Limits...
Fast approximate models of large networks

View Article: PubMed Central - HTML

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

This work addresses two key challenges in modeling large neural systems such as the primate visual system... The first challenge is that large detailed models require prohibitively expensive computers and long simulation times... The second is that interpretation of simulation results is difficult due to the large number of model parameters... The present work consists of a computationally efficient model of this approximation, i.e. of the functional differences between NEF neural-circuit models and corresponding lower-dimensional dynamics... The procedure is to model a group of point neurons and their connections within a network, to approximate their aggregate behaviour as a surrogate “population model”, and then to simulate only the population model... These surrogate models have two benefits... The first is the practical benefit of a 1-2 orders-of-magnitude reduction in computation... Efficient parallel implementations are currently in progress, but functionally realistic real-time simulation of 10-10 point neurons is anticipated on a single graphical processing unit (the number depending substantially on network connection statistics)... The second benefit is that this model provides a simple and thorough description of the behaviour of complex networks and how it arises from neuron parameters... The main goal of this work has been to accelerate and enhance insight into NEF models, but the method applies to any network of point neurons with linear synaptic integration... If there are about half as many large singular values in a synaptic weight matrix as there are post-synaptic neurons, the surrogate model will be about as efficient as the full neuron model... However, if there are few large singular values then the population model is much more efficient.

No MeSH data available.


Simulations of 400 neurons that encode a one-dimensional value x, which ramps from -1 to 3. A. Weighted sum of spikes (weights chosen to optimally approximate x), filtered by a synapse model. This approximates synaptic drive from these neurons to another neuron (not simulated). B. Efficient surrogate model of the quantity in A.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4126432&req=5

Figure 1: Simulations of 400 neurons that encode a one-dimensional value x, which ramps from -1 to 3. A. Weighted sum of spikes (weights chosen to optimally approximate x), filtered by a synapse model. This approximates synaptic drive from these neurons to another neuron (not simulated). B. Efficient surrogate model of the quantity in A.


Fast approximate models of large networks
Simulations of 400 neurons that encode a one-dimensional value x, which ramps from -1 to 3. A. Weighted sum of spikes (weights chosen to optimally approximate x), filtered by a synapse model. This approximates synaptic drive from these neurons to another neuron (not simulated). B. Efficient surrogate model of the quantity in A.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4126432&req=5

Figure 1: Simulations of 400 neurons that encode a one-dimensional value x, which ramps from -1 to 3. A. Weighted sum of spikes (weights chosen to optimally approximate x), filtered by a synapse model. This approximates synaptic drive from these neurons to another neuron (not simulated). B. Efficient surrogate model of the quantity in A.

View Article: PubMed Central - HTML

AUTOMATICALLY GENERATED EXCERPT
Please rate it.

This work addresses two key challenges in modeling large neural systems such as the primate visual system... The first challenge is that large detailed models require prohibitively expensive computers and long simulation times... The second is that interpretation of simulation results is difficult due to the large number of model parameters... The present work consists of a computationally efficient model of this approximation, i.e. of the functional differences between NEF neural-circuit models and corresponding lower-dimensional dynamics... The procedure is to model a group of point neurons and their connections within a network, to approximate their aggregate behaviour as a surrogate “population model”, and then to simulate only the population model... These surrogate models have two benefits... The first is the practical benefit of a 1-2 orders-of-magnitude reduction in computation... Efficient parallel implementations are currently in progress, but functionally realistic real-time simulation of 10-10 point neurons is anticipated on a single graphical processing unit (the number depending substantially on network connection statistics)... The second benefit is that this model provides a simple and thorough description of the behaviour of complex networks and how it arises from neuron parameters... The main goal of this work has been to accelerate and enhance insight into NEF models, but the method applies to any network of point neurons with linear synaptic integration... If there are about half as many large singular values in a synaptic weight matrix as there are post-synaptic neurons, the surrogate model will be about as efficient as the full neuron model... However, if there are few large singular values then the population model is much more efficient.

No MeSH data available.