Biology subjects

Hannagan, T.

Publications and source records attributed to Hannagan, T..

4 recordsLinked to original sources

bioRxiv2024

Recurrent neural networks as neuro-computational models of human speech recognition

Human speech recognition transforms a continuous acoustic signal into categorical linguistic units, by aggregating information that is distributed in time. It has been suggested that this kind of information processing may be understood through the computations of a Recurrent Neural Network (RNN) that receives input frame by frame, linearly in time, but builds an incremental representation of this input through a continually evolving internal state. While RNNs can simulate several key behavioral observations about human speech and language processing, it is unknown whether RNNs also develop computational dynamics that resemble human neural speech processing. Here we show that the internal dynamics of long short-term memory (LSTM) RNNs, trained to recognize speech from auditory spectrograms, predict human neural population responses to the same stimuli, beyond predictions from auditory features. Variations in the RNN architecture motivated by cognitive principles further improve this predictive power. Moreover, different components of hierarchical RNNs predict separable components of brain responses to speech in an anatomically structured manner, suggesting that RNNs reproduce a hierarchy of speech recognition in the brain. Our results suggest that RNNs provide plausible computational models of the cortical processes supporting human speech recognition.

neuroscience↗

bioRxiv2021

Reset Networks: Emergent Topography in Networks of Convolutional Neural Networks

We introduce the Reset model, a composition of neural networks - typically several levels of convolutional neural networks - whose outputs at one level are gathered and reshaped into a spatial input for the next level. We show that units in Reset networks self-organize into clusters when trained on MNIST, Fashion MNIST, CIFAR-10 and CIFAR-100. We then show that a stronger type of self-organization, reminiscent of the topography found for numbers in parietal cortex, arises when number images are mapped onto developmentally realistic number codes. We outline the implications of this model for theories of the cortex and developmental neuroscience.

neuroscience↗

bioRxiv2021

Simulating the emergence of the Visual Word Form Area: Recycling a convolutional neural network for reading

The visual word form area (VWFA) is a region of human inferotemporal cortex that emerges at a fixed location in occipitotemporal cortex during reading acquisition, and systematically responds to written words in literate individuals. According to the neuronal recycling hypothesis, this region arises through the repurposing, for letter recognition, of a subpart of the ventral visual pathway initially involved in face and object recognition. Furthermore, according to the biased connectivity hypothesis, its universal localization is due to pre-existing connections from this subregion to areas involved in spoken language processing. Here, we evaluate those hypotheses in an explicit computational model. We trained a deep convolutional neural network of the ventral visual pathway, first to categorize pictures, and then to recognize written words invariantly for case, font and size. We show that the model can account for many properties of the VWFA, particularly when a subset of units possesses a biased connectivity to word output units. The network develops a sparse, invariant representation of written words, based on a restricted set of reading-selective units. Their activation mimics several properties of the VWFA, and their lesioning causes a reading-specific deficit. Our simulation fleshes out the neuronal recycling hypothesis, and make several testable predictions concerning the neural code for written words.

neuroscience↗

bioRxiv2017

A random-matrix theory of the number sense

Number sense, a spontaneous ability to process approximate numbers, has been documented in human adults, infants and newborns, and many other animals. Species as distant as monkeys and crows exhibit number-selective neuronal activity. How number sense can emerge in the absence of learning or fine tuning is currently unknown. We introduce a random matrix theory of self-organised neural states where numbers are coded by vectors of activation across multiple units, and where the vector codes for successive integers are obtained through multiplication by a fixed but random matrix. This cortical implementation of the \"von Mises\" algorithm explains many otherwise disconnected observations ranging from neural tuning curves in monkeys to looking times in neonates and cortical numerotopy in adults. The theory clarifies the origin of Weber-Fechners law and yields a novel and empirically validated prediction of multi-peak number neurons. Random matrices constitute a novel mechanism for the emergence of brain states coding for quantity.

neuroscience↗