Responses of recurrent nets of asymmetric ON and OFF cells

A neural field model of ON and OFF cells with all-to-all inhibitory feedback is investigated. External spatiotemporal stimuli drive the ON and OFF cells with, respectively, direct and inverted polarity. The dynamic differences between networks built of ON and OFF cells (“ON/OFF”) and those having only ON cells (“ON/ON”) are described for the general case where ON and OFF cells can have different spontaneous firing rates; this asymmetric case is generic. Neural responses to nonhomogeneous static and time-periodic inputs are analyzed in regimes close to and away from self-oscillation. Static stimuli can cause oscillatory behavior for certain asymmetry levels. Time-periodic stimuli expose dynamical differences between ON/OFF and ON/ON nets. Outside the stimulated region, we show that ON/OFF nets exhibit frequency doubling, while ON/ON nets cannot. On the other hand, ON/ON networks show antiphase responses between stimulated and unstimulated regions, an effect that does not rely on specific receptive field circuitry. An analysis of the resonance properties of both net types reveals that ON/OFF nets exhibit larger response amplitude. Numerical simulations of the neural field models agree with theoretical predictions for localized static and time-periodic forcing. This is also the case for simulations of a network of noisy integrate-and-fire neurons. We finally discuss the application of the model to the electrosensory system and to frequency-doubling effects in retina.     

Conformational selection by the aIF2 GTPase: a molecular dynamics study of functional pathways

Archaeal initiation factor 2 (aIF2) is a GTPase involved in protein biosynthesis. In its GTP-bound, "ON" conformation, it binds an initiator tRNA and carries it to the ribosome. In its GDP-bound, "OFF" conformation, it dissociates from tRNA. To improve our understanding of the role of each conformational state in the aIF2 "life cycle", we start from the state immediately after GTP hydrolysis, ON:GDP:P(i) (where P(i) is inorganic phosphate), and consider the possible next steps on the pathway to the OFF:GDP product. The first possibility is P(i) dissociation, leading to ON:GDP, which could then relax into OFF:GDP. We use molecular dynamics simulations to compute the P(i) dissociation free energy and show that dissociation is highly favorable. The second possibility is conformational relaxation into the OFF state before P(i) dissociation, to form OFF:GDP:P(i). We estimate the corresponding free energy approximately, 2 ± 3.5 kcal/mol, so that this is an uphill or weakly downhill process. A third possibility is relaxation into another conformation, neither ON nor OFF. Indeed, a third, "MIXED" conformation was seen recently in a crystal structure of the aIF2:GDP:P(i) complex. For this conformational state, P(i) dissociation is weakly unfavorable, in contrast to the ON and OFF states. From this, we will deduce that if the MIXED:GDP complex is not too unstable, the ON:GDP:P(i) → MIXED:GDP:P(i) transformation is a downhill process, which can occur spontaneously. This suggests that the MIXED state could be a functional intermediate.     

The claudin superfamily protein nsy-4 biases lateral signaling to generate left-right asymmetry in C. elegans olfactory neurons

Early in C. elegans development, signaling between bilaterally symmetric AWC olfactory neurons causes them to express different odorant receptor genes. AWC left-right asymmetry is stochastic: in each animal, either the left or the right neuron randomly becomes AWC(ON), and the other neuron becomes AWC(OFF). Here we show that the nsy-4 gene coordinates the lateral signaling that diversifies AWC(ON) and AWC(OFF) neurons. nsy-4 mutants generate 2 AWC(OFF) neurons, as expected if communication between the AWC neurons is lost, whereas overexpression of nsy-4 results in 2 AWC(ON) neurons. nsy-4 encodes a transmembrane protein related to the gamma subunits of voltage-activated calcium channels and the claudin superfamily; it interacts genetically with calcium channels and antagonizes a calcium-to-MAP kinase cascade in the neuron that becomes AWC(ON). Genetic mosaic analysis indicates that nsy-4 functions both cell-autonomously and nonautonomously in signaling between AWC neurons, providing evidence for lateral signaling and feedback that coordinate asymmetric receptor choice.     

