Midbrain dopamine neurons are implicated in many disorders of dopamin signaling, including addiction, schizophrenia and Parkinson’s disease.In vivo, they exhibit two primary activity patterns, tonic (singlespike) firing and phasic bursting The spontaneous tonic firing ofthese neurons plays a fundamental role in dopaminergic signaling by setting the basal level of dopaminergic tone in the striatum andsetting the gain for phasic reward signaling.

The brain consists of billions of neurons, which together form the world’s most powerful information processing machine. The fundamental principles that allow these cells to organize into computing networks are unknown. This talk will describe a hypothesis for neuronal self-organization, in which competition for retroaxonal factors causes neurons to form functional networks, through processes akin to those of a free-market economy. Classically, neurons communicate by anterograde conduction of action potentials.

Experiments have revealed precisely timed patterns of spikes in several neuronal systems, raising the possibility that these temporal signals are used by the brain to encode and transmit sensory information. It is thus important to understand the capability of neural circuits to learn to produce stimulus specific temporally precise spikes. Learning to spike at given times is challenging since the spike threshold and the ensuing reset induce a strongly nonlinear dependence of the voltage on the value of the synaptic weights.

Physiological neurons and networks are highly complex in terms of their biophysical, biochemical, morphological and anatomical ingredients. How much of that detail do we have to include in a physiologically realistic model? I will first give some examples that seem to suggest that biophysical details really matter for dynamical phenomena at the network level, but will then review some evidence that, on the contrary, seems to imply that much of this detail can be safely neglected.

In addition to visual information from thalamus, neurons in primary visual cortex (V1) receive inputs from other V1 neurons, as well as from higher cortical areas. This “non-classical” input to cortical neurons, which can be inferred in part from the local field potential (LFP), can clearly modulate the “classical” feed-forward responses of cortical neurons to visual stimuli. We characterize this modulation in awake primate, using multi-electrode recordings to infer a model of neuron responses from both the stimulus and LFP.

Over the past 65 million years, the evolution of mammals led --in several lineages-- to a dramatic increase in brain size. During this process, some neocortical areas, including the primary sensory ones,expanded by many orders of magnitude. The primary visual cortex, for instance, measured about a square millimeter in late cretaceous stem eutherians but in homo sapiens comprises more than 2000 mm2.

Perceptual inference relies on very nonlinear processing of high-dimensional sensory inputs. This poses a challenge as the space of possible nonlinearities is huge and each of there functions might be implemented in many different ways. To gain better insights into the nonlinear processing of sensory signals in the brain, we are trying to make progress on two fronts: (1) By learning probabilistic representations of natural images, we explore which nonlinearities are most successful in capturing the degrees of freedom of the visual input.

Decisions and neural correlates of decision variables both indicate that humans and animals make decisions taking into account task structure. Such deliberative, "model-based" choice is thought to be important for overcoming habits and various sorts of compulsions, but there is still little evidence about the algorithmic or neural mechanisms that support it. I discuss recent studies attempting to address these questions.

Decision confidence is a forecast about the correctness of one’s decision. It is often regarded as a higher-order function of the brain requiring a capacity for metacognition that may be unique to humans. If confidence manifests itself to us as a feeling, how can then one identify it amongst the brain’s electrical signals in an animal? We tackle this issue by using mathematical models to gain traction on the problem of confidence, allowing us to identify neural correlates and mechanisms.