Abstract
The brain represents information through the ensemble firing of neurons. These neural processes are difficult to study in vivo because they are highly non-linear, dynamical and often time-varying. Hardware systems, such as the FPGA-based platforms, are very efficient in doing such studies given their intrinsic parallelism, reconfigurability and real-time processing capability. We have successfully used the Xilinx Virtex-6 FPGA devices to prototype the generalized Laguerre–Volterra model (GLVM), which is a rigorous and well-functioning mathematical abstraction for the description of neural processes from a system input/output relationship standpoint. The hardware system first conducts GLVM parameters estimation using the neural firing data from experiments; then it is able to predict the neural firing outputs based on the field estimated model coefficients and the novel model inputs. The hardware system has been prototyped and is proved very efficient in this study compared to our previous software model running on the Intel Core i7-2620M CPU (with Turbo Boost to 3.4 GHz). It achieves up to a 2,662 times speedup in doing GLVM parameters estimation and a 699 times speedup in conducting neural firing outputs prediction. The calculation results are very precise with the NMSE being successfully controlled at the 10− 11 scale compared to the software approach. This FPGA-based architecture is also significant to the future cognitive neural prostheses design.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
D. Purves, E.M. Brannon, R. Cabeza, S.A. Huettel, K.S. LaBar, M.L. Platt, M. Woldorff, Principles of Cognitive Neuroscience (Sinauer Associates Inc., Sunderland, MA, USA, 2007)
B. Milner, Memory and the medial temporal regions of the brain, in Biology of Memory (Academic, New York, 1970), pp. 29–50
L.R. Squire, S.M. Zola, Episodic memory, semantic memory, and amnesia. Hippocampus 8, 205–211 (1998)
M.S. Humayun, E. de Juan, J.D. Weiland, G. Dagnelie, S. Katona, R. Greenberg, S. Suzuki, Pattern electrical simulation of the human retina. Vis. Res. 39, 2569–2576 (1999)
G.E. Loeb, Gochelear prosthetics. Annu. Rev. Neurosci. 13, 357–371 (1990)
G.E. Loeb, R.A. Peck, W.H. Moore, K. Hood, Biontm system for distributed neural prosthetic interfaces. Med. Eng. Phys. 23, 9–18 (2001)
K.H. Mauritz, H.P. Peckham, Restoration of grasping functions in quadriplegic patients by functional electrical stimulation (FES). Int. J. Rehabil. Res. 10(4), 57–61 (1987)
J.P. Donoghue, Connecting cortex to machines: recent advances in brain interfaces. Nat. Neurosci. 5, 1085–1088 (2002)
L.R. Hochberg, M.D. Serruya, G.M. Friehs, J.A. Mukand, M. Saleh, A.H. Caplan, A. Branner, D. Chen, R.D. Penn, J.P. Donoghue, Neuronal ensemble control of prosthetic devices by a human with tetraplegia. Nature 442(7099), 164–171 (2006)
M.A.L. Nicolelis, Brain-machine interfaces to restore motor function and probe neural circuits. Nat. Neurosci. 4, 417–422 (2003)
K.V. Shenoy, D. Meeker, S.Y. Cao, S.A. Kureshi, B. Pesaran, C.A. Buneo, A.R. Batista, P.P. Mitra, J.W. Burdick, R.A. Andersen, Neural prosthetic control signals from plan activity. Neuroreport 14, 591–596 (2003)
D.M. Taylor, S.I.H. Tillery, A.B. Schwartz, Information conveyed through brain-control: cursor versus robot. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 195–199 (2003)
J.R. Wolpaw, D.J. McFarland, Control of a two-dimensionalmovement signal by a noninvasive brain-computer interfacein humans. Proc. Natl. Acad. Sci. USA 101, 17849–17854 (2004)
T.W. Berger, G. Chauvet, R.J. Sclabassi, A biological based model of functional properties of the hippocampus. J. Physiol. 7, 1031–1064 (1982)
J. Magee, D. Hoffman, C. Colbert, D. Johnston, Electrical and calcium signaling in dendrites of hippocampal pyramidal neurons. Annu. Rev. Physiol. 60, 327–346 (1998)
S.S. Dalal, V.Z. Marmarelis, T.W. Berger, A nonlinear positive feedback model of glutamatergic synaptic transmission in dentate gyrus, in Proceedings of the The 4th Joint Symposium on Neural Computation, vol. 7 (Institute for Neural Computation, San Diego, CA, USA, 1997), pp. 68–75
D. Song, Z. Wang, V.Z. Marmarelis, T.W. Berger, Non-parametric interpretation and validation of parametric models of short-term plasticity, in Proceedings of Annual International Conference of the IEEE EMBS (Institute of Electrical and Electronics Engineers, New York, NY, USA, 2003), pp. 1901–1904
