Sohail Siadat

Sohail Siadat (Siadatnejad), PhD in Neuroscience

Researcher in Neuroscience, (also C++/Python/Java/JavaScript Developer, Neural Engineering)

 •   Linked-in         • google scholar page

My current focus is on                 Opteran, GIL, HLang, ApiSave

Recent Career:

•  (currently:) Senior Developer at Opteran Technologies: Building Brains for Robotics, inspired by Neuroscience of Insect Brains

• Apis Save (Volunteering work about Apis mellifera) + ApisLabs

• Senior Software Engineer at LBG

• Senior Research Officer at Essex BCI-NE Lab (University of Essex)

• (Scientific) Developer at iMakr Ltd. (3D shape implicit modelling, 3D printing). Developer and algorithm design/implementation at Wide IO Ltd. (2014 -- 2016).

• Post-doctoral Research Associate at Robert Lucas' lab in University of Manchester (May-Sept 2014). - Detecting Thalamic visual responses in retinal degenerated mice.

• PhD graduate from the University of Manchester. My PhD Thesis title was "Quantifying sensory information in continuous brain signals" (December 2013) (Faculty of Life Sciences, FLS.)

Current research interests:

Neural codes; Decoding: Multi-array spike train analysis using Bayesian and State Space models for BCI.

Neural codes; Encoding: During my PhD my focus was the study of neural codes: How the external world is represented inside the brain. My topics of research during my PhD included: Information Theory, phase-of-firing code in cortex, spike train analysis, the inference of non-parametric and parametric probabilistic models based on neural data with small sample size, and estimation methods of entropy-like quantities. Phase-related encoding.

Information Theory: Estimation, Synergy, GIL. (a unified framework of concepts I developed since 2005 based on Information Theory based on insights from Neuroscience (NEural Information Flow), Quantum Information, etc) (Neural Information Theory) (GIL™)

Estimation methods for entropy-like quantities for small sample sizes (bias-correction and model selection) -- Entropy Estimator Engineering

Statistical modelling: MCMC, Information Geometry, Bayesian models in the brain. EM.

Learning: Boltzmann machines, Energy-based learning, Bayesian models in the brain, associated learning models, and models of Dopamine reward system.

Neural Engineering (Engineering inspired-by (or about) Neuroscience). Use of Group Theory and Representation Theory in Neuroscience. PyTorch.

BCI: neural prosthesis. (Neural decoding algorithm design for control of prosthetic hand)

Other interests in Neuroscience: Causality, network information dynamics, neural modelling, statistical modelling of neural data, decision making (value based and perceptual), simplified neural models and detailed realistic conductance-based models. Bee neuroscience. Affective neuroscience. I am currently reading: Statistical mechanics, information geometry, Bayesian epistemology, Lagrangian and Hamiltonian mechanics, and Group Theory. (See skills)

Predictive Processing, enActive cognition (4E cognition): active, active perception, extended mind (approach described by Andy Clark's Surfing Uncertainty), Free Energy Principle.

Other interests:

Quantum Computing: Asymmetric Quantum Cloning Machine (first to propose the structure of such machine), quantum information theory.

Reversible Computing (extremely low-power computing)

Heterogeneous Computing (The next generation / The future of computing)  Heterogeneous computing (HLang™).

Engineering: Robotics, 3D printing. Brain-computer interfaces (robotic prosthetic hand), low-level systems programming (Linux), distributed computing, FPGA/VHDL, Software Quality (Eχtreme Quality™)

Theoretical computer science (computability and reversible computing), Resource Oriented Computing as a model of computation, energy-efficient and cloud computing, 3D shape modelling using implicit surfaces (algebraic modelling).

Publication:

• I have 2 new manuscripts in preparation based on my PhD Thesis results.

• Brown TM*, Siadatnejad S*, Gigg J, Lucas RJ, Montemurro MA (submitted). Melanopsin enhances irradiance coding in the early phase of LGN visual responses. (*TB and SS contributed equally)

• Sohail Siadatnejad. Quantifying sensory information in continuous brain signals (January 2013) PhD Thesis. The University of Manchester.

• Siadatnejad S, Bale MR, Petersen RS, Montemurro MA. Phase-of-firing coding of dynamical whisker stimuli and the thalamocortical code in barrel cortex. (2013). BMC Neuroscience 2013 [accepted; Twenty-Second Annual Computational Neuroscience Meeting: CNS Paris, France: July 13-18, 2013].

• Siadatnejad S, Brown TM, Gigg J, Piggins HD, Lucas RJ, Montemurro MA. (2011). Quantifying the visual information sourced from melanopsin photoreceptors in mouse LGN field responses.  BMC Neuroscience 2011, 12(Suppl 1):P226 doi:10.1186/1471-2202-12-S1-P226  [Twentieth Annual Computational Neuroscience Meeting: CNS*2011 Stockholm, Sweden. 23-28 July 2011]

• Siadatnejad S, Panzeri S, Kayser C, Logothetis NK, Montemurro MA. Does the information in the phase of low-frequency LFP reflect the low-frequency envelope of local spike rates? BMC Neuroscience 2011, 12(Suppl 1):P227 doi:10.1186/1471-2202-12-S1-P227 [Same conference as above]

• Rezakhani AT, Siadatnejad S, Ghaderi AH (2004). Separability in asymmetric phase-covariant cloning. Physics Letters A, Volume 336, Issues 4-5, 14 March 2005, Pages 278-289, ISSN 0375-9601, doi:10.1016/j.physleta.2004.12.015

You can visit my google scholar page or my github page.

Published Software: ImpliSolid, Software Developed at WideIO, iMakr, LBG, ApiSave, Opteran. (Also in 1990s: Big3D, Mehr3D(audio) and FPGA reverb)

Background:

• PhD in Neuroscience (University of Manchester) - Computational Neuroscience

• MSc in Computer Science (Sharif University of Technology) - Scientific Calculations

• BSc in Software Engineering (University of Isfahan) - Software Engineering

See background

New URL: https://sites.google.com/view/sohail-siadatnejads-homepage/home-page  Old URL https://sites.google.com/site/sohale