Sussex Neuroscience

Professor Jeremy Niven

Dr Jeremy NivenSensory information processing, neural circuits and brain evolution in arthropods.

We are interested in understanding how neural circuits function and the selective pressures they are subject to. Our laboratory combines electrophysiological methods, including intracellular recording and anatomical reconstruction of identified neurons, with transmission electron microscopy, fluorescence microscopy, behaviour, phylogenetic analysis and computational modelling to approach these questions in insect nervous systems. The following proposals indicate our current research directions; specific PhD projects are available upon request.

Applicants should have a strong academic background at undergraduate in biological or physical sciences, or a related subject (e.g. engineering, mathematics) and a willingness to learn computer programming skills. A keen interest in neuroscience, biophysics and behaviour is essential.

(For full list of publications and more details about the lab, please visit: http://www.sussex.ac.uk/lifesci/nivenlab/)

Proposal 1 – The Function and Evolution of Neural Circuits

Little is known about how neural circuits evolve and how this relates to the behaviours they generate. Potentially, almost any aspect of a neural circuit can change influencing behaviour. For example, the number of neurons within a neural circuit, their morphology and physiology and the numbers of connections between neurons could all change. Circuits of identified neurons in insects provide a unique opportunity to assess these changes and relate them to behaviour. Several aspects of this problem are under investigation in our laboratory:

1) the tuning of membrane properties in sensory neurons to match environmental stimuli

2) the evolution of synaptic connections between identified neurons

3) the effects of changes in neural circuits on the behaviours they generate

4) the evolution of motor control and decision making

Sengupta B, Faisal AA, Laughlin SB, Niven JE (2013). The effect of cell size and channel density on neuronal information encoding and energy efficiency. J Cereb Blood Flow Metab. doi: 10.1038/jcbfm.2013.103.

Niven JE, Farris SM. (2012). Miniaturization of nervous systems and neurons. Curr Biol. 22:R323-9. doi: 10.1016/j.cub.2012.04.002.

Perge JA, Niven JE, Mugnaini E, Balasubramanian V, Sterling P. (2012). Why do axons differ in caliber? J Neurosci. 32:626-38. doi: 10.1523/JNEUROSCI.4254-11.2012.

Sengupta B, Stemmler M, Laughlin SB, Niven JE. (2010). Action potential energy efficiency varies among neuron types in vertebrates and invertebrates. PLoS Comput Biol. 6:e1000840. doi: 10.1371/journal.pcbi.1000840.

Proposal 2 – The Energy Consumption of Information Processing in the Nervous System

The electrical signals that neurons use to process and transmit information consume energy, with profound consequences for the evolution of neural circuits in relation to behaviour. Although detailed biophysical models exist for the energy consumption of graded neurons, such as photoreceptors, there is still little information about the energy consumption of spiking neurons, which are a major component of neural circuits in both vertebrates and invertebrates. The properties of voltage-gated ion channels in neurons are essential for understanding action potential energy consumption. How much energy do action potentials consume? What are the constraints on voltage-gated ion channels and action potential energy consumption? Are there trade-offs between action potential energy consumption and information processing?

Sengupta B, Faisal AA, Laughlin SB, Niven JE (2013). The effect of cell size and channel density on neuronal information encoding and energy efficiency. J Cereb Blood Flow Metab. doi: 10.1038/jcbfm.2013.103.

Sengupta B, Stemmler M, Laughlin SB, Niven JE. (2010). Action potential energy efficiency varies among neuron types in vertebrates and invertebrates. PLoS Comput Biol. 6:e1000840. doi: 10.1371/journal.pcbi.1000840.

Niven JE, Laughlin SB. (2008). Energy limitation as a selective pressure on the evolution of sensory systems. J Exp Biol. 2008 211:1792-804. doi: 10.1242/jeb.017574

Niven JE, Anderson JC, Laughlin SB. (2007). Fly photoreceptors demonstrate energy-information trade-offs in neural coding. PLoS Biol. 2007 5:e116.

Proposal 3 – The Neural Basis of Multisensory Fusion for Decision Making

When moving through natural or artificial environments towards a goal, humans and animals fuse information from several sensory modalities to improve the reliability of their actions and decisions. How is information from different modalities that reach the observer with different latencies combined? What neural circuits integrate these multiple sensory modalities? How are these modalities combined to account for changing reliability with environmental conditions? We have recently developed several behavioural paradigms in which behavioural read-outs of multisensory fusion can be combined with circuit-level analysis to investigate how visual and mechanosensory information is fused. The project will assess how the sensory modalities are fused to influence motor strategy selection and which modality is dominant in a particular situation. Using intracellular recording techniques in vivo, we will identify neurons involved in multisensory fusion, allowing us to investigate the computations involved. This will improve our understanding of this process in humans and animals, and aid the implementation of this mechanism in autonomous systems.

Niven JE, Ott SR, Rogers SM. (2012). Visually targeted reaching in horse-head grasshoppers. Proc Roy Soc B. 279:3697-705. doi: 10.1098/rspb.2012.0918.

Niven JE, Buckingham CJ, Lumley S, Cuttle MF, Laughlin SB. (2010). Visual targeting of forelimbs in ladder-walking locusts. Curr Biol. 20:86-91. doi: 10.1016/j.cub.2009.10.079.

Chittka L, Niven J. (2009). Are bigger brains better? Curr Biol. 19:R995-R1008. doi: 10.1016/j.cub.2009.08.023.