169 research outputs found
The Attentional Routing Circuit: Receptive Field Modulation Through Nonlinear Dendritic Interactions
We present a model of attentional routing called the Attentional Routing Circuit (ARC) that extends an existing model of spiking neurons with dendritic nonlinearities. Specifically, we employ the Poirazi et al. (2003) pyramidal neuron in a population coding framework. ARC demonstrates that the dendritic nonlinearities can be exploited to result in selective routing, with a decrease in the number of cells needed by a factor of ~5 as compared with a linear dendrite model.

Routing of attended information occurs through the modulation of feedforward visual signals by a cortical control signal specifying the location and size of the attended target. The model is fully specified in spiking single cells. Our approach differs from past work on shifter circuits by having more efficient control, and using a more biologically detailed substrate. Our approach differs from existing models that use gain fields by providing precise hypotheses about how the control signals are generated and distributed in a hierarchical model in spiking neurons. Further, the model accounts for numerous experimental findings regarding the timing, strength and extent of attentional modulation in ventral stream areas, and the perceived contrast enhancement of attended stimuli.

To further demonstrate the plausibility of ARC, it is applied to the attention experiments of Womelsdorf et al. (2008) and tested in detail. For the simulations, the model has only two free parameters that influence its ability to match the experimental data, and without fitting, we show that it can account for the experimental observations of changes in receptive field (RF) gain and position with attention in macaques. In sum, the model provides an explanation of RF modulation as well as testable predictions about nonlinear cortical dendrites and attentional changes of receptive field properties
Learning over time using a neuromorphic adaptive control algorithm for robotic arms
In this paper, we explore the ability of a robot arm to learn the underlying
operation space defined by the positions (x, y, z) that the arm's end-effector
can reach, including disturbances, by deploying and thoroughly evaluating a
Spiking Neural Network SNN-based adaptive control algorithm. While traditional
control algorithms for robotics have limitations in both adapting to new and
dynamic environments, we show that the robot arm can learn the operational
space and complete tasks faster over time. We also demonstrate that the
adaptive robot control algorithm based on SNNs enables a fast response while
maintaining energy efficiency. We obtained these results by performing an
extensive search of the adaptive algorithm parameter space, and evaluating
algorithm performance for different SNN network sizes, learning rates, dynamic
robot arm trajectories, and response times. We show that the robot arm learns
to complete tasks 15% faster in specific experiment scenarios such as scenarios
with six or nine random target points
Python Scripting in the Nengo Simulator
Nengo (http://nengo.ca) is an open-source neural simulator that has been greatly enhanced by the recent addition of a Python script interface. Nengo provides a wide range of features that are useful for physiological simulations, including unique features that facilitate development of population-coding models using the neural engineering framework (NEF). This framework uses information theory, signal processing, and control theory to formalize the development of large-scale neural circuit models. Notably, it can also be used to determine the synaptic weights that underlie observed network dynamics and transformations of represented variables. Nengo provides rich NEF support, and includes customizable models of spike generation, muscle dynamics, synaptic plasticity, and synaptic integration, as well as an intuitive graphical user interface. All aspects of Nengo models are accessible via the Python interface, allowing for programmatic creation of models, inspection and modification of neural parameters, and automation of model evaluation. Since Nengo combines Python and Java, it can also be integrated with any existing Java or 100% Python code libraries. Current work includes connecting neural models in Nengo with existing symbolic cognitive models, creating hybrid systems that combine detailed neural models of specific brain regions with higher-level models of remaining brain areas. Such hybrid models can provide (1) more realistic boundary conditions for the neural components, and (2) more realistic sub-components for the larger cognitive models
A Neural Model for Insect Steering Applied to Olfaction and Path Integration
Many animal behaviors require orientation and steering with respect to the environment. For insects, a key brain area involved in spatial orientation and navigation is the central complex. Activity in this neural circuit has been shown to track the insectâs current heading relative to its environment and has also been proposed to be the substrate of path inte-gration. However, it remains unclear how the output of the central complex is integrated into motor commands. Central complex output neurons project to the lateral accessory lobes (LAL), from which descending neurons project to thoracic motor centers. Here, we present a computational model of a simple neural network that has been described anatomically and physiologically in the LALs of male silkworm moths, in the context of odor-mediated steering. We present and analyze two versions of this network, one rate based and one based on spiking neurons. The mod-eled network consists of an inhibitory local interneuron and a bistable descending neuron (flip-flop) that both receive input in the LAL. The flip-flop neuron projects onto neck motor neurons to induce steering. We show that this simple computational model not only replicates the basic parameters of male silkworm moth behavior in a simulated odor plume but can also take input from a computational model of path integration in the central complex and use it to steer back to a point of origin. Fur-thermore, we find that increasing the level of detail within the model im-proves the realism of the modelâs behavior, leading to the emergence of looping behavior as an orientation strategy. Our results suggest that descending neurons originating in the LALs, such as flip-flop neurons, are sufficient to mediate multiple steering behaviors. This study is therefore a first step to close the gap between orientation circuits in the central complex and downstream motor centers
