Abstract
Human brain is functionally and physically complex. This 'complexity' can be seen as a result of biological design process involving extensive use of concepts such as modularity and hierarchy. Over the past decade, deeper insights into the functioning of cortical neurons have led to the development of models that can be implemented in hardware. The implementation of biologically inspired spiking neuron networks in silicon can provide solutions to difficult cognitive tasks. The work reported in this paper is an application of a VLSI cortical neuron model for low power design. The VLSI implementation shown in this paper is based on the spike and burst firing pattern of cortex and follows the Izhikevich Ψ neuron model. This model is applied to a DC differential amplifier as practical application of power reduction.
| Original language | English |
|---|---|
| Title of host publication | ACE 2010 - 2010 International Conference on Advances in Computer Engineering |
| Pages | 86-89 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 2010 |
| Externally published | Yes |
| Event | 2010 International Conference on Advances in Computer Engineering, ACE 2010 - Bangalore, India Duration: Jun 21 2010 → Jun 22 2010 |
Other
| Other | 2010 International Conference on Advances in Computer Engineering, ACE 2010 |
|---|---|
| Country | India |
| City | Bangalore |
| Period | 6/21/10 → 6/22/10 |
Fingerprint
Keywords
- Cortical neuron
- Differential amplifier
- Low power
ASJC Scopus subject areas
- Computer Science (miscellaneous)
Cite this
A neuron based switch : Application to low power mixed signal circuits. / James, Alex Pappachen; Shariff, Fayaz; Maan, Akshay Kumar.
ACE 2010 - 2010 International Conference on Advances in Computer Engineering. 2010. p. 86-89 5532870.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A neuron based switch
T2 - Application to low power mixed signal circuits
AU - James, Alex Pappachen
AU - Shariff, Fayaz
AU - Maan, Akshay Kumar
PY - 2010
Y1 - 2010
N2 - Human brain is functionally and physically complex. This 'complexity' can be seen as a result of biological design process involving extensive use of concepts such as modularity and hierarchy. Over the past decade, deeper insights into the functioning of cortical neurons have led to the development of models that can be implemented in hardware. The implementation of biologically inspired spiking neuron networks in silicon can provide solutions to difficult cognitive tasks. The work reported in this paper is an application of a VLSI cortical neuron model for low power design. The VLSI implementation shown in this paper is based on the spike and burst firing pattern of cortex and follows the Izhikevich Ψ neuron model. This model is applied to a DC differential amplifier as practical application of power reduction.
AB - Human brain is functionally and physically complex. This 'complexity' can be seen as a result of biological design process involving extensive use of concepts such as modularity and hierarchy. Over the past decade, deeper insights into the functioning of cortical neurons have led to the development of models that can be implemented in hardware. The implementation of biologically inspired spiking neuron networks in silicon can provide solutions to difficult cognitive tasks. The work reported in this paper is an application of a VLSI cortical neuron model for low power design. The VLSI implementation shown in this paper is based on the spike and burst firing pattern of cortex and follows the Izhikevich Ψ neuron model. This model is applied to a DC differential amplifier as practical application of power reduction.
KW - Cortical neuron
KW - Differential amplifier
KW - Low power
UR - http://www.scopus.com/inward/record.url?scp=77956160592&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77956160592&partnerID=8YFLogxK
U2 - 10.1109/ACE.2010.98
DO - 10.1109/ACE.2010.98
M3 - Conference contribution
SN - 9780769540580
SP - 86
EP - 89
BT - ACE 2010 - 2010 International Conference on Advances in Computer Engineering
ER -