Kwabena boahen biography

We analyze and characterize the circuit, and demonstrate that it can be configured to exhibit desired behaviors, including spike-frequency adaptation and two forms of bursting. In this paper, we present a network of silicon interneurons that synchronize in the gamma frequency range Hz. The gamma rhythm strongly influences neuronal spike timing within many brain regions, potentially playing a crucial role in computation.

Yet it has largely been ignored in neuromorphic systems, which use mixed analog and digital circuits to model neurobiology in silicon. Our neurons synchronize by using shunting inhibition conductance based with a synaptic rise time. Synaptic rise time promotes synchrony by delaying the effect of inhibition, providing an opportune period for interneurons to spike together.

Shunting inhibition, through its voltage dependence, inhibits interneurons that spike out of phase more strongly delaying the spike furtherpushing them into phase in the next cycle. We characterize the interneuron, which consists of soma cell body and synapse circuits, fabricated in a 0. Further, we show that synchronized interneurons population of spike with a period that is proportional to the synaptic rise time.

We use these interneurons to entrain model excitatory principal neurons and to implement a form of object binding. We model ion channels in silicon by exploiting similarities between the thermodynamic principles that govern ion channels and those that govern transistors. Using just eight transistors, we replicate--for the first time in silicon--the sigmoidal voltage dependence of activation or inactivation and the bell-shaped voltage-dependence of its time constant.

We derive equations describing the dynamics of our silicon analog and explore its flexibility by varying various parameters. In addition, we validate the design by implementing a channel kwabena boahen biography a single activation variable. The design's compactness allows tens of thousands of copies to be built on a single chip, facilitating the study of biologically realistic models of neural computation at the network level in silicon.

Prosthetic devices may someday be used to treat lesions of the central nervous system. Similar to neural circuits, these prosthetic devices should adapt their properties over time, independent of external control. Here we describe an artificial retina, constructed in silicon using single-transistor synaptic primitives, with two forms of locally controlled adaptation: luminance adaptation and contrast gain control.

Both forms of adaptation rely on local modulation of synaptic strength, thus meeting the criteria of internal control. Our device is the first to reproduce the responses of the four major ganglion cell types that drive visual cortex, producing spiking outputs in total. We demonstrate how the responses of our device's ganglion cells compare to those measured from the mammalian retina.

Replicating the retina's synaptic organization in our chip made it possible to perform these computations using a hundred times less energy than a microprocessor-and to match the mammalian retina in size and weight. With this level of efficiency and autonomy, it is now possible to develop fully implantable intraocular prostheses. Neural circuits consist of many noisy, slow components, with individual neurons subject to ion channel noise, axonal propagation delays, and unreliable and slow synaptic transmission.

This raises a fundamental question: how can reliable computation emerge from such unreliable components? A classic strategy is to simply average over a population of N weakly-coupled neurons to achieve errors that scale as [Formula: see text].

Kwabena boahen biography

However, spike transmission delays preclude such fast inhibition, and computational studies have observed that such delays can cause pathological synchronization that in turn destroys superclassical coding performance. Intriguingly, it has also been observed in simulations that noise can actually improve coding performance, and that there exists some optimal level of noise that minimizes coding error.

However, we lack a quantitative theory that describes this fascinating interplay between delays, noise and neural coding performance in spiking networks. In this work, we elucidate the mechanisms underpinning this beneficial role of noise by deriving analytical expressions for coding error as a function of spike propagation delay and noise levels in predictive coding tight-balance networks of LIF neurons.

Furthermore, we compute the minimal coding error and the associated optimal noise level, finding that they grow as power-laws with the delay. Our analysis reveals quantitatively how optimal levels of noise can rescue neural coding performance in spiking neural networks with delays by preventing the build up of pathological synchrony without overwhelming the overall spiking dynamics.

This analysis can serve as a foundation for the further study of precise computation in the presence of noise and delays in efficient spiking neural circuits. A fundamental question in neuroscience is how neurons perform precise operations despite inherent variability. This question also applies to neuromorphic engineering, where low-power microchips emulate the brain using large populations of diverse silicon neurons.

Biological neurons in the auditory pathway display precise spike timing, critical for sound localization and interpretation of complex waveforms such as speech, even though they are a heterogeneous population. Silicon neurons are also heterogeneous, due to a key design constraint in neuromorphic engineering: smaller transistors offer lower power consumption and more neurons per unit area of silicon, but also more variability between transistors and thus between silicon neurons.

