Neurons and Objects: The Case of Auditory Cortex

Sounds are encoded into electrical activity in the inner ear, where they are represented (roughly) as patterns of energy in narrow frequency bands. However, sounds are perceived in terms of their high-order properties. It is generally believed that this transformation is performed along the auditory hierarchy, with low-level physical cues computed at early stages of the auditory system and high-level abstract qualities at high-order cortical areas. The functional position of primary auditory cortex (A1) in this scheme is unclear – is it ‘early’, encoding physical cues, or is it ‘late’, already encoding abstract qualities? Here we argue that neurons in cat A1 show sensitivity to high-level features of sounds. In particular, these neurons may already show sensitivity to ‘auditory objects’. The evidence for this claim comes from studies in which individual sounds are presented singly and in mixtures. Many neurons in cat A1 respond to mixtures in the same way they respond to one of the individual components of the mixture, and in many cases neurons may respond to a low-level component of the mixture rather than to the acoustically dominant one, even though the same neurons respond to the acoustically-dominant component when presented alone

From Face Perception to Individual Recognition: The Missing Link

Recognizing other individuals is a key social aspect of our everyday lives. To recognize a familiar individual, we must establish a link between sensory inputs and a representation of that individual held in memory. In primates, faces play a particularly important role on the sensory side of this process, which is reflected in an extensive network of face-selective areas along the inferior temporal lobe. However, where and how memory is re-activated during face perception remains unclear. Using functional magnetic resonance imaging (fMRI), we measured whole brain activity in macaques while they were watching pictures of other monkey faces that were either long-term acquaintances, visually familiar, or totally unfamiliar. In comparison to unfamiliar faces, the entire face-processing network showed increased activity in response to familiar faces of long-time personal acquaintances. In contrast, faces that were only visually familiar elicited less activity than totally unfamiliar faces in most face-selective areas. The face-processing network thus distinguished personally familiar faces from visually familiar faces. Personally familiar faces also prompted the activation of two previously unknown face-selective areas in the temporal lobe. One area was located in the perirhinal cortex (PR), which has been associated with declarative memory, and the other area was embedded in the temporal pole (TP), a region previously associated with social knowledge. These two novel face areas showed a non-linear response as blurred faces became gradually visible, rapidly becoming active when the faces of personal acquaintances became recognizable. Thus, mimicking the perception of a face approaching us, this paradigm revealed a neural correlate of the ‘aha!’ recognition moment in face areas TP and PR. As a first step towards advancing our understanding of the neuronal processing of individual recognition, our fMRI experiments identified two novel face areas specifically involved in recognizing familiar faces. However, the hemodynamic response cannot directly assess neurophysiological properties. Using fMRI-guided electrophysiology, we investigated the responses of neurons within the novel face area TP in awake monkeys, and we provided the first systematic evidence of cells selective for familiar faces. A high fraction of neurons in face area TP were selective for familiar monkey faces, and unfamiliar faces that were physically similar failed to elicit the same neural responses. Importantly, neurons in face area AM, which is thought to compute facial identity at the top of the face perception hierarchy, were not modulated by familiarity. Within TP, neurons also responded to monkey bodies, and to monkey vocalizations. Maximum activity was elicited by the joint observation of faces and bodies, and audiovisual interactions were evident in some TP neurons. Together, these results reveal neuronal processes underlying memory re-activation during face perception and generate hypotheses for testing how individual recognition is achieved through different modalities, thus advancing our understanding into how unique representations of familiar individuals are developed at the neural level

Neural Circuit Dynamics and Ensemble Coding in the Locust and Fruit Fly Olfactory System

Raw sensory information is usually processed and reformatted by an organism’s brain to carry out tasks like identification, discrimination, tracking and storage. The work presented in this dissertation focuses on the processing strategies of neural circuits in the early olfactory system in two insects, the locust and the fruit fly. Projection neurons (PNs) in the antennal lobe (AL) respond to an odor presented to the locust’s antennae by firing in slow information-carrying temporal patterns, consistent across trials. Their downstream targets, the Kenyon cells (KCs) of the mushroom body (MB), receive input from large ensembles of transiently synchronous PNs at a time. The information arrives in slices of time corresponding to cycles of oscillatory activity originating in the AL. In the first part of the thesis, ensemble-level analysis techniques are used to understand how the AL-MB system deals with the problem of identifying odors across different concentrations. Individual PN odor responses can vary dramatically with concentration, but invariant patterns in PN ensemble responses are shown to allow odor identity to be extracted across a wide range of intensities by the KCs. Second, the sensitivity of the early olfactory system to stimulus history is examined. The PN ensemble and the KCs are found capable of tracking an odor in most conditions where it is pulsed or overlapping with another, but they occasionally fail (are masked) or reach intermediate states distinct from those seen for the odors presented alone or in a static mixture. The last part of the thesis focuses on the development of new recording techniques in the fruit fly, an organism with well-studied genetics and behavior. Genetically expressed fluorescent sensors of calcium offer the best available option to study ensemble activity in the fly. Here, simultaneous electrophysiology and two-photon imaging are used to estimate the correlation between G-CaMP, a popular genetically expressible calcium sensor, and electrical activity in PNs. The sensor is found to have poor temporal resolution and to miss significant spiking activity. More generally, this combination of electrophysiology and imaging enables explorations of functional connectivity and calibrated imaging of ensemble activity in the fruit fly.</p

