Understanding Human ability to learn novel concepts from just a few sensory experiences is a fundamental problem in cognitive neuroscience. I will describe a recent work with Ben Sorcher and Surya Ganguli (PNAS, October 2022) in which we propose a simple, biologically plausible, and mathematically tractable neural mechanism for few-shot learning of naturalistic concepts. We posit that the concepts that can be learned from few examples are defined by tightly circumscribed manifolds in the neural firing-rate space of higher-order sensory areas. Discrimination between novel concepts is performed by downstream neurons implementing ‘prototype’ decision rule, in which a test example is classified according to the nearest prototype constructed from the few training examples. We show that prototype few-shot learning achieves high few-shot learning accuracy on natural visual concepts using both macaque inferotemporal cortex representations and deep neural network (DNN) models of these representations. We develop a mathematical theory that links few-shot learning to the geometric properties of the neural concept manifolds and demonstrate its agreement with our numerical simulations across different DNNs as well as different layers. Intriguingly, we observe striking mismatches between the geometry of manifolds in intermediate stages of the primate visual pathway and in trained DNNs. Finally, we show that linguistic descriptors of visual concepts can be used to discriminate images belonging to novel concepts, without any prior visual experience of these concepts (a task known as ‘zero-shot’ learning), indicated a remarkable alignment of manifold representations of concepts in visual and language modalities. I will discuss ongoing effort to extend this work to other high level cognitive tasks.

Restoring hand function is a top priority for individuals with tetraplegia. This challenge motivates considerable research on brain-computer interfaces (BCIs), which bypass damaged neural pathways to control paralyzed or prosthetic limbs. Here, we demonstrate the BCI control of a prosthetic hand using intracortical recordings from the posterior parietal cortex (PPC). As part of an ongoing clinical trial, two participants with cervical spinal cord injury were each implanted with a 96-channel array in the left PPC. Across four sessions each, we recorded neural activity while they attempted to press individual fingers of the contralateral (right) hand. Single neurons modulated selectively for different finger movements. Offline, we accurately classified finger movements from neural firing rates using linear discriminant analysis (LDA) with cross-validation (accuracy = 90%; chance = 17%). Finally, the participants used the neural classifier online to control all five fingers of a BCI hand. Online control accuracy (86%; chance = 17%) exceeded previous state-of-the-art finger BCIs. Furthermore, offline, we could classify both flexion and extension of the right fingers, as well as flexion of all ten fingers. Our results indicate that neural recordings from PPC can be used to control prosthetic fingers, which may help contribute to a hand restoration strategy for people with tetraplegia.

Spike-driven adaptation involves intracellular mechanisms that are initiated by neural firing and lead to the subsequent reduction of spiking rate followed by a recovery back to baseline. We report on long (>0.5 second) recovery times from adaptation in a thalamic-like structure in weakly electric fish. This adaptation process is shown via modeling and experiment to encode in a spatially invariant manner the time intervals between event encounters, e.g. with landmarks as the animal learns the location of food. These cells also come in two varieties, ones that care only about the time since the last encounter, and others that care about the history of encounters. We discuss how the two populations can share in the task of representing sequences of events, supporting path integration and converting from ego-to-allocentric representations. The heterogeneity of the population parameters enables the representation and Bayesian decoding of time sequences of events which may be put to good use in path integration and hilus neuron function in hippocampus. Finally we discuss how all the cells of this gateway to the pallium exhibit mixed selectivity of social features of their environment. The data and computational modeling further reveal that, in contrast to a long-held belief, these gymnotiform fish are endowed with a corollary discharge, albeit only for social signalling.

Neurological disorders share common high-level alterations, such as cognitive deficits, anxiety, and depression. This raises the possibility of fundamental alterations in the way information conveyed by neural firing is maintained and dispatched in the diseased brain. Using experimental epilepsy as a model of neurological disorder we tested the hypothesis of altered information processing, analyzing how neurons in the hippocampus and the entorhinal cortex store and exchange information during slow and theta oscillations. We equate the storage and sharing of information to low level, or primitive, information processing at the algorithmic level, the theoretical intermediate level between structure and function. We find that these low-level processes are organized into substates during brain states marked by theta and slow oscillations. Their internal composition and organization through time are disrupted in epilepsy, losing brain state-specificity, and shifting towards a regime of disorder in a brain region dependent manner. We propose that the alteration of information processing at an algorithmic level may be a mechanism behind the emergent and widespread co-morbidities associated with epilepsy, and perhaps other disorders.

The typical goal of homeostatic mechanisms is to ensure a system operates at or in the vicinity of a stable set point, where a particular measure is relatively constant and stable. Neural firing rate homeostasis is unusual in that a set point of fixed firing rate is at odds with the goal of a neuron to convey information, or produce timed motor responses, which require temporal variations in firing rate. Therefore, for a neuron, a range of firing rates is required for optimal function, which could, for example, be set by a dual system that controls both mean and variance of firing rate. We explore, both via simulations and analysis, how two experimentally measured mechanisms for firing rate homeostasis can cooperate to improve information processing and avoid the pitfall of pulling in different directions when their set points do not appear to match.