The prospect of personalized computational modeling in neurological disorders, and in particular in epilepsy, is poised to revolutionize the field.
Work in the last two decades has demonstrated that neural mass models (NMM) can realistically reproduce and explain epileptic seizure transitions as recorded by electrophysiological methods (EEG, SEEG). In previous work, advances were achieved by i) increasing excitation in NMM and ii) heuristically varying network inhibitory coupling parameters or, equivalently, inhibitory synaptic gains. Based on those studies, we provide here a laminar neural mass model capable of realistically reproducing the electrical activity recorded by SEEG in the epileptogenic zone during interictal to ictal states. With the exception of the external noise input onto the pyramidal cell population, the model dynamics are autonomous --- all model parameters are static. By setting the system at a point close to bifurcation, seizure-like transitions are generated, including pre-ictal spikes, low voltage fast activity, and ictal rhythmic activity. A novel element in the model is a physiologically plausible algorithm for chloride accumulation dynamics: the gain of GABAergic post-synaptic potentials is modulated by the pathological accumulation of Cl$^-$ in pyramidal cells, due to high inhibitory input and/or dysfunctional chloride transport. In addition, in order to simulate SEEG signals to compare with real recordings performed in epileptic patients, the NMM is embedded first in a layered model of the neocortex and then in a realistic physical model. We compare modeling results with data from four epilepsy patient cases. By including key pathophysiological mechanisms, the proposed framework captures succinctly the electrophysiological phenomenology observed in ictal states, paving the way for robust personalization methods using brain network models based on NMMs.