Z. Wang, X. Xie, D. Song, T.W. Berger, Probabilistic transformation of temporal information at individual synapses, in Proceedings of Annual International Conference of the IEEE EMBS (Institute of Electrical and Electronics Engineers, New York, NY, USA, 2003), pp. 1909–1912
G. Gholmieh, S.H. Courellis, D. Song, Z. Wang, V.Z. Marmarelis, T.W. Berger, Characterization of short-term plasticity of the dentate gyrus-ca3 system using nonlinear systems analysis, in Proceedings of Annual International Conference of the IEEE EMBS (Institute of Electrical and Electronics Engineers, New York, NY, USA, 2003), pp. 1929–1932
A. Dimoka, S.H. Courellis, D. Song, V. Marmarelis, T.W. Berger, Identification of lateral and medial perforant path using single- and dual-input random impulse train stimulation, in Proceedings of Annual International Conference of the IEEE EMBS (Institute of Electrical and Electronics Engineers, New York, NY, USA, 2003), pp. 1933–1936
M.C. Citron, J.P. Kroeker, G.D. McCann, Nonlinear interactions in ganglion cell receptive fields. J. Neurophysiol. 46, 1161–1176 (1981)
M.C. Citron, R.C. Emerson, W.R. Levick, Nonlinear measurement and classification of receptive fields in cat retinal ganglion cells. Ann. Biomed. Eng. 16, 65–77 (1988)
P.Z. Marmarelis, K.I. Naka, Nonlinear analysis and synthesis of receptive field responses in the catfish retina II: one-input white-noise analysis. J. Neurophysiol. 36, 619–633 (1973)
D. McAlpine, Creating a sense of auditory space. J. Physiol. 566, 21–28 (2005)
L. Paninski, M.R. Fellows, N.G. Hatsopoulos, J.P. Donoghue, Spatiotemporal tuning of motor neurons for hand position and velocity. J. Neurophysiol. 91, 515–532 (2004)
A.L. Hodgkin, A.F. Huxley, A quantitative description of membrane current and its application to conduction and excitation in nerve. J. Physiol. 117, 500–544 (1952)
H. Markram, The blue brain project. Nat. Rev. Neurosci. 7, 153–160 (2006)
Elementary Objects of the Nervous System. [Online]. Available: http://synapticmodeling.com/ Accessed January 2013
Brain in Silicon. [Online]. Available: http://brainsinsilicon.stanford.edu Accessed January 2013
NEURON. [Online]. Available: http://www.neuron.yale.edu/neuron/ Accessed January 2013
Mcell: A Monte Carlo Simulator of Cellular Microphysiology. [Online]. Available: http://www.mcell.cnl.salk.edu/ Accessed January 2013
J.P. Cunningham, V. Gilja, S.I. Ryu, K.V. Shenoy, Methods for estimating neural firing rates, and their application to brain-machine interfaces. Neural Networks 22(9), 1235–1246 (2009)
D.R. Brillinger, Nerve cell spike train data analysis: a progression of technique. J. Am. Stat. Assoc. 87, 260–271 (1992)
E.N. Brown, R. Barbieri, U.T. Eden, L.M. Frank, Likelihood methods for neural data analysis, in Computational Neuroscience: A Comprehensive Approach, vol. 7 (Chapman & Hall/CRC, London, UK, 2003), pp. 253–286
W. Wu, Y. Gao, E. Bienenstock, J. Donoghue, M. Black, Bayesian population decoding of motor cortical activity using a Kalman filter. Neural Comput. 18(1), 80–118 (2006)
V. Volterra, Theory of Functionals and of Integral and Integro-Differential Equations (Dover, New York, 1959)
N. Wiener, Nonlinear Problems in Random Theory (MIT, New York, 1958)
V.Z. Marmarelis, P.Z. Marmarelis, Analysis of Physiological Systems: The White-Noise Approach (Plenum, New York, 1978)
V.Z. Marmarelis, Nonlinear Dynamic Modeling of Physiological Systems (Wiley-IEEE Press, Hoboken, 2004)
D. Song, R.H.M. Chan, V.Z. Marmarelis, R.E. Hampson, S.A. Deadwyler, T.W. Berger, Nonlinear dynamic modeling of spike train transformations for hippocampal-cortical prostheses. IEEE Trans. Biomed. Eng. 54, 1053–1066 (2007)
R.H.M. Chan, D. Song, T.W. Berger, Tracking temporal evolution of nonlinear dynamics in hippocampus using time-varying volterra kernels, in Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 54 (Institute of Electrical and Electronics Engineers, New York, NY, USA, 2008), pp. 4996–4999
D. Song, R.H.M. Chan, V.Z. Marmarelis, R.E. Hampson, S.A. Deadwyler, T.W. Berger, Nonlinear modeling of neural population dynamics for hippocampal prostheses. Neural Networks 22, 1340–1351 (2009)
