“…the binding problem may be an artifact of theoretical assumptions rather than a real computational challenge for the brain…we propose a framework where the visual cortex represents naturally co-occurring patterns of information rather than processing isolated features that need binding.”
Risk reshapes amygdala representation of choice
"...these findings reveal how dynamic reshaping of choice-related basolateral amygdala representations underpins behavioral flexibility in the face of risk."
www.cell.com/neuron/fullt...
Check out our new profile! Dr. Anila D'Mello (@aniladmello.bsky.social) studies language and cognition, and how the underlying brain circuits are altered in neurodevelopmental disorders. Follow the link below to learn more!
#WomenInNeuroscience #StoriesOfWiN
www.storiesofwin.org/profiles/202...
Someone just told me that The Princess Bride is "overrated" and long story short it's perfectly acceptable to remove toxic people from your life.
We're excited to share our new study on decoding brain activity in participants with post-stroke aphasia! We think this is an important step towards cognitive brain-computer interfaces for patients with language disorders
www.biorxiv.org/content/10.6...
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A new study led by Dr. Melissa Franch in the lab of Dr. Ben Hayden at BCM Neurosurgery seeks to understand the neural activities that create communication differences for individuals with Autism Spectrum disorder (ASD).
“Communication differences in autism may reflect a reduced use of earlier words in a sentence when interpreting language. By studying single neurons in autistic adults while they listened to speech, we found that basic word meanings are preserved, but the way the brain combines information from surrounding words is different. Neural activity showed broader, less precise use of prior words, simpler patterns of organization, and weaker signals related to predicting upcoming words. These findings suggest that communication differences in autism arise not from understanding words themselves, but from how the brain uses surrounding words to shape meaning.” -Dr. Melissa Franch, Postdoctoral Fellow
Social and communicative deficits are defining characteristics of #Autism Spectrum disorder (ASD).
A new study led by @mfranch.bsky.social in the lab of Dr. Ben Hayden at #BCMNeurosurgery seeks to understand the neural activities that create certain communication differences.
#AutismAwareness
By studying brain cells in autistic adults during listening, Dr. Franch from the Hayden and Sheth Labs found that the brain relies on word-meaning signals but less on prior words for understanding, supporting predictive coding theories of #autism #neuroskyence
tinyurl.com/SULLM @mfranch.bsky.social
NASA just dropped this image of Artemis II astronaut Christina Koch looking back at us. The first woman to ever see our planet in its entirety. I’m not crying you’re crying 🥹🔭🧪 📸: NASA
New preprint: our lab’s first Alzheimer’s paper! “Loss of neuronal population organization links pathology to behavior in a model of Alzheimer's disease”
www.biorxiv.org/content/10.6... 🧪🧵1/
Cortical circuits are often thought to be specialized, but the same large-scale activity patterns can arise from different circuit architectures. In other words, different instruments can play the same tune, and the same instrument can sometimes play different tunes.
www.cell.com/neuron/fullt...
Thank you, Nicole! ✨
Thank you to @sameershethmd.bsky.social @nicoleprovenza.bsky.social @srheilbronner.bsky.social @jhennig.bsky.social @bcmneurosurgery.bsky.social @elebartoli.bsky.social and the entire Hayden lab and neurosurgery team for their support in my training and this work!
I have a personal connection, as my brother Stefan has profound autism and is nonverbal. LLMs generate fluent language, but many people like him still lack reliable ways to communicate. I hope these findings one day improve neural interventions for communication in ASD.
Autism is a form of neurodiversity that should be accepted and embraced. However it is a spectrum, and there are some individuals with profound autism. These are people with high support needs, limited communication and independence, who usually require 24hr care.
In summary, this study provides a rare circuit-level account of how semantic processing is altered in autism by combining single-neuron recordings with computational frameworks drawn from predictive coding and LLMs.
This bias towards lower neural dimensionality in autism may help explain the observed changes in semantic contextualization, as it could limit how effectively the brain can combine information from earlier words, resulting in weaker predictions and less precise weighting of context.
Why care about dimensionality? The dimensionality of the neural subspace determines the brain's capacity to represent semantic information. We think autism may be associated with a bias towards low dimensional neural subspaces for semantic coding.
Said another way, in autistic patients, neural population activity is dominated by a smaller number of shared latent signals, causing neurons to vary more together rather than independently. Reduced dimensionality was also associated with reduced semantic decoding in 2/3 ASD patients.
Consistent with this, all autistic patients exhibited lower effective dimensionality in the neural subspace predictive of semantics, indicating fewer independent coding axes.
While single neurons in all patients showed prediction error to the most surprising words, autistic patients showed reduced population-level decoding of word surprisal, which may arise from their altered weighting of contextual information.
An altered tracking of past words should affect expectations of future words. We extracted word surprisal from our stories and found some of the most surprising words to be ‘bungee’, ‘sizzling’’, and ‘typewriter’, whereas expected words included ‘birthday’, ‘call’, and ‘team’.
Neural weighting of prior context was systematically altered in autistic patients, reflected in a broader distribution of weights. This broader distribution of prior word weighting in autism resembles the proposed weaker, broad distribution of priors in Bayesian theories for ASD.
For example, the meaning of ‘bat’ changes depending on whether ‘swinging’ or ‘flying’ precedes it. We computed how neural responses to a word were weighted by embeddings of preceding words that provide contextual information.
We used attention-derived metrics from LLMs to quantify how contextual information from prior words contributes to the representation of the current word, both in LLM embeddings and in neural population activity.
Both control and ASD patients had comparable single neuron encoding of semantics, representations of semantic relationships between words, and neural flexibility in semantic representations as word meanings change with context.
We found that autistic patients exhibited a shared neural profile, characterized by intact core semantic representations but systematic alterations in semantic contextualization relative to controls.
We recorded neural activity from single neurons in the hippocampus while epilepsy patients with and without autism listened to stories, and leveraged the power of LLMS (GPT-2) to quantify semantic coding and contextualization.
Inspired by Pellicano and Burr’s theory that suggests autistic traits may arise from differences in predictive coding, we tested this hypothesis in the domain of language comprehension, as interpreting word meanings requires incorporation of semantic context.
I’m excited to share my newest work with @benhayden.bsky.social, and the work I’m most proud of to date, on characterizing semantic coding in single-neuron hippocampal activity in patients with autism during natural language comprehension!
www.biorxiv.org/content/10.6...
Meet Sarah Heilbronner, a neuroanatomist building wiring diagrams to understand the structure of pathways in the brain. 🧠#AskaNeuroscientist @srheilbronner.bsky.social
