Persistent Activation Blobs in Spiking Neural Networks with Mexican Hat Connectivity

Piekniewski, Filip

doi:10.1007/978-3-642-13232-2_9

Cited by 1 publication

(2 citation statements)

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“…These mechanisms, that are typically modelled by lateral connectivity, are not only attractive from a conceptual point of view but are scenarios worthy of exploration when building models, as they can provide simple mechanisms by which information can be sent to other neurons. Evidence of short range excitatory/long range inhibitory connectivity has been identified in cortex organisation, see for instance [6] – [8] and references therein.…”

Section: Resultsmentioning

confidence: 99%

“…short range excitatory, long range inhibitory connections. This type of connectivity has been identified in cortex organisation [6] – [8] and offers a “plausible” neural network implementation for reading out the information (the decision) encoded in the output layer. We term this scenario “non-democratic” decision making, as the participation of the neurons in the decision is influenced by others via recurrent (lateral) connections.…”

Section: Introductionmentioning

confidence: 99%

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Democratic Population Decisions Result in Robust Policy-Gradient Learning: A Parametric Study with GPU Simulations

et al. 2011

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High performance computing on the Graphics Processing Unit (GPU) is an emerging field driven by the promise of high computational power at a low cost. However, GPU programming is a non-trivial task and moreover architectural limitations raise the question of whether investing effort in this direction may be worthwhile. In this work, we use GPU programming to simulate a two-layer network of Integrate-and-Fire neurons with varying degrees of recurrent connectivity and investigate its ability to learn a simplified navigation task using a policy-gradient learning rule stemming from Reinforcement Learning. The purpose of this paper is twofold. First, we want to support the use of GPUs in the field of Computational Neuroscience. Second, using GPU computing power, we investigate the conditions under which the said architecture and learning rule demonstrate best performance. Our work indicates that networks featuring strong Mexican-Hat-shaped recurrent connections in the top layer, where decision making is governed by the formation of a stable activity bump in the neural population (a “non-democratic” mechanism), achieve mediocre learning results at best. In absence of recurrent connections, where all neurons “vote” independently (“democratic”) for a decision via population vector readout, the task is generally learned better and more robustly. Our study would have been extremely difficult on a desktop computer without the use of GPU programming. We present the routines developed for this purpose and show that a speed improvement of 5x up to 42x is provided versus optimised Python code. The higher speed is achieved when we exploit the parallelism of the GPU in the search of learning parameters. This suggests that efficient GPU programming can significantly reduce the time needed for simulating networks of spiking neurons, particularly when multiple parameter configurations are investigated.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%