Electrophysiology seminars

Experience unfolds continuously, with current states constrained by prior activity and changing context. Domenico Guarino tests whether this continuity appears as reproducible neural trajectories rather than isolated events. Neuronal population events form persistent trajectories whose transitions depend on prior states and other brain areas. In human EEG, microstate sequences exhibit dependencies beyond dwell time and first-order Markov predictions, and stimuli systematically reshape those dependencies. Linking ensemble and EEG dynamics may reveal classes of individual responses and inform general AI models predicting responses to stimuli.

Why do dorsal raphe serotonin neurons respond to reward, surprise, salience and uncertainty in ways that resist a single explanation? This talk proposes that serotonin activity encodes prospective value: a prediction of cumulative future reward. The model is motivated by the unusually strong and persistent spike-frequency adaptation of these neurons, which may support efficient and accurate predictions. It reproduces important qualitative features of serotonergic responses that earlier models miss and predicts measured in vivo firing rates more accurately than competing accounts. By treating previously separate responses as manifestations of one prospective value code, the work connects serotonin physiology to its computational roles in learning and behaviour.

Subcallosal cingulate stimulation for resistant depression needs objective biomarkers beyond stress-sensitive clinical ratings. Sankaraleengam Alagapan uses variational autoencoders to address high-dimensional longitudinal local-field potentials and unreliable labels. Supervised models track depressive states; a generative causal explainer identifies interpretable, low-dimensional features, advancing actionable biomarkers for stimulation management.

Uri Cohen examines how stable memories can emerge in recurrent networks without imposing specially engineered dynamics such as a Lyapunov gradient. The analysis concerns discrete fixed points in networks with graded activity and noise. Random matrix methods characterize both the bulk and outlier eigenvalues of the local Jacobians. This yields a stability threshold: either all fixed points are stable or all are unstable, depending on their number. That threshold differs from the usual storage capacity, which counts achievable fixed points irrespective of stability. Relating the stability threshold to measurable pattern statistics and activation functions predicts advantages for sparse-like memories and threshold-linear responses. The talk connects these predictions to hippocampal CA3, a proposed auto-associative memory circuit. An assembled dataset of in-vivo patch-clamp recordings from CA3 pyramidal cells in behaving mice provides preliminary support for the predicted threshold-linear activation.

Radoslaw Martin Cichy examines three contributions of empirical vision research to AI: visual representations developing from infancy to adulthood; feedforward and recurrent processing distinguished using layer-specific fMRI with EEG; and difficult object recognition recruiting cortex-wide recurrence. These findings test the limits of vision models inspired primarily by visual cortex.

Nicole Provenza discusses deep brain stimulation for people with severe obsessive-compulsive disorder whose symptoms persist despite available treatments. Recordings from implanted devices allow researchers to examine neural activity associated with symptom changes, responses to stimulation and recovery. The talk connects these measurements with the longer-term goal of developing more precise, personalised treatments, using real-world recordings to understand how neuromodulation affects brain activity and behaviour.