Reservoir Memory Machines as Neural Computers
Differentiable neural computers extend artificial neural networks with an
explicit memory without interference, thus enabling the model to perform
classic computation tasks such as graph traversal. However, such models are
difficult to train, requiring long training times and large datasets. In this
work, we achieve some of the computational capabilities of differentiable
neural computers with a model that can be trained very efficiently, namely an
echo state network with an explicit memory without interference. This extension
enables echo state networks to recognize all regular languages, including those
that contractive echo state networks provably can not recognize. Further, we
demonstrate experimentally that our model performs comparably to its
fully-trained deep version on several typical benchmark tasks for
differentiable neural computers.Comment: In print at the special issue 'New Frontiers in Extremely Efficient
Reservoir Computing' of IEEE TNNL
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A neural representation of continuous space using fractional binding
We present a novel method for constructing neurally imple-mented spatial representations that we show to be useful forbuilding models of spatial cognition. This method representscontinuous (i.e., real-valued) spaces using neurons, and iden-tifies a set of operations for manipulating these representa-tions. Specifically, we use âfractional bindingâ to constructâspatial semantic pointersâ (SSPs) that we use to generate andmanipulate representations of spatial maps encoding the posi-tions of objects. We show how these representations can betransformed to answer queries about the location and identitiesof objects, move the relative or global position of items, andanswer queries about regions of space, among other things.We demonstrate that the neural implementation in spiking net-works of SSPs have similar accuracy and capacity as the math-ematical ideal
A functional spiking-neuron model of activity-silent working memory in humans based on calcium-mediated short-term synaptic plasticity
In this paper, we present a functional spiking-neuron model of human working memory (WM). This model combines neural firing for encoding of information with activity-silent maintenance. While it used to be widely assumed that information in WM is maintained through persistent recurrent activity, recent studies have shown that information can be maintained without persistent firing; instead, information can be stored in activity-silent states. A candidate mechanism underlying this type of storage is short-term synaptic plasticity (STSP), by which the strength of connections between neurons rapidly changes to encode new information. To demonstrate that STSP can lead to functional behavior, we integrated STSP by means of calcium-mediated synaptic facilitation in a large-scale spiking-neuron model and added a decision mechanism. The model was used to simulate a recent study that measured behavior and EEG activity of participants in three delayed-response tasks. In these tasks, one or two visual gratings had to be maintained in WM, and compared to subsequent probes. The original study demonstrated that WM contents and its priority status could be decoded from neural activity elicited by a task-irrelevant stimulus displayed during the activity-silent maintenance period. In support of our model, we show that it can perform these tasks, and that both its behavior as well as its neural representations are in agreement with the human data. We conclude that information in WM can be effectively maintained in activity-silent states by means of calcium-mediated STSP
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Towards a Cognitively Realistic Representation of Word Associations
The ability to associate words is an important cognitive skill.In this study we investigate different methods for representingword associations in the brain, using the Remote AssociatesTest (RAT) as a task. We explore representations derived fromfree association norms and statistical n-gram data. Althoughn-gram representations yield better performance on the test, acloser match with the human performance is obtained with rep-resentations derived from free associations. We propose thatword association strengths derived from free associations playan important role in the process of RAT solving. Furthermore,we show that this model can be implemented in spiking neu-rons, and estimate the number of biologically realistic neuronsthat would suffice for an accurate representation
Reservoir based spiking models for univariate Time Series Classification
A variety of advanced machine learning and deep learning algorithms achieve state-of-the-art performance on various temporal processing tasks. However, these methods are heavily energy inefficientâthey run mainly on the power hungry CPUs and GPUs. Computing with Spiking Networks, on the other hand, has shown to be energy efficient on specialized neuromorphic hardware, e.g., Loihi, TrueNorth, SpiNNaker, etc. In this work, we present two architectures of spiking models, inspired from the theory of Reservoir Computing and Legendre Memory Units, for the Time Series Classification (TSC) task. Our first spiking architecture is closer to the general Reservoir Computing architecture and we successfully deploy it on Loihi; the second spiking architecture differs from the first by the inclusion of non-linearity in the readout layer. Our second model (trained with Surrogate Gradient Descent method) shows that non-linear decoding of the linearly extracted temporal features through spiking neurons not only achieves promising results, but also offers low computation-overhead by significantly reducing the number of neurons compared to the popular LSM based modelsâmore than 40x reduction with respect to the recent spiking model we compare with. We experiment on five TSC datasets and achieve new SoTA spiking results (âas much as 28.607% accuracy improvement on one of the datasets), thereby showing the potential of our models to address the TSC tasks in a green energy-efficient manner. In addition, we also do energy profiling and comparison on Loihi and CPU to support our claims
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