Organization of thalamic networks and mechanisms of dysfunction in schizophrenia and autism
Thalamic networks, at the core of thalamocortical and thalamosubcortical communications, underlie processes of perception, attention, memory, emotions, and the sleep-wake cycle, and are disrupted in mental disorders, including schizophrenia and autism. However, the underlying mechanisms of pathology are unknown. I will present novel evidence on key organizational principles, structural, and molecular features of thalamocortical networks, as well as critical thalamic pathway interactions that are likely affected in disorders. This data can facilitate modeling typical and abnormal brain function and can provide the foundation to understand heterogeneous disruption of these networks in sleep disorders, attention deficits, and cognitive and affective impairments in schizophrenia and autism, with important implications for the design of targeted therapeutic interventions
Rejuvenating the Alzheimer’s brain: Challenges & Opportunities
Mechanisms of human cortical development and neuropsychiatric disease
Zero to Birth: How the Human Brain is Built
By the time a baby is born, its brain is equipped with tens of billions of intricately crafted neurons wired together to form a compact and breathtakingly efficient supercomputer. The book is meant to give a broad audience (i.e. non-neuroscientists) a sense of the step-by-step construction of a human brain as well as our current conceptual understanding of various processes involved. The book also hopes to highlight relevance of brain development to our growing understanding of cognitive and psychological variations and syndromes. The author will talk about the book including the many challenges and rewards involved in writing it.
Artificial neural networks do not adequately mimic whatever is going on in the real brain
One may think that Deep Learning technology works in ways that are similar to the human brain. This is not really true. Our best AI technology still does not mimic the brain sufficiently well to be a match in intelligence. I will describe seven differences on how our minds work in ways diametrically opposite to those of Deep Learning technology.
Do deep learning latent spaces resemble human brain representations?
In recent years, artificial neural networks have demonstrated human-like or super-human performance in many tasks including image or speech recognition, natural language processing (NLP), playing Go, chess, poker and video-games. One remarkable feature of the resulting models is that they can develop very intuitive latent representations of their inputs. In these latent spaces, simple linear operations tend to give meaningful results, as in the well-known analogy QUEEN-WOMAN+MAN=KING. We postulate that human brain representations share essential properties with these deep learning latent spaces. To verify this, we test whether artificial latent spaces can serve as a good model for decoding brain activity. We report improvements over state-of-the-art performance for reconstructing seen and imagined face images from fMRI brain activation patterns, using the latent space of a GAN (Generative Adversarial Network) model coupled with a Variational AutoEncoder (VAE). With another GAN model (BigBiGAN), we can decode and reconstruct natural scenes of any category from the corresponding brain activity. Our results suggest that deep learning can produce high-level representations approaching those found in the human brain. Finally, I will discuss whether these deep learning latent spaces could be relevant to the study of consciousness.
A computational explanation for domain specificity in the human brain
Many regions of the human brain conduct highly specific functions, such as recognizing faces, understanding language, and thinking about other people’s thoughts. Why might this domain specific organization be a good design strategy for brains, and what is the origin of domain specificity in the first place? In this talk, I will present recent work testing whether the segregation of face and object perception in human brains emerges naturally from an optimization for both tasks. We trained artificial neural networks on face and object recognition, and found that networks were able to perform both tasks well by spontaneously segregating them into distinct pathways. Critically, networks neither had prior knowledge nor any inductive bias about the tasks. Furthermore, networks optimized on tasks which apparently do not develop specialization in the human brain, such as food or cars, and object categorization showed less task segregation. These results suggest that functional segregation can spontaneously emerge without a task-specific bias, and that the domain-specific organization of the cortex may reflect a computational optimization for the real-world tasks humans solve.
Species-specific mechanisms of the timing of human cortical development
The human brain, in particular the cerebral cortex, has undergone rapid expansion and increased complexity during recent evolution. One striking feature of human corticogenesis is that it is highly protracted in time, from prenatal stages of neurogenesis (taking months instead of days in the mouse), to postnatal stages of neuronal maturation and circuit formation (taking years instead of weeks in the mouse). This prolonged development is thought to contribute in an important fashion to increased cortical size, but also enhanced circuit complexity and plasticity. Here we will discuss how the species-specific temporal patterning of corticogenesis is largely intrinsic to cortical progenitors and neurons, and involves human-specific genes and cell properties that underlie human brain evolution, as well as our selective sensitivity to certain brain diseases.