Multi-electrode array recording and data analysis methods for molluscan central nervous systems

In this work the use of the central nervous system (CNS) of the aquatic snail Lymnaea stagnalis on planar multi-electrode arrays (MEAs) was developed and analysis methods for the data generated were created. A variety of different combinations of configurations of tissue from the Lymnaea CNS were explored to determine the signal characteristics that could be recorded by sixty channel MEAs. In particular, the suitability of the semi-intact system consisting of the lips, oesophagus, CNS, and associated nerve connectives was developed for use on the planar MEA. The recording target area of the dorsal surface of the buccal ganglia was selected as being the most promising for study and recordings of its component cells during fictive feeding behaviour stimulated by sucrose were made. The data produced by this type of experimentation is very high volume and so its analysis required the development of a custom set of software tools. The goal of this tool set is to find the signal from individual neurons in the data streams of the electrodes of a planar MEA, to estimate their position, and then to predict their causal connectivity. To produce such an analysis techniques for noise filtration, neural spike detection, and group detection of bursts of spikes were created to pre-process electrode data streams. The Kohonen self-organising map (SOM) algorithm was adapted for the purpose of separating detected spikes into data streams representing the spike output of individual cells found in the target system. A significant addition to SOM algorithm was developed by the concurrent use of triangulation methods based on current source density analysis to predict the position of individual cells based on their spike output on more than one electrode. The likely functional connectivity of individual neurons identified by the SOM technique were analysed through the use of a statistical causality method known as Granger causality/causal connectivity. This technique was used to produce a map of the likely connectivity between neural sources

A Neural Signal Processor for Low-Latency Spike Inference

This thesis describes the development of a system that can assign identities to a population of single-units, in multi-electrode recordings, at single-spike resolution with low-latency. The system has two parts. The first is a Field-Programmable Gate Array (FPGA)-based Neural Signal Processor (NSP) that receives raw input and generates labelled spikes as output, a process referred to as real-time spike inference. The second is a piece of software (Spiketag) that runs on a PC, communicates with the NSP, and generates a spike-sorted model to guide the real-time spike inference. The NSP provides clocks and control signals to five 32-channel INTAN RHD2132 chips to manage the acquisition of 160 channels of raw neural data. In parallel, the NSP further filters, detects and extracts extracellular spike waveforms from the raw neural data recorded by tetrodes or silicon probes and assigns single-unit identity to each detected spike. A set of Python application programming interfaces (APIs) was developed in Spiketag to enable the communication between the NSP and the PC. These APIs allow the NSP to obtain a model from the PC, which holds parameters such as reference channels, spike detection thresholds, spike feature transformation matrix and vector quantized clusters generated by spike sorting a short recording session. Using the spike-sorted model, the NSP performs data acquisition and real-time spike inference simultaneously. Algorithmic modules were implemented in the FPGA and pipelined to compute during 40 ms acquisition intervals. At the output end of the FPGA NSP, the real-time assigned single-unit identity (spike-id) is packaged with the timestamp, the electrode group, and the spike features as a spike-id packet. Spike-id packets are asynchronously transmitted through a low-latency Peripheral Component Interconnect Express (PCIe) interface to the PC, producing the real-time spike trains. The real-time spike trains can be used for further processing, such as real-time decoding. Several types of ground-truth data, including intracellular/extracellular paired recordings, synthesized tetrode extracellular waveforms with ground-truth spike timing and high-channel-count silicon probe recordings with ground-truth animal positions during navigation were used to validate the low-latency (1 ms) and high-accuracy (as high as state-of-the-art offline sorting and decoding algorithms) of the NSP’s real-time spike inference and the NSP-based real-time population decoding performance