Neural adaptation facilitates oscillatory responses to static inputs in a recurrent network of ON and OFF cells

We investigate the role of adaptation in a neural field model, composed of ON and OFF cells, with delayed all-to-all recurrent connections. As external spatially profiled inputs drive the network, ON cells receive inputs directly, while OFF cells receive an inverted image of the original signals. Via global and delayed inhibitory connections, these signals can cause the system to enter states of sustained oscillatory activity. We perform a bifurcation analysis of our model to elucidate how neural adaptation influences the ability of the network to exhibit oscillatory activity. We show that slow adaptation encourages input-induced rhythmic states by decreasing the Andronov–Hopf bifurcation threshold. We further determine how the feedback and adaptation together shape the resonant properties of the ON and OFF cell network and how this affects the response to time-periodic input. By introducing an additional frequency in the system, adaptation alters the resonance frequency by shifting the peaks where the response is maximal. We support these results with numerical experiments of the neural field model. Although developed in the context of the circuitry of the electric sense, these results are applicable to any network of spontaneously firing cells with global inhibitory feedback to themselves, in which a fraction of these cells receive external input directly, while the remaining ones receive an inverted version of this input via feedforward di-synaptic inhibition. Thus the results are relevant beyond the many sensory systems where ON and OFF cells are usually identified, and provide the backbone for understanding dynamical network effects of lateral connections and various forms of ON/OFF responses.     

Crossover inhibition in the retina: circuitry that compensates for nonlinear rectifying synaptic transmission

In the mammalian retina, complementary ON and OFF visual streams are formed at the bipolar cell dendrites, then carried to amacrine and ganglion cells via nonlinear excitatory synapses from bipolar cells. Bipolar, amacrine and ganglion cells also receive a nonlinear inhibitory input from amacrine cells. The most common form of such inhibition crosses over from the opposite visual stream: Amacrine cells carry ON inhibition to the OFF cells and carry OFF inhibition to the ON cells (”crossover inhibition”). Although these synapses are predominantly nonlinear, linear signal processing is required for computing many properties of the visual world such as average intensity across a receptive field. Linear signaling is also necessary for maintaining the distinction between brightness and contrast. It has long been known that a subset of retinal outputs provide exactly this sort of linear representation of the world; we show here that rectifying (nonlinear) synaptic currents, when combined thorough crossover inhibition can generate this linear signaling. Using simple mathematical models we show that for a large set of cases, repeated rounds of synaptic rectification without crossover inhibition can destroy information carried by those synapses. A similar circuit motif is employed in the electronics industry to compensate for transistor nonlinearities in analog circuits.     

The development of intrinsic excitability in mouse retinal ganglion cells

Activity-dependent refinement of synaptic connections occurs throughout the developing nervous system, including the visual system. Retinal ganglion cells (RGCs) overproduce synapses then refine them in an activity-dependent manner that segregates RGC connections into multicellular patterns, such as eye-specific regions and retinotopic maps. Ferrets additionally segregate ON and OFF retinogeniculate pathways in an activity-dependent manner. It was unknown whether differences in ON versus OFF intrinsic and spontaneous activity occur in postnatal mouse. The work reported here measured the intrinsic properties and spontaneous activity of morphologically identified postnatal mouse RGCs, and tested the hypothesis that mouse ON and OFF RGCs develop differences in spontaneous activity. We found developmental changes in resting potential, action potential threshold, depolarization to threshold, action potential width, action potential patterns, and maximal firing rates. These results are consistent with the maturation of the intrinsic properties of RGCs extending through the first three postnatal weeks. However, there were no differences among mouse ON, OFF, and multistratified RGCs in intrinsic excitability, spontaneous synaptic drive or spontaneous action potential patterns. The absence of differences between ON and OFF activity patterns is unlike the differences that arise in ferrets. In contrast to the ferret, the ON and OFF target neurons in the mouse are organized in a random pattern, not layers. This supports the hypothesis that the absence of systematic differences in activity results in the nonlayered distribution of retinogeniculate connections.     

