Research topics

What's driving >90% of animal behavior?
Or, why do we need a brain that consumes much of our energy budget? 

Neuroscience has been trying to describe behavior as a mind-centered activity. But minds are not disembodied agents. From a survival perspective, physiological needs are one of the most fundamental behavioral drives, suggesting that internal physiological signals play a huge role in shaping neural dynamics.

An intricate interplay exists between the brain and the body, acting over widely different timescales. From fast interoceptive spikes down to ultradian, circadian, and infradian hormonal cycles, various types of internal signals have been shown to affect behavior deeply. This suggests that the nervous system evolved together with the endocrine and immune systems to orchestrate physiological balance. 

One of the main questions that drive my research deals with the formalization of behavior and physiological balance within information theoretical frameworks, the effect of internal signals on behavior and brain processing, and the transfer of information across the brain and the body. More information here:  

How are neural interactions within and across brain areas shaped?
And how do we measure/study them?

Local circuit interactions shape neural dynamics and computations. These are hard to study: direct synaptic connections can be detected only in a few cases from spiking data. Statistics can help characterize effective interactions representing the summed action of multiple synapses, neuromodulators, and long-range neural oscillations. These interactions can be measured from data by analyzing the second-order moments of the population activity. The traditional way to measure them is by partitioning neural co-variability into an explainable component, driven by external stimuli, and an unexplained component, or “noise” correlations, representing the amount of co-variability that cannot be explained by stimulus selectivity.

From an information theory perspective, these interactions can profoundly affect the encoding and transfer of information within and across brain areas. Moreover, they can change across experience, shedding light on internal novelty mechanisms and local-processing of stimulus-dependent inputs.

Relevant publications: https://doi.org/10.1523/JNEUROSCI.0194-23.2023 and https://doi.org/10.1016/j.celrep.2023.113015 

Theory of spiking neural networks
How do we make them more biologically realistic?

Biological neural networks carry out nonlinear computations crucial for survival. Moreover, biological cells can send signals over different distances and different timescales. From short to long, neurons within a local network can communicate via gap junctions, synaptic neurotransmitters, and intrinsic neuromodulation. More globally, network dynamics can be shaped by extrinsic neuromodulation (i.e., coming from the outside) and hormones. 

Current artificial neural networks can implement almost any nonlinear computation using only one type of synapse (usually linear integration of inputs passed through a non-linear threshold function); nonetheless, this requires extensive training, and the link between network connectivity and computation may be unclear.

How do we make this link clearer? One option is to ask directly, "What type of neural computation requires using a particular type of synapse, neuromodulator, or other forms of communication"?

Relevant publication: https://doi.org/10.24072/pcjournal.69 

Take a look at the Publications page for previous projects.