Dendritic computations, dendritic spiking and dendritic plasticity in nanoelectronic neurons

Dendritic computations play a major role in the processing that occurs within each cortical neuron. In particular, for many pyramidal neurons, dendritic spiking has a major effect on neural behavior and must be modeled in order to capture nonlinear response of a neuron to its presynaptic inputs. This paper presents electronic circuits for dendritic spiking that demonstrate the global and local reset of dendritic spiking mechanisms. The dendritic computations are illustrated with circuits simulating entire cortical neurons, using carbon nanotube FET SPICE models.

[1]  H.-S. Philip Wong,et al.  Design Methods for Misaligned and Mispositioned Carbon-Nanotube Immune Circuits , 2008, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems.

[2]  A. Polsky,et al.  Submillisecond Precision of the Input-Output Transformation Function Mediated by Fast Sodium Dendritic Spikes in Basal Dendrites of CA1 Pyramidal Neurons , 2003, The Journal of Neuroscience.

[3]  Alice C. Parker,et al.  A carbon nanotube cortical neuron with excitatory and inhibitory dendritic computations , 2009, 2009 IEEE/NIH Life Science Systems and Applications Workshop.

[4]  M. Larkum,et al.  Signaling of Layer 1 and Whisker-Evoked Ca2+ and Na+ Action Potentials in Distal and Terminal Dendrites of Rat Neocortical Pyramidal Neurons In Vitro and In Vivo , 2002, The Journal of Neuroscience.

[5]  K. Natori,et al.  Characteristics of a carbon nanotube field-effect transistor analyzed as a ballistic nanowire field-effect transistor , 2005 .

[6]  H. Wong,et al.  A Circuit-Compatible SPICE model for Enhancement Mode Carbon Nanotube Field Effect Transistors , 2006, 2006 International Conference on Simulation of Semiconductor Processes and Devices.

[7]  N. Spruston,et al.  Dendritic spikes induce single-burst long-term potentiation , 2007, Proceedings of the National Academy of Sciences.

[8]  Judit K. Makara,et al.  Compartmentalized dendritic plasticity and input feature storage in neurons , 2008, Nature.

[9]  Bartlett W. Mel,et al.  Encoding and Decoding Bursts by NMDA Spikes in Basal Dendrites of Layer 5 Pyramidal Neurons , 2009, The Journal of Neuroscience.

[10]  Alice C. Parker,et al.  Towards a Nanoscale Artificial Cortex , 2006, CDES.

[11]  Bartlett W. Mel,et al.  Computational subunits in thin dendrites of pyramidal cells , 2004, Nature Neuroscience.

[12]  Matthew E Larkum,et al.  Synaptic clustering by dendritic signalling mechanisms , 2008, Current Opinion in Neurobiology.

[13]  N. Spruston,et al.  Synapse Distribution Suggests a Two-Stage Model of Dendritic Integration in CA1 Pyramidal Neurons , 2009, Neuron.

[14]  Jozsef Csicsvari,et al.  Activity-Dependent Control of Neuronal Output by Local and Global Dendritic Spike Attenuation , 2009, Neuron.

[15]  Rodney J. Douglas,et al.  Forward- and backpropagation in a silicon dendrite , 2001, IEEE Trans. Neural Networks.

[16]  J.V. Arthur,et al.  Recurrently connected silicon neurons with active dendrites for one-shot learning , 2004 .

[17]  Paul E. Hasler,et al.  A reconfigurable bidirectional active 2 dimensional dendrite model , 2004, 2004 IEEE International Symposium on Circuits and Systems (IEEE Cat. No.04CH37512).

[18]  Yingxue Wang,et al.  Input evoked nonlinearities in silicon dendritic circuits , 2009, 2009 IEEE International Symposium on Circuits and Systems.

[19]  H. Wong,et al.  Impact of a Process Variation on Nanowire and Nanotube Device Performance , 2007, IEEE Transactions on Electron Devices.

[20]  J. G. Elias,et al.  Silicon implementation of an artificial dendritic tree , 1992, [Proceedings 1992] IJCNN International Joint Conference on Neural Networks.

[21]  J. Meindl,et al.  Performance comparison between carbon nanotube and copper interconnects for gigascale integration (GSI) , 2005, IEEE Electron Device Letters.

[22]  Chih-Chieh Hsu,et al.  A carbon nanotube implementation of temporal and spatial dendritic computations , 2008, 2008 51st Midwest Symposium on Circuits and Systems.