Utilizing this variability in a neuromorphic model of the auditory brain stem with 1, silicon neurons, we found that a low-voltage-activated potassium conductance g KL enables precise spike timing via two mechanisms: statically reducing the resting membrane time constant and dynamically suppressing late synaptic inputs. The relative contribution of these two mechanisms is unknown because blocking g KL in vitro eliminates dynamic adaptation but also lengthens the membrane time constant.

We replaced g KL with a static leak in silico to recover the short membrane time constant and found that silicon neurons could mimic the spike-time precision of their biological counterparts, but only over a narrow range of stimulus intensities and biophysical parameters. The dynamics of g KL were required for precise spike timing robust to stimulus variation across a heterogeneous population of silicon neurons, thus explaining how neural and neuromorphic systems may perform precise operations despite inherent variability.

To produce smooth and coordinated motion, our nervous systems need to generate precisely timed muscle activation patterns that, due to axonal conduction delay, must be generated in a predictive and feedforward manner. Kawato proposed that the cerebellum accomplishes this by acting as an inverse controller that modulates descending motor commands to predictively drive the spinal cord such that the musculoskeletal dynamics are canceled out.

This and other cerebellar theories do not, however, account for the rich biophysical properties expressed by the olivocerebellar complex's various cell types, making these theories difficult to verify experimentally. Here we propose that a multizonal microcomplex's MZMC inferior olivary neurons use their subthreshold oscillations to mirror a musculoskeletal joint's underdamped dynamics, thereby achieving inverse control.

We used control theory to map a joint's inverse model onto an MZMC's biophysics, and we used biophysical modeling to confirm that inferior olivary neurons can express the dynamics required to mirror biomechanical joints. We then combined both techniques to predict how experimentally injecting current into the inferior olive would affect overall motor output performance.

We found that this experimental manipulation unmasked a joint's natural dynamics, as observed by motor output ringing at the joint's natural frequency, with amplitude proportional to the amount of current. These results support the proposal that the cerebellum-in particular an MZMC-is an inverse controller; the results also provide a biophysical implementation for this controller and allow one to make an experimentally testable prediction.

We present a novel log-domain silicon synapse designed for subthreshold analog operation that emulates common synaptic interactions found in biology. Our circuit models the dynamic gating of ion-channel conductances by emulating the processes of neurotransmitter release-reuptake and receptor binding-unbinding in a superposable fashion: Only a single circuit is required to model the entire population of synapses of a given type that a biological neuron receives.

Unlike previous designs, which are strictly excitatory or inhibitory, our silicon synapse implements-for the first time in the log-domain-a programmable reversal potential i. To demonstrate our design's scalability, we fabricated in nm CMOS an array of 64K silicon neurons, each with four independent superposable synapse circuits occupying After verifying that these synapses have the predicted effect on the neurons' spike rate, we explored a recurrent network where the synapses' reversal potentials are set near the neurons' threshold, acting as shunts.

These shunting synapses synchronized neuronal spiking more robustly than nonshunting synapses, confirming that reversal potentials can have important network-level implications. Smooth and coordinated motion requires precisely timed muscle activation patterns, which due to biophysical limitations, must be predictive and executed in a feed-forward manner.

In a previous study, we tested Kawato's original proposition, that the cerebellum implements an inverse controller, by mapping a multizonal microcomplex's MZMC kwabena boahen biography to a joint's inverse transfer function and showing that inferior olivary neuron may use their intrinsic oscillations to mirror a joint's oscillatory dynamics.

Here, to continue to validate our mapping, we propose that climbing fiber input into the deep cerebellar nucleus DCN triggers rebounds, primed by Purkinje cell inhibition, implementing gain on IO's signal to mirror the spinal cord reflex's gain thereby achieving inverse control. We combined this result with our control theory mapping to predict how experimentally injecting kwabena boahen biography into DCN will affect overall motor output performance, and found that injecting current will proportionally scale the output and unmask the joint's natural response as observed by motor output ringing at the joint's natural frequency.

Read Edit View history. Tools Tools. Download as PDF Printable version. In other projects. Wikidata item. Ghanaian professor of engineering born AccraGhana. Education and early life [ edit ]. Career [ edit ]. Research [ edit ]. Honors [ edit ]. References [ edit ]. Modern Ghana. Retrieved 29 December Hynna and K. PP, issue 99, I was a teenager when my father, one of the first black professors at the University of Ghana, gave it to me.

He knew that I was always building something. But I was totally intimidated by this device. So, I went to the library and learned about Boolean logic, and how the whole thing worked. If I wanted something to play with, I built it myself, such as a go-kart.