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Insanally, M. N.

Publications and source records attributed to Insanally, M. N..

3 recordsLinked to original sources

Distinct cortical encoding of acoustic and electrical cochlear stimulation

Cochlear implants are neuroprosthetic devices that restore hearing and speech comprehension to profoundly deaf humans and represent an exemplar application of biomedical engineering and research to clinical conditions. However, the utility of these devices in many subjects is limited, largely due to lack of information about how neural circuits respond to implant stimulation. Recently we showed that deafened rats can use cochlear implants to recognize sounds, and that this training refined the responses of single neurons in the primary auditory cortex. Here we asked how local populations of cortical neurons represent acute implant stimuli, using electrode arrays we developed for cortical surface recordings for micro-electrocorticography ({micro}ECoG), a form of intracranial electroencephalography (iEEG). We found that there was a coarse, non-random spatial organization with limited evidence for consistent, sharply graded cochleotopy across recording sites, relative to a clearer tonotopic spatial representation in normal-hearing rats. Single-trial iEEG responses to acoustic inputs were more reliable than responses to cochlear implant stimulation, although stimulus identity could be successfully decoded in both cases. However, the spatio-temporal response profiles to acoustic vs cochlear implant stimulation were substantially different. Decoders trained on acoustic responses showed essentially zero information transfer when tested on electrical stimulation responses in the same animals after deafening and cochlear implant stimulation. Thus, while acute cochlear implant stimulation evoked spatially non-random cortical activity with coarse cochleotopic structure, the dynamics of network activity were substantially different from those evoked by acoustic stimulation, with possible implications for perceptual similarity that remain to be tested.

neuroscience↗

Contributions and synaptic basis of diverse cortical neuron responses to task performance

Neuronal responses during behavior are diverse, ranging from highly reliable classical responses to irregular or seemingly-random non-classically responsive firing. While a continuum of response properties is frequently observed across neural systems, little is known about the synaptic origins and contributions of diverse response profiles to network function, perception, and behavior. Here we use a task-performing, spiking recurrent neural network model incorporating spike-timing-dependent plasticity that captures heterogeneous responses measured from auditory cortex of behaving rodents. Classically responsive and non-classically responsive model units contributed to task performance via output and recurrent connections, respectively. Excitatory and inhibitory plasticity independently shaped spiking responses and task performance. Local patterns of synaptic inputs predicted spiking response properties of network units as well as the responses of auditory cortical neurons from in vivo whole-cell recordings during behavior. Thus a diversity of neural response profiles emerges from synaptic plasticity rules with distinctly important functions for network performance.

neuroscience↗

Nominally non-responsive frontal and sensory cortical cells encode task-relevant variables via ensemble consensus-building

Neurons recorded in behaving animals often do not discernibly respond to sensory input and are not overtly task-modulated. These nominally non-responsive neurons are difficult to interpret and are typically neglected from analysis, confounding attempts to connect neural activity to perception and behavior. Here we describe a trial-by-trial, spike-timing-based algorithm to reveal the hidden coding capacities of these neurons in auditory and frontal cortex of behaving rats. Responsive and nominally non-responsive cells contained significant information about sensory stimuli and behavioral decisions, and network modeling indicated that nominally non-responsive cells are important for task performance. Sensory input was more accurately represented in frontal cortex than auditory cortex, via ensembles of nominally non-responsive cells coordinating the behavioral meaning of spike timings on correct but not error trials. This unbiased approach allows the contribution of all recorded neurons - particularly those without obvious task-modulation - to be assessed for behavioral relevance on single trials.

neuroscience↗