The oscillation-like activity in bullfrog ON–OFF retinal ganglion cell

Oscillatory activity of retinal ganglion cell (RGC) has been observed in various species. It was reported such oscillatory activity is raised within large neural network and involved in retinal information coding. In the present research, we found an oscillation-like activity in ON–OFF RGC of bullfrog retina, and studied the mechanisms underlying the ON and OFF activities respectively. Pharmacological experiments revealed that the oscillation-like activity patterns in both ON and OFF pathways were abolished by GABA receptor antagonists, indicating GABAergic inhibition is essential for generating them. At the meantime, such activities in the ON and OFF pathways showed different responses to several other applied drugs. The oscillation-like pattern in the OFF pathway was abolished by glycine receptor antagonist or gap junction blocker, whereas that in the ON pathway was not affected. Furthermore, the blockade of the ON pathway by metabotropic glutamate receptor agonist led to suppression of the oscillation-like pattern in the OFF pathway. These results suggest that the ON pathway has modulatory effect on the oscillation-like activity in the OFF pathway. Therefore, the mechanisms underlying the oscillation-like activities in the ON and OFF pathways are different: the oscillation-like activity in the ON pathway is likely caused by GABAergic amacrine cell network, while that in the OFF pathway needs the contributions of GABAergic and glycinergic amacrine cell network, as well as gap junction connections.     

On problem solving with Hopfield neural networks

Hopfield and Tank have shown that neural networks can be used to solve certain computationally hard problems, in particular they studied the Traveling Salesman Problem (TSP). Based on network simulation results they conclude that analog VLSI neural nets can be promising in solving these problems. Recently, Wilson and Pawley presented the results of their simulations which contradict the original results and cast doubts on the usefulness of neural nets. In this paper we give the results of our simulations that clarify some of the discrepancies. We also investigate the scaling of TSP solutions found by neural nets as the size of the problem increases. Further, we consider the neural net solution of the Clustering Problem, also a computationally hard problem, and discuss the types of problems that appear to be well suited for a neural net approach.     

The effect of morphology upon electrophysiological responses of retinal ganglion cells: simulation results

Retinal ganglion cells (RGCs) display differences in their morphology and intrinsic electrophysiology. The goal of this study is to characterize the ionic currents that explain the behavior of ON and OFF RGCs and to explore if all morphological types of RGCs exhibit the phenomena described in electrophysiological data. We extend our previous single compartment cell models of ON and OFF RGCs to more biophysically realistic multicompartment cell models and investigate the effect of cell morphology on intrinsic electrophysiological properties. The membrane dynamics are described using the Hodgkin - Huxley type formalism. A subset of published patch-clamp data from isolated intact mouse retina is used to constrain the model and another subset is used to validate the model. Two hundred morphologically distinct ON and OFF RGCs are simulated with various densities of ionic currents in different morphological neuron compartments. Our model predicts that the differences between ON and OFF cells are explained by the presence of the low voltage activated calcium current in OFF cells and absence of such in ON cells. Our study shows through simulation that particular morphological types of RGCs are capable of exhibiting the full range of phenomena described in recent experiments. Comparisons of outputs from different cells indicate that the RGC morphologies that best describe recent experimental results are ones that have a larger ratio of soma to total surface area.     

Cellular basis for the response to second-order motion cues in Y retinal ganglion cells.

We perceive motion when presented with spatiotemporal changes in contrast (second-order cue). This requires linear signals to be rectified and then summed in temporal order to compute direction. Although both operations have been attributed to cortex, rectification might occur in retina, prior to the ganglion cell. Here we show that the Y ganglion cell does indeed respond to spatiotemporal contrast modulations of a second-order motion stimulus. Responses in an OFF ganglion cell are caused by an EPSP/IPSP sequence evoked from within the dendritic field; in ON cells inhibition is indirect. Inhibitory effects, which are blocked by tetrodotoxin, clamp the response near resting potential thus preventing saturation. Apparently the computation for second-order motion can be initiated by Y cells and completed by cortical cells that sum outputs of multiple Y cells in a directionally selective manner.     

Connectomic constraints on computation in feedforward networks of spiking neurons

Several efforts are currently underway to decipher the connectome or parts thereof in a variety of organisms. Ascertaining the detailed physiological properties of all the neurons in these connectomes, however, is out of the scope of such projects. It is therefore unclear to what extent knowledge of the connectome alone will advance a mechanistic understanding of computation occurring in these neural circuits, especially when the high-level function of the said circuit is unknown. We consider, here, the question of how the wiring diagram of neurons imposes constraints on what neural circuits can compute, when we cannot assume detailed information on the physiological response properties of the neurons. We call such constraints—that arise by virtue of the connectome—connectomic constraints on computation. For feedforward networks equipped with neurons that obey a deterministic spiking neuron model which satisfies a small number of properties, we ask if just by knowing the architecture of a network, we can rule out computations that it could be doing, no matter what response properties each of its neurons may have. We show results of this form, for certain classes of network architectures. On the other hand, we also prove that with the limited set of properties assumed for our model neurons, there are fundamental limits to the constraints imposed by network structure. Thus, our theory suggests that while connectomic constraints might restrict the computational ability of certain classes of network architectures, we may require more elaborate information on the properties of neurons in the network, before we can discern such results for other classes of networks.     

