7,450 research outputs found
Statistical Mechanics of Recurrent Neural Networks I. Statics
A lecture notes style review of the equilibrium statistical mechanics of
recurrent neural networks with discrete and continuous neurons (e.g. Ising,
coupled-oscillators). To be published in the Handbook of Biological Physics
(North-Holland). Accompanied by a similar review (part II) dealing with the
dynamics.Comment: 49 pages, LaTe
Cell assembly dynamics of sparsely-connected inhibitory networks: a simple model for the collective activity of striatal projection neurons
Striatal projection neurons form a sparsely-connected inhibitory network, and
this arrangement may be essential for the appropriate temporal organization of
behavior. Here we show that a simplified, sparse inhibitory network of
Leaky-Integrate-and-Fire neurons can reproduce some key features of striatal
population activity, as observed in brain slices [Carrillo-Reid et al., J.
Neurophysiology 99 (2008) 1435{1450]. In particular we develop a new metric to
determine the conditions under which sparse inhibitory networks form
anti-correlated cell assemblies with time-varying activity of individual cells.
We found that under these conditions the network displays an input-specific
sequence of cell assembly switching, that effectively discriminates similar
inputs. Our results support the proposal [Ponzi and Wickens, PLoS Comp Biol 9
(2013) e1002954] that GABAergic connections between striatal projection neurons
allow stimulus-selective, temporally-extended sequential activation of cell
assemblies. Furthermore, we help to show how altered intrastriatal GABAergic
signaling may produce aberrant network-level information processing in
disorders such as Parkinson's and Huntington's diseases.Comment: 22 pages, 9 figure
Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms
Advancing the size and complexity of neural network models leads to an ever
increasing demand for computational resources for their simulation.
Neuromorphic devices offer a number of advantages over conventional computing
architectures, such as high emulation speed or low power consumption, but this
usually comes at the price of reduced configurability and precision. In this
article, we investigate the consequences of several such factors that are
common to neuromorphic devices, more specifically limited hardware resources,
limited parameter configurability and parameter variations. Our final aim is to
provide an array of methods for coping with such inevitable distortion
mechanisms. As a platform for testing our proposed strategies, we use an
executable system specification (ESS) of the BrainScaleS neuromorphic system,
which has been designed as a universal emulation back-end for neuroscientific
modeling. We address the most essential limitations of this device in detail
and study their effects on three prototypical benchmark network models within a
well-defined, systematic workflow. For each network model, we start by defining
quantifiable functionality measures by which we then assess the effects of
typical hardware-specific distortion mechanisms, both in idealized software
simulations and on the ESS. For those effects that cause unacceptable
deviations from the original network dynamics, we suggest generic compensation
mechanisms and demonstrate their effectiveness. Both the suggested workflow and
the investigated compensation mechanisms are largely back-end independent and
do not require additional hardware configurability beyond the one required to
emulate the benchmark networks in the first place. We hereby provide a generic
methodological environment for configurable neuromorphic devices that are
targeted at emulating large-scale, functional neural networks
A unified approach to linking experimental, statistical and computational analysis of spike train data
A fundamental issue in neuroscience is how to identify the multiple biophysical mechanisms through which neurons generate observed patterns of spiking activity. In previous work, we proposed a method for linking observed patterns of spiking activity to specific biophysical mechanisms based on a state space modeling framework and a sequential Monte Carlo, or particle filter, estimation algorithm. We have shown, in simulation, that this approach is able to identify a space of simple biophysical models that were consistent with observed spiking data (and included the model that generated the data), but have yet to demonstrate the application of the method to identify realistic currents from real spike train data. Here, we apply the particle filter to spiking data recorded from rat layer V cortical neurons, and correctly identify the dynamics of an slow, intrinsic current. The underlying intrinsic current is successfully identified in four distinct neurons, even though the cells exhibit two distinct classes of spiking activity: regular spiking and bursting. This approach – linking statistical, computational, and experimental neuroscience – provides an effective technique to constrain detailed biophysical models to specific mechanisms consistent with observed spike train data.Published versio
A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)
Neuromorphic computing systems comprise networks of neurons that use
asynchronous events for both computation and communication. This type of
representation offers several advantages in terms of bandwidth and power
consumption in neuromorphic electronic systems. However, managing the traffic
of asynchronous events in large scale systems is a daunting task, both in terms
of circuit complexity and memory requirements. Here we present a novel routing
methodology that employs both hierarchical and mesh routing strategies and
combines heterogeneous memory structures for minimizing both memory
requirements and latency, while maximizing programming flexibility to support a
wide range of event-based neural network architectures, through parameter
configuration. We validated the proposed scheme in a prototype multi-core
neuromorphic processor chip that employs hybrid analog/digital circuits for
emulating synapse and neuron dynamics together with asynchronous digital
circuits for managing the address-event traffic. We present a theoretical
analysis of the proposed connectivity scheme, describe the methods and circuits
used to implement such scheme, and characterize the prototype chip. Finally, we
demonstrate the use of the neuromorphic processor with a convolutional neural
network for the real-time classification of visual symbols being flashed to a
dynamic vision sensor (DVS) at high speed.Comment: 17 pages, 14 figure
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