.. title: Cognitive, Systems and Computational Neuroscience .. slug: index .. date: 2023-03-24 23:52:42 UTC-07:00 .. tags: neuroscience, fmri, neurophysiology, modeling .. category: neuroscience .. link: .. description: .. type: text
This site provides information about ongoing research in Jack Gallant's cognitive, systems and computational neuroscience lab at UC Berkeley. Here you can find our cool brain viewers, some of our published papers, information about the great people who do the work, our open data, open source code, and tutorials. If you would like to know more about the general philosophy of the lab, please listen to this Freakanomics podcast interview with Jack Gallant or to these OHBM discussions between Peter Bandettini and Jack Gallant [discussion 1] [discussion 2].
We currently have openings for potential postdocs. If you are interested please contact Jack Gallant directly (gallant @ berkeley.edu).
New preprint!
Model connectivity: leveraging the power of encoding models to overcome
the limitations of functional connectivity
(Meschke et al., in review).
Functional connectivity (FC) is the most popular method for recovering
functional networks of brain areas with fMRI. However, because FC is
defined as temporal correlations in brain activity, FC networks are
inevitably confounded by noise and their function cannot be determined
directly from FC. To overcome these limitations, we have developed model
connectivity (MC). MC is defined as similarities in encoding model weights,
which quantify reliable functional activity in terms of interpretable
stimulus- or task-related features. In this paper we compare these two
methods directly in a language comprehension dataset. We confirm the
confounds of FC, and we show that MC does not suffer from these confounds.
MC recovers more spatially localized networks and it reveals their
functional assignment. MC is powerful tool for recovering the functional
networks that support complex cognitive processes.
New paper!
Phonemic segmentation of narrative speech in human cerebral cortex
(Gong et al., Nature Communications, 2023).
Phonemes are a critical intermediate element of speech. This fMRI study
identifies the brain representation of single phonemes, and of diphones and
triphones. We find that many regions in and around the auditory cortex
represent phonemes. These regions include classical areas in the dorsal
superior temporal gyrus and a larger region in the lateral temporal cortex
(where diphone features appear to be represented). Furthermore, we identify
regions where phonemic processing and lexical retrieval are intertwined.
(Note: this is work done in collaboration with the
Theunissen lab
here at UCB.)
Our (former) senior postdoc, Dr. Fatma Deniz, has accepted
a tenured full Professor position at the Technical University
of Berlin. She began her new position as of April 1, 2023.
Congratulations Professor Deniz! We expect great things from you!
New paper!
Semantic representations during language production are affected by
context (Deniz et al., J. Neuroscience, 2023).
Context is important for understanding the meaning of natural
language, but most neuroimaging language studies use isolated words
and sentences with little context. This study investigates whether
the results of studies that use out-of-context stimuli generalize to
natural language. We find that increasing context improves the
quality of neuroimaging data, and that it changes the representation
of semantic information in the brain. These results suggest that
findings from studies using out-of-context stimuli may not generalize
to natural language used in daily life.
New paper!
Feature-space selection with banded ridge regression
(Dupre la Tour et al., Neuroimage, 2022).
Encoding models identify the information represented in brain
recordings, but fitting multiple models simultaneously presents
several challenges. This paper describes how banded ridge regression
can be used to solve these problems. Furthermore, several methods are
proposed to address the computational challenge of fitting banded
ridge regressions on large numbers of voxels and feature spaces. All
implementations are released in an open-source Python package called
Himalaya.
Christine Tseng has received their PhD! Christine has recently
been working on functional mapping of the self, others, and
social relationships. They will be taking up a postdoctoral
position in the lab while the studies are prepared for publication.
Congratulations Christine!
New paper!
Visual and linguistic semantic representations are aligned at the
border of human visual cortex (Popham et al., Nature Neuroscience, 2021).
The human brain contains functionally and anatomically distinct networks
for representing semantic information in each sensory modality, and a
separate, distributed amodal conceptual network. In this study we
examined the spatial organization of visual and amodal semantic
functional maps. The pattern of semantic selectivity in these two
distinct networks corresponds along the boundary of visual cortex:
for visual categories represented posterior to the boundary, the
same categories are represented linguistically on the anterior side.
These results suggest that these two networks are smoothly joined
to form one contiguous map.