Computational Neuroscience 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.

Mammalian brains increasingly reuse the same neurons and regions for different tasks, while maintaining robust performance when the environment or stimulus context changes. Udo Ernst combines theoretical modelling with quantitative physiological observations to investigate the mechanisms that support both flexibility and resilience. Selective processing in the primate visual system provides the central example. One analytically tractable framework routes attended external signals through avalanches of spikes in recurrent neuronal networks; a second examines control circuits that preserve reliable routing across stimulus contexts. Mathematical analysis clarifies both mechanisms and how their operating ranges depend on physiological parameters.

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.

The brain adjusts its internal activity to direct visual and auditory attention and regulate responses to external stimuli. Appropriate endogenous activity may support normal cognition and behaviour, while its modulation can also produce dysfunction. Beginning with mathematical models of general anaesthesia, this talk examines how changes in noisy endogenous activity can fragment functional brain organization and account for loss of consciousness. Anaesthesia experiments in ferrets support the theoretical result. Further experimental examples are discussed to show how underlying internal noise may help explain observed transitions between brain states.