Modelling intrinsic electrophysiological properties of ON and OFF retinal ganglion cells

ON and OFF retinal ganglion cells (RGCs) display differences in their intrinsic electrophysiology: OFF cells maintain spontaneous activity in the absence of any input, exhibit subthreshold membrane potential oscillations, rebound excitation and burst firing; ON cells require excitatory input to drive their activity and display none of the aforementioned phenomena. The goal of this study was to identify and characterize ionic currents that explain these intrinsic electrophysiological differences between ON and OFF RGCs. A mathematical model of the electrophysiological properties of ON and OFF RGCs was constructed and validated using published patch-clamp data from isolated intact mouse retina. The model incorporates three ionic currents hypothesized to play a role in generating behaviors that are different between ON and OFF RGCs. These currents are persistent Na⁺, I _NaP, hyperpolarization-activated, I _h, and low voltage activated Ca^2 +, I _T, currents. Using computer simulations of Hodgkin-Huxley type neuron with a single compartment model we found two distinct sets of I _NaP, I _h, I _T conductances that correspond to ON and OFF RGCs populations. Simulations indicated that special properties of I _T explain the differences in intrinsic electrophysiology between ON and OFF RGCs examined here. The modelling shows that the maximum conductance of I _T is higher in OFF than in ON cells, in agreement with recent experimental data.     

A neural net model for multiple memory domains

Previous studies with neural nets constructed of discrete populations of formal neurons have assumed that all neurons have the same probability of connection with any other neuron in the net. However, in this new study we incorporate the behavior of the neural systems in which the neural connections can be set up by means of chemical markers carried by the individual cells. With this new approach we studied the dynamics of isolated neural nets again as well as the dynamics of neural nets with sustained inputs. Results obtained with this approach show simple and multiple hysteresis phenomena. Such hysteresis loops may be considered to represent the basis for short-term memory.     

Pharmacology of the skate electroretinogram indicates independent ON and OFF bipolar cell pathways

Organization of afferent information into parallel ON and OFF pathways is a critical feature of the vertebrate visual system. All afferent visual information in the vertebrate retina reaches the inner plexiform layer (IPL) via bipolar cells. It is at the bipolar cell level that separation of ON and OFF information first appears for afferent information from cones. This may also hold true for the rod pathway of cold-blooded vertebrates, but not for mammals. The all-rod retina of the skate presents an opportunity to examine such pathways in a retina having but a single class of photoreceptor. Immunocytochemical evidence suggests that both ON and OFF bipolar cells are present in the skate retina. We examined the pharmacology of the skate electroretinogram (ERG) to test the hypothesis that independent ON and OFF bipolar cell pathways are functional as rod afferent pathways from outer to inner plexiform layer in the skate. 100 microM 2-amino-4-phosphonobutyric acid (APB) reversibly blocked the skate ERG b-wave. A small d-wave-like OFF component of the ERG revealed by DC recording of response to a prolonged (10 s) flash of light was reduced or blocked by 5 mM kynurenic acid (KYN). We found that addition of 200 microM picrotoxin to the Ringer''s solution revealed prominent ON and OFF components of the skate ERG while reducing the c-wave. These ON and OFF components were reversibly blocked by 100 microM APB and 5 mM KYN, respectively. Reversible block of the OFF component by KYN was also accomplished in the presence of 500 microM N-methyl-DL-aspartate. From these findings, we conclude that ON and OFF bipolar cells are likely to be functional as parallel afferent interplexiform pathways in the all-rod retina of the skate.     

