Upinder S. Bhalla, Ph.D.
Professor & Dean, NCBS, Bengaluru, India
Watching the network change during the formation of associative memory
The process of learning is measured through behavioural changes, but it is of enormous interest to understand its cellular and network basis. We used 2-photon imaging of hippocampal CA1 pyramidal neuron activity in mice to monitor such changes during the acquisition of a trace conditioning task. One of the questions in such learning is how the network retains a trace of a brief conditioned stimulus (a sound), until the arrival of a delayed unconditioned stimulus (a puff of air to the eye). During learning, the mice learn to blink when the tone is presented, well before the arrival of the air puff.
The mice learnt this task in 20-50 trials. We observed that in this time-frame the cells in the network changed the time of their peak activity, such that their firing times tiled the interval between sound and air puff. Thus the cells seem to form a relay of activity. We also observed an evolution in functional connectivity in the network, as measured by groupings of correlated cells. These groupings were stable till the learning protocol commenced, and then changed. Thus we have been able to observe two aspects of network learning: changes in activity (relay firing), and changes in connectivity (correlation groups).
Ashok Pandey, Ph.D.
Scientist F & Head, Biotechnology Division, National Institute for Interdisciplinary Science and Technology-CSIR), Thiruvananthapuram, India
Alternative renewable resources: Issues and perspectives for India – the case of transport fuels
With the increase in the urbanization way of life and also more and more dependence on materialistic life, there is substantial growing demand for the energy. The science and technological policy of the India has looked several avenues to fulfill this demand through alternative resources such as solar energy, wind energy, tidal energy, bioenergy, etc. The demand for the transport sector is largely met through the import (~70%). Biofuels, in particular bioethanol from lignocellulosic biomass offer attractive possibilities in this regard.
The sugar platform which generates ethanol is considered to be the most valuable solution to the transport fuel demand. Bioethanol can be generated from grains as well as from lignocellulosic plant material by their saccharification to sugars and subsequent fermentation of the sugars to produce ethanol. Bio-ethanol as a transportation fuel is attractive since it is more energy efficient than gasoline and produces less emissions. The benefits of developing biomass to ethanol technology(s) include: increased national energy security, reduction in GHG emissions, use of renewable resources, economic benefits and creation of employment and the foundation of a carbohydrate based chemical industry. However, the utilization of lignocellulosic biomass for fuel generation has not been given the sort of attention it ought to receive. It is known that the technology for ethanol production from biomass has to evolve greatly for an economical commercial scale utilization of the renewable biomass resources. Biomass requires extensive processing involving multiple steps for hydrolysis and fermentation of the raw material for producing ethanol. Feed stock availability, pretreatment, saccharification, fermentation and ethanol recovery are all factors which influence the production of ethanol and which needs R&D efforts for overall improvement of the production economics.
Bioconversion of lignocellulosic biomass (LB) can contribute significantly to the production of organic chemicals also. LB is also considered to be the only foreseeable source of energy. LB is mainly composed of (dry wt basis): cellulose, 40-60; hemicellulose, 20-40; and lignin, 10-25%. Most efficient method of biomass hydrolysis is through enzymatic saccharification5 using cellulases and hemicellulases. Fungal cellulases (FCs) have proved to be a better candidate than other microbial cellulases, with their secreted free cellulase complexes comprising all three components of cellulase [endoglucanases, exoglucanases and cellobiases (glucosidases).
The Centre for Biofuels at NIIST, Trivandrum, India aims ultimately to develop technologies and processes which will address the nation’s need for making fuel ethanol from the renewable resource: biomass. It is proposed to direct R&D activities at the major requirements of a biomass-ethanol technology, which include production of cellulases, hydrolysis of biomass, and ethanol fermentation. Viable technologies for each of these processes will contribute to the overall process development for fuel alcohol production from cheap and renewable biomass resources.
The lecture would present perspectives on bioethanol from lignocellulosic feedstocks.
References
- Biofuels- Alternative Feedstocks and Conversion Processes, Editors- Ashok Pandey, C Larroche, SC Ricke, CG Dussap & E Gnansounou, Academic Press, Elsevier Inc; San Diego, USA, p629 (2011) ISBN: 978-0-12-385099-7
- Handbook of Plant-Based Biofuels, Editor- Ashok Pandey, CRC Press, Francis & Taylors, Boca Raton, USA, p 297 (2008) ISBN 978-q-5602-2175-3
- Biofuels II, Special issue of Journal of Scientific & Industrial Research, Guest Editors- E Gnansounou, C Larroche and Ashok Pandey, 67(11), 837-1040 (2008) ISSN: 0022-4456
- Biofuels, Special issue of Journal of Scientific & Industrial Research, Guest Editors- C Larroche and Ashok Pandey, 64(11), 797-988 (2005) ISSN: 0022-4456
Karmeshu, Ph.D.
Dean & Professor, School of Computer & Systems Sciences & School of Computational & Integrative Sciences, Jawaharlal Nehru University, India.
Interspike Interval Distribution of Neuronal Model with distributed delay: Emergence of unimodal, bimodal and Power law
The study of interspike interval distribution of spiking neurons is a key issue in the field of computational neuroscience. A wide range of spiking patterns display unimodal, bimodal ISI patterns including power law behavior. A challenging problem is to understand the biophysical mechanism which can generate the empirically observed patterns. A neuronal model with distributed delay (NMDD) is proposed and is formulated as an integro-stochastic differential equation which corresponds to a non-markovian process. The widely studied IF and LIF models become special cases of this model. The NMDD brings out some interesting features when excitatory rates are close to inhibitory rates rendering the drift close to zero. It is interesting that NMDD model with gamma type memory kernel can also account for bimodal ISI pattern. The mean delay of the memory kernels plays a significant role in bringing out the transition from unimodal to bimodal ISI distribution. It is interesting to note that when a collection of neurons group together and fire together, the ISI distribution exhibits power law.