S.A. Deadwyler, T. Bunn, R.E. Hampson, Hippocampal ensemble activity during spatial delayed-nonmatch-to-sample performance in rats. J. Neurosci. 16, 354–372 (1996)
B.E. Alger, R.A. Nicoll, Pharmacological evidence for two kinds of GABA receptor on rat hippocampal pyramidal cells studied in vitro. J. Physiol. 328, 125–141 (1982)
J. Keat, P. Reinagel, R.C. Reid, M. Meister, Predicting every spike: a model for the responses of visual neurons. Neuron 30, 803–817 (2001)
L. Paninski, J.W. Pillow, E.P. Simoncelli, Maximum likelihoodestimation of a stochastic integrate-and-fire neural encoding model. Neural Comput. 16, 2533–2561 (2004)
D. Song, Z. Wang, T.W. Berger, Contribution of T-type VDCC to TEA-induced long-term synaptic modification in hippocampal CA1 and dentate gyrus. Hippocampus 12, 689–697 (2002)
J.F. Storm, Action potential repolarization and a fast after-hyperpolarizationin rat hippocampal pyramidal cells. J. Physiol. 385, 733–759 (2002)
V.Z. Marmarelis, Identification of nonlinear biological systems using Laguerre expansions of kernels. Ann. Biomed. Eng. 21, 573–589 (1993)
C. Boukis, D.P. Mandic, A.G. Constantinides, L.C. Polymenakos, A novel algorithm for the adaptation of the pole of Laguerre filters. IEEE Signal Process. Lett. 13, 429–432 (2006)
M.D. Linderman, G. Santhanam, C.T. Kemere, V. Gilja, S. O’Driscoll, B.M. Yu, A. Afshar, S.I. Ryu, K.V. Shenoy, T.H. Meng, Signal processing challenges for neural prostheses. IEEE Signal Process. Mag. 25, 18–28 (2008)
C. Hansang, D. Corina, J.F. Brinkley, G.A. Ojemann, L.G. Shapiro, A new template matching method using variance estimation for spike sorting, in Proceedings of the 2nd International IEEE EMBS Conference on Neural Engineering (Institute of Electrical and Electronics Engineers, New York, NY, USA, 2005), pp. 225–228
W.F. Wong, E. Gogo, Fast hardware-based algorithms for elementary function computations using rectangular multipliers. IEEE Trans. Comp. 43(3), 278–294 (1994)
J.C. Bajard, S. Kla, J.M. Muller, BKM: a new hardware algorithm for complex elementary functions. IEEE Trans. Comp. 43(8), 955–963 (1994)
H. Bui, S. Tahar, Design and synthesis of an IEEE-754 exponential function, in IEEE Canadian Conference on Electrical and Computer Engineering, vol. 1 (Institute of Electrical and Electronics Engineers, New York, NY, USA, 1999), pp. 450–455
G. Even, P.M. Seidel, A comparison of three rounding algorithms for IEEE floating-point multiplication. IEEE Trans. Comp. 49(7), 638–650 (2000)
M.A. Figueiredo, C. Gloster, Implementation of a probabilistic neural network for multi-spectral image classification on an FPGA based custom computing machine, in Proceedings of the 5th Brazilian Symposium on Neural Networks (IEEE Computer Society Press, Washington, D.C., USA, 1998), pp. 174–179
V. Kantabutra, On hardware for computing exponential and trigonometric functions. IEEE Trans. Comp. 45(3), 328–339 (1996)
B. Blodget, P. James-Roxby, E. Keller, S. McMillan, P. Sundararajan, A self-reconfiguring platform, in Proceedings of the 13th International Conference on Field Programmable Logic and Applications (FPL’03) (Springer, New York, NY, USA, 2003), pp. 565–574
W.X.Y. Li, R.H.M. Chan, W. Zhang, R.C.C. Cheung, D. Song, T.W. Berger, High-performance and scalable system architecture for the real-time estimation of generalized Laguerre–Volterra MIMO model from neural population spiking activity. IEEE J. Emerg. Sel. Top. Circ. Syst. 1(4), 489–501 (2011)
T.E. Tkacik, A hardware random number generator, in Proceedings of the 4th International Workshop on Cryptographic Hardware and Embedded Systems (Springer, New York, NY, USA, 2002), pp. 450–453
M.C. Jeruchim, P. Balaban, K.S. Shanmugan, Simulation of Communication Systems: Modeling, Methodology, and Techniques (Springer, Berlin, 2000). ISBN 978-0-306-46267-2
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Li, W.X.Y. et al. (2013). High-Performance Computing for Neuroinformatics Using FPGA. In: Vanderbauwhede, W., Benkrid, K. (eds) High-Performance Computing Using FPGAs. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1791-0_6
Download citation
DOI: https://doi.org/10.1007/978-1-4614-1791-0_6
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-1790-3
Online ISBN: 978-1-4614-1791-0
eBook Packages: EngineeringEngineering (R0)