An FPGA Implementation of a Polychronous Spiking Neural Network with Delay Adaptation

Wang, Runchun Mark; Cohen, Gregory; Stiefel, Klaus M.; Hamilton, Tara Julia; Tapson, Jonathan; Schaik, André van

doi:10.3389/fnins.2013.00014

Cited by 66 publications

(53 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The experimental results showed that the CETC model requires reduced hardware resources and accurately reproduces the thalamocortical dynamical characteristics in both the normal and Parkinsonian states. Numerous studies have proposed FPGA-based implementation of the realistic neural networks with different hardware structures and methods for the real-time emulations of the large-scale networks46474849. The real-time emulations of the large-scale neural networks are of vital significance to understand how the brain transfers, decodes and processes information5051.…”

Section: Discussionmentioning

confidence: 99%

Efficient implementation of a real-time estimation system for thalamocortical hidden Parkinsonian properties

Yang

Deng

Wang

et al. 2017

Sci Rep

View full text Add to dashboard Cite

Real-time estimation of dynamical characteristics of thalamocortical cells, such as dynamics of ion channels and membrane potentials, is useful and essential in the study of the thalamus in Parkinsonian state. However, measuring the dynamical properties of ion channels is extremely challenging experimentally and even impossible in clinical applications. This paper presents and evaluates a real-time estimation system for thalamocortical hidden properties. For the sake of efficiency, we use a field programmable gate array for strictly hardware-based computation and algorithm optimization. In the proposed system, the FPGA-based unscented Kalman filter is implemented into a conductance-based TC neuron model. Since the complexity of TC neuron model restrains its hardware implementation in parallel structure, a cost efficient model is proposed to reduce the resource cost while retaining the relevant ionic dynamics. Experimental results demonstrate the real-time capability to estimate thalamocortical hidden properties with high precision under both normal and Parkinsonian states. While it is applied to estimate the hidden properties of the thalamus and explore the mechanism of the Parkinsonian state, the proposed method can be useful in the dynamic clamp technique of the electrophysiological experiments, the neural control engineering and brain-machine interface studies.

show abstract

Section: Discussionmentioning

confidence: 99%

Efficient implementation of a real-time estimation system for thalamocortical hidden Parkinsonian properties

Yang

Deng

Wang

et al. 2017

Sci Rep

View full text Add to dashboard Cite

show abstract

“…. , t 0 ) does depend on t 0 (the second term in (28)) and this dependence cannot be eliminated. See also Appendix, below, where the above general reasoning is illustrated for the LIF neuronal model with threshold 2 (that is two input impulses applied in a short succession are able to trigger, see (31), below).…”

Section: Thismentioning

confidence: 99%

Fast Cl-type inhibitory neuron with delayed feedback has non-Markov output statistics

Vidybida¹

2018

J. Phys. Stud.

View full text Add to dashboard Cite

show abstract

“…where [ ] is a random binary variable, is the probability that [ ] = 1, % is the modulo operation and ( [ ]/( + 1)) is the integer part of [ ]/( + 1). The expected value of X is given by (8), and simply represents the fractional part of [ ]/( + 1). The expected value for the decay is thus the integer plus fractional part of [ ]/( + 1) and thus equal to the IIR decay in (3), but we now only need to store a few bits for V[t].…”

Section: B Stochastic Decaymentioning

confidence: 99%

A stochastic approach to STDP

Wang

Thakur

Hamilton

et al. 2016

2016 IEEE International Symposium on Circuits and Systems (ISCAS)

Self Cite

View full text Add to dashboard Cite

Abstract-We present a digital implementation of the Spike Timing Dependent Plasticity (STDP) learning rule. The proposed digital implementation consists of an exponential decay generator array and a STDP adaptor array. On the arrival of a pre-and post-synaptic spike, the STDP adaptor will send a digital spike to the decay generator. The decay generator will then generate an exponential decay, which will be used by the STDP adaptor to perform the weight adaption. The exponential decay, which is computational expensive, is efficiently implemented by using a novel stochastic approach, which we analyse and characterise here. We use a time multiplexing approach to achieve 8192 (8k) virtual STDP adaptors and decay generators with only one physical implementation of each. We have validated our stochastic STDP approach with measurement results of a balanced excitation/inhibition experiment. Our stochastic approach is ideal for implementing the STDP learning rule in large-scale spiking neural networks running in real time. I.BACKGROUNDThe Spike Timing Dependent Plasticity (STDP) algorithm [1], which has been observed in the mammalian brain, modulates the weight of a synapse based on the relative timing of pre-synaptic and post-synaptic spikes. In STDP, the synaptic weight will be increased (or decreased) if a presynaptic spike arrives several milliseconds before (or after) the post-synaptic spike fires. This learning rule is computationally intensive as it exponential functions and divisions.In neuromorphic systems, various implementations of the STDP algorithm have been proposed, such as a circuit based on analogue blocks and flip-flops [2], a bistable synapse with a very compact analogue implementation of the STDP [3], and analogue blocks and switches to implement exponential STDP [4]. We have previously presented a compact implementation of the STDP using linear decays [5], [6]. Here, we present a novel stochastic approach that works with our previous system and can efficiently implement the STDP operations. II. EXPONENTIAL DECAY A. Infinite Impulse Response (IIR) filter approachA discrete time first order exponential decay implemented with an IIR filter can be expressed by the following equation:where, t represents the index of the time step, and [ ] represent the previous value of V and the IIR filter constant is defined as:where, τ is the time constant (in clock cycles) and the decay d is given by:When τ is large, is only a little less than 1, and a large number of bits are needed to encode its value accurately in a digital system. If the number of bits used to encode V is less than, the number of bits used to encode , the above recursive multiplication just results in a linear decay.This situation occurs, for example, when simulating a neural network with many millions of neurons using time multiplexing [7]-[10]. With a standard IIR filter approach, a large number of bits would be needed for each state variable to achieve enough resolution to calculate long time constants. Large memory storage per state variable w...

show abstract

An FPGA Implementation of a Polychronous Spiking Neural Network with Delay Adaptation

Cited by 66 publications

References 24 publications

Efficient implementation of a real-time estimation system for thalamocortical hidden Parkinsonian properties

Efficient implementation of a real-time estimation system for thalamocortical hidden Parkinsonian properties

Fast Cl-type inhibitory neuron with delayed feedback has non-Markov output statistics

A stochastic approach to STDP

Contact Info

Product

Resources

About