independent, user-defined neuronal and synapse models defined in NineML (rather than the fixed menu of simulator-independent models previously provided by PyNN); (5) integration into PyNN of the MUSIC library, enabling simultaneous use of multiple different simulators in a single model, defined in a single simulation script.Taken as a whole, these new features are a good illustration both of the merits of Python in general and PyNN in particular as a federation platform/integration tool for neuronal simulation, and of the benefits of a modular approach to neuroscience software development.AcknowledgementsElements of some of these efforts have been reported previously [10]. This work was supported by European Union projects FP7-269921 (BrainScaleS) and FP6-015879 (FACETS).References[1] Cornelis H, Coop AD, Bower JM (2012) A Federated Design for a Neurobiological Simulation Engine: The CBI Federated Software Architecture. PLoS ONE 7(1): e28956. doi:10.1371/journal.pone.0028956[2] Djurfeldt M (2012) The connection-set algebra--a novel formalism for the representation of connectivity structure in neuronal network models. Neuroinformatics. 10(3):287-304. doi:10.1007/s12021-012-9146-1[3] Raikov I, Cannon R, Clewley R, Cornelis H, Davison AP, De Schutter E, Djurfeldt M, Gleeson P, Gorchetchnikov A, Plesser HE, Hill S, Hines ML, Kriener B, Le Franc Y, Lo C-C, Morrison A, Muller E, Ray S, Schwabe L, Szatmary B (2011) NineML: the network interchange for neuroscience modeling language. BMC Neurosci. 12(Suppl 1): P330. doi: 10.1186/1471-2202-12-S1-P330[4] Gleeson P, Crook S, Cannon RC, Hines ML, Billings GO, et al. 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