2015/10/26 by Rodrigo Cofré, Rodrigo Cofre, Cofre, Rodrigo +2
Biochemistry, Genetics and Molecular Biology · Computer Science · Engineering · Mathematics · Neuroscience · Physics and Astronomy · #Advanced Memory and Neural Computing #Biological Physics (physics.bio-ph) #FOS: Biological sciences #FOS: Physical sciences #Mathematical Physics (math-ph) #Molecular Networks (q-bio.MN) #Neural Networks and Applications #Neural dynamics and brain function #Physics and Society (physics.soc-ph) #math-ph #math.MP #physics.bio-ph #physics.soc-ph #q-bio.MN
paper · pdf · doi:10.48550/arxiv.1512.01419
24 pages, 4 figures
arxiv created 2015/10/26 · openalex publication_date 2015/10/26 · arxiv updated 2015/12/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Spiking activity from populations of neurons display causal interactions and memory effects. Therefore, they are expected to show some degree of irreversibility in time. Motivated by the spike train statistics, in this paper we build a framework to quantify the degree of irreversibility of any maximum entropy distribution. Our approach is based on the transfer matrix technique, which enables us to find an homogeneous irreducible Markov chain that shares the same maximum entropy measure. We provide relevant examples in the context of spike train statistics