Response dynamics of bullfrog ON-OFF RGCs to different stimulus durations

Stimulus duration is an important feature of visual stimulation. In the present study, response properties of bullfrog ON-OFF retinal ganglion cells (RGCs) in exposure to different visual stimulus durations were studied. By using a multi-electrode recording system, spike discharges from ON-OFF RGCs were simultaneously recorded, and the cells’ ON and OFF responses were analyzed. It was found that the ON response characteristics, including response latency, spike count, as well as correlated activity and relative latency between pair-wise cells, were modulated by different light OFF intervals, while the OFF response characteristics were modulated by different light ON durations. Stimulus information carried by the ON and OFF responses was then analyzed, and it was found that information about different light ON durations was more carried by transient OFF response, whereas information about different light OFF intervals were more carried by transient ON response. Meanwhile, more than 80 % information about stimulus durations was carried by firing rate. These results suggest that ON-OFF RGCs are sensitive to different stimulus durations, and they can efficiently encode the information about visual stimulus duration by firing rate.     

Neuron selection by relative importance for neural decoding of dexterous finger prosthesis control application

Future generations of upper limb prosthesis will have dexterous hand with individual fingers and will be controlled directly by neural signals. Neurons from the primary motor (M1) cortex code for finger movements and provide the source for neural control of dexterous prosthesis. Each neuron's activation can be quantified by the change in firing rate before and after finger movement, and the quantified value is then represented by the neural activity over each trial for the intended movement. Since this neural activity varies with the intended movement, we define the relative importance of each neuron independent of specific intended movements. The relative importance of each neuron is determined by the inter-movement variance of the neural activities for respective intended movements. Neurons are ranked by the relative importance and then a subpopulation of rank-ordered neurons is selected for the neural decoding. The use of the proposed neuron selection method in individual finger movements improved decoding accuracy by 21.5% in the case of decoding with only 5 neurons and by 9.2% in the case of decoding with only 10 neurons. With only 15 highly ranked neurons, a decoding accuracy of 99.5% was achieved. The performance improvement is still maintained when combined movements of two fingers were included though the decoding accuracy fell to 95.7%. Since the proposed neuron selection method can achieve the targeting accuracy of decoding algorithms with less number of input neurons, it can be significant for developing brain–machine interfaces for direct neural control of hand prostheses.     

Cell-type specific dendritic contacts between retinal ganglion cells during development.

The extent of a neuron's dendritic field defines the region within which information is processed. The dendritic fields of functionally distinct ON and OFF center retinal ganglion cells (RGCs) form separate mosaics across the retina. Within each mosaic, neighboring dendritic fields overlap by a constant amount, sampling the visual field with the appropriate coverage. Contact-mediated lateral inhibition between neighboring RGCs has long been thought to regulate both the extent and overlap of dendritic fields during development. Here we show that dendro-dendritic contact exists between developing RGCs and occurs in a manner that would regulate the formation of ON and OFF mosaics separately. Dye-filled neighboring ON and OFF ferret alpha RGCs were reconstructed using multiphoton microscopy. At all neonatal ages examined, we observed dendro-dendritic contacts between RGCs of the same sign (ON/ON; OFF/OFF), but never between cells of opposite signs (ON/OFF). Terminal dendrites of one cell often touched a dendrite of its neighbor as they intersected. In some instances, the distal dendrite of one cell formed a fascicle with the proximal process of its neighbor. Alpha cells did not form contacts with neighboring beta cells of the same sign. Together, these observations suggest that dendro-dendritic contact between RGCs is cell-type specific. Dendritic contacts were observed even before the alpha cell arbors were completely stratified, suggesting that cell-cell recognition may take place early in their development. For each cell type, the relative overlap of dendritic fields was constant with age, despite a two-fold increase in field area. We suggest that dendro-dendritic contacts may be sites of intercellular signaling that could regulate local extension of dendrites to maintain the relative overlap of RGCs within a mosaic during development.     

Current developments and future directions of bio-inspired computation and implications for ecoinformatics

Evolutionary and neural computation has been used widely in solving various problems in biological ecosystems. This paper reviews some of the recent work in evolutionary computation and neural network ensembles that could be explored further in the context of ecoinformatics. Although these bio-inspired techniques were not developed specifically for ecoinformatics, their successes in solving complex problems in other fields demonstrate how these techniques could be adapted and used for tackling difficult problems in ecoinformatics. Firstly, we will review our work in modelling and model calibration, which is an important topic in ecoinformatics. Secondly one example will be given to illustrate how coevolutionary algorithms could be used in problem-solving. Thirdly, we will describe our work on neural network ensembles, which can be used for various classification and prediction problems in ecoinformatics. Finally, we will discuss ecosystem-inspired computational models and algorithms that could be